Structure Based Drug Design Tools

Introduction

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The design and production of drugs is a field in which chemistry has had a favorable impact on life expectancy and quality over the past century. (1, 2) As such, this field provides a rare opportunity to introduce several concepts in chemistry and biochemistry to a large audience.

It is widely known that the design and development of a new drug generally costs more than 1 billion dollars in total and takes at least 10 years, (3, 4) while, despite all these efforts, only a very limited number of drug discovery projects will lead to the actual release of a new drug. (5, 6) Several technologies have been developed to rationalize the process by reducing duration, cost, and attrition rate, one of which is computer-aided drug design (CADD). (7-10) CADD uses computing resources, algorithms, and 3D-visualization to help generate rational ideas about how to create or modify molecules, and to make decisions in the execution of the drug design process.

Whereas the general audience is aware of the overall concept and global cost of drug discovery and development, usually little is known about the actual challenges and the role played by CADD. To address this, we present a new freely available web-based educational tool, which introduces the basics of drug design and provides anyone with access to simple computational methodologies to conceive and evaluate molecules for their potential to become actual drugs. (11)

Although macromolecular entities, such as antibodies, can act as therapeutic agents, in this report we will consider that drugs are small organic molecules that activate or inhibit the function of a biomolecule, generally a protein, which in turn results in a therapeutic or prophylactic benefit to the patient.

Nature has been the most important source of medicinal agents for centuries. Many useful drugs were developed from plant products, including for instance morphine from Poppy Papaver for pain management, quinine from Cinchonae tree's bark as an antimalarial drug and muscle relaxant, or paclitaxel (also known as taxol) from the Pacific yew tree Taxus brevifolia for cancer therapy, to name a few. While natural molecules are still a major source of inspiration for drug design, only 6% of the small-molecule drugs developed over the past decades are purely natural products, unmodified in structure. Other compounds comprise natural product derivatives containing synthetic modifications (27%), synthetic molecules inspired by natural products (32%), and brand new structure synthetic compounds (35%). (12) In other words, 94% of the newly released drugs have, at the very least, necessitated chemical modifications either to increase affinity and selectivity for the protein target, to correct absorption distribution metabolism or excretion (ADME) and toxicity problems, or to circumvent an intellectual property (IP) issue. Although serendipity has had an important role in many therapeutic advances, rational design including CADD has become a major factor in producing new treatments. (13) The vast majority of the drugs developed recently have benefited to various extents from computer-aided approaches as introduced below. (7)

Basic Principles of Computer-Aided Drug Design

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CADD technologies can be classified into two main categories: structure-based and ligand-based approaches.

Structure-based approaches make use of the three-dimensional structure of the protein target when it is available, or can be reliably modeled. (14) We generally consider that molecular docking is the cornerstone of structure-based drug design, of which anti-influenza drugs zanamivir (Relenza) and oseltamivir (Tamiflu) are among the most salient successful applications. (15, 16)

The first and most basic objective in structure-based drug design is indeed to predict whether a given small molecule will bind to a chosen protein target and, if so, what will be the strength of this molecular recognition. The first goal can be achieved using a so-called docking program, whose aim is to predict the most probable geometry and position of a small molecule at the surface of a protein by optimizing the interactions between both molecular partners. (8) Many docking programs are freely available and can be used for educational purposes, including web-based tools such as SwissDock.ch, (17) or downloadable programs such as Autodock (18) and Autodock Vina. (19) The concept of molecular docking is very intuitive and can be easily introduced to the general audience. For example, we obtained good results using scaled 3D-printed models of cyclooxygenase (COX) and ibuprofen, a well-known anti-inflammatory drug that binds to COX. The COX model can be opened, exposing the binding site that accommodates ibuprofen. Then, students (even youngsters) are invited to manually perform the docking of ibuprofen into COX (Figure 1). Although this manual positioning neglects the difficulties encountered when accounting for the flexibility of both the protein and the ligand, it shows the challenges of the process, and the necessity to automatize the approach using computer algorithms to possibly treat a large number of molecules to dock.

Figure 1

Figure 1. As a clear example of the molecular docking concept, educators can let participants dock a drug in the binding site of the protein target manually. Both the drug and the protein must be printed in 3D at the same scale. Here, we selected an example related to the first online workshop: an anti-inflammatory drug (ibuprofen in yellow) to be placed inside the cyclooxygenase 1 protein (COX1 in white). The printed protein model can be opened, exposing the ibuprofen binding site. The protein file, retrieved from the protein databank (PDB), (43) can be converted into a 3D-object and saved as STL, VMRL, or X3D files compatible with most 3D-printers, by using a molecular visualization software such as UCSF Chimera. (20)

The second goal, i.e., determining the strength of binding of the small molecule to the protein, can be achieved using a binding free energy estimator. Several computer-aided approaches are available for this purpose. (8) They are generally based on high-level methods involving concepts in physical chemistry and statistical physics. However, this theoretical complexity can be hidden behind the simple notions of fitness or scoring. Docking software usually provides a crude estimation of this binding free energy, which can be presented simply to users as a score (without physical or chemical meanings) to optimize.

Basically, drug design consists of the conception of molecules that are complementary to the protein target in terms of 3D-shape and charge distribution, to optimize molecular recognition and binding. Through prediction of molecular recognition and binding affinity, molecular docking opens the road to in silico design and optimization of virtual compounds.

On the contrary, ligand-based approaches rely on the knowledge implicitly contained in the chemical structure or physical properties of other molecules that bind to the biological target of interest. Typically, this knowledge can be extracted, analyzed, and used to create predictive models using machine-learning technologies, under the name quantitative structure–activity relationships (QSAR) if the objective is to create new ligands and/or predict their activity, or quantitative structure–properties relationships (QSPR) if the objective is to predict molecular properties in relation to lipophilicity, (21) drug likeness, or pharmacokinetics (22) (PK), for example. These molecular properties are fundamental in drug design. Indeed, although a high affinity for the protein target is essential, it is not sufficient for the designed small molecule to become a drug: to obtain a therapeutic effect, the molecule needs to reach its target in the body, and stay there long enough for the expected biological events to occur. Therefore, to support efficiently the design of new drugs, it is important to predict their PK behaviors with computer-aided approaches.

In addition to QSAR and QSPR, another set of ligand-based approaches rely on the commonly accepted assumption that very similar small molecules are more likely to be active on the same target. Such approaches can be used to perform molecular screening, i.e., searching for molecules similar to known active compounds and potentially also active on the same target, or reverse screening, i.e., deducing the potential protein targets of a given molecule by identifying similar existing compounds for which the activity is experimentally known. (23-25) This reverse screening can be of high interest to predict potential secondary targets of small molecules, i.e., proteins to which a small molecule will be able to bind although it was developed to target another macromolecule. These secondary targets can be at the origin of the negative side effects of small molecules, but on the contrary can also open the way to positive drug repurposing, i.e., finding another possible application to an existing drug. (26, 27)

Pedagogical Objectives

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Approaches, methodologies, and technologies involved in computational chemistry, chemoinformatics, bioinformatics, and molecular modeling, and generally in CADD, have a wide scope of application, but their teaching remains limited, even at an advanced academic level. (28) Several remarkable educational protocols have been proposed to achieve the objective of teaching how to properly perform drug discovery tasks with existing computational tools to future professionals in pharmaceutical research. Of note is the well-structured, thorough course by Tsai, (29) which includes different modules, lectures, and practical sessions encompassing many facets of CADD. More recently, Rodrigues et al. provided a comprehensive technical course of drug design. (30) Other educational programs emphasis more on virtual screening (31) or ADME (32) aspects. Moreover, some studies have demonstrated the positive impact of using tridimensional molecular graphics visualization for the perception of complex molecular properties. (33, 34) All of these excellent teaching materials imply multiple methodologies based on a combination of web and standalone software. Whereas this has the merit to make the student face the technical hurdles of the discipline (e.g., incompatibility of file formats, irreproducibility of implementations, instability of multiple computer platforms), we believe this can prevent reaching the pedagogical goals of teaching the global concepts of the drug design process, especially at the high-school level. Not surprisingly, the above-mentioned courses and sessions are merely dedicated to upper-level undergraduate students. A noticeable exception is the e-malaria project, which was used to introduce high-school students to drug design in a real-life context. (35) Unfortunately, this latter endeavor faced licensing and confidentiality issues that required a complex hardware and network infrastructure along with an account login procedure. Together with significant computational time, this lack of flexibility hinders the trial-and-error cyclic process, which we consider key to appreciating the basis of molecular design.

By leveraging our expertise in designing expert CADD services, (17, 24, 36-39) we took advantage of today's opportunities provided by web technologies and open-source resources to develop the fully integrated, flexible educational tools described below for a broader audience, including high-school students, high-school teachers, undergraduate students, and the public at large. (11) This web-based teaching environment reduces technical difficulties to the minimum, allowing several pedagogical objectives to be reached.

First, it is useful to remind or inform the general audience that, in conventional medicine, a drug is a small molecule, most of the time synthesized by organic chemistry, which interacts with a biological macromolecule to generate the therapeutic effect. This concept is the essence of drug design and must be understood at the beginning of the Drug Design Workshop. In contrast, it can also serve as a starting point for a discussion regarding the differences between conventional (also known as allopathic) and homeopathic medicine.

Second, we consider it important to state that the design of new drugs is a collective effort necessitating the close collaboration of several different scientific backgrounds including not only biology, medicine, pharmacy, and biochemistry, but also chemistry, molecular modeling, and bioinformatics.

Third, we would like to introduce the key concept that drug design is a multiobjective optimization process whose aim is to create compounds with not only a high affinity for the target but also optimal pharmacokinetics properties. This requires access to professional scientific web-based tools and necessitates the guidance of an expert in the field or an educator trained in these specific topics.

A fourth objective is to explain that computer-aided drug design activities consist of the usage of several diverse structure-based and ligand-based approaches, to predict and evaluate all fundamental characteristics necessary for a molecule to become a drug: e.g., complementarity and affinity for the target, fate inside the organism, along with possible side effects and toxicity.

Finally, we would like to state that drug design is generally a cyclic optimization process, which gathers knowledge obtained from the first molecules in order to design better compounds in the next rounds and converge toward drug-candidates. From our experience and from teacher feedback, high-school students are very smoothly engaged with and attracted by the challenged of iteratively generating a molecule with the highest score on a given target. Positive emulation and competition in classrooms were frequently observed and reported.

Design of the Workshop and New Educational Tools

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Short Movie Describing the Role of Bioinformatics in Drug Design

First, with the help of professional graphic designers from Studio KO, (40) we produced a short movie to introduce the concepts mentioned in the pedagogical objectives, but also to recall the nature and definition of a protein. The movie also links diseases to possible over- or underexpression of proteins, or to protein mutations leading to malfunction. This allows the introduction of the notion that a drug is generally a small molecule able to bind such proteins and lead to the therapeutic effect. This movie is available online at the main Drug Design Workshop URL. (11)

Online Drug Design Workshop: Selecting Didactic Drug Design Targets

Second, we created a simple and integrated web interface to perform the basic steps of CADD. As for real drug design, this online tool allows for performance of multiple iterative cycles of molecular optimization, taking into account the complementarity of the designed molecule for the target. In a second step, different properties regarding ADME, toxicity, and secondary targets (Figure 2) may be considered.

Figure 2

Figure 2. General principle of the online Drug Design Workshop exemplified in the context of the inhibition of indolamine 2,3-dioxygenase (IDO1) by the optimization of a newly discovered inhibitor (PIM) to obtain a drug candidate (MMG-0358). Several cycles of optimization can be performed, during which the molecules are drawn by the users, automatically docked into the protein, scored for molecular complementarity, and analyzed for some ADMET properties and possible secondary targets. All technical aspects have been simplified and can be performed by one-click or drag-and-drop actions. NLG-919, L1MT, and AMG-1 are other known ligands of IDO1 used as examples in the workshop and defined in the Web site.

For this, we have selected three relevant protein targets for drug design: the cyclooxygenase (two isoforms: COX1 and COX2), B-Raf, and indoleamine 2,3-dioxygenase 1 (IDO1).

COX1/2 are the targets of the very well-known nonsteroidal anti-inflammatory drugs (NSAID) like ibuprofen or diclofenac, which are commonly used to treat inflammation, pain, and fever. COX1/2 can be used to make a link between the concepts introduced during the workshop and a medication that everyone has already used. COX1/2 is also a good model to introduce the notion of selectivity for the target. Indeed, COX1 plays an important role in blood coagulation and in protecting the gastric lining, while COX2 is produced locally in the inflamed tissue, and is directly responsible for the sensation of pain. Recent efforts led to the design of ligands specific for COX2, which once targeted becomes responsible for the therapeutic effect, thus avoiding COX1 which is responsible for the side effects of the classical NSAIDs. Users are invited to design selective ligands by following the example of celecoxib, a selective COX2 inhibitor.

B-Raf is a kinase whose mutants commonly cause cancer by excessive stimulation of cell growth. Inhibitors of the V600E B-Raf mutant, a form often found in melanoma cells, were recently introduced for the treatment of late-stage melanoma. Molecules such as vemurafenib, a specific inhibitor of V600E B-Raf, were among the first drugs to trigger an efficient response against this type of skin cancer. This target protein thus provides an example of a recent success story of drug design in the targeted therapy of cancer. Possibly, it can also be used to open a discussion on personalized medicine. Indeed, in case of melanoma cells not bearing the V600E mutation of B-Raf, vemurafenib was proven to be deleterious as the drug favors tumor growth. (41) Therefore, its prescription can only be made upon sequencing the BRAF gene of the patient's cancer cells to ascertain the presence of this sequence alteration.

IDO1 is an enzyme that catabolizes tryptophan, and is used by cancer cells to shun the immune system. Therefore, inhibitors of IDO1 could be of major interest for cancer immunotherapy, (42) and evaluated for coadministration with other agents that inhibit immune escape of cancer cells (e.g., monoclonal antibodies ipilimumab or nivolumab). IDO1 is thus an elegant opportunity to delineate the relationship between CADD and the latest state-of-the-art discoveries in cancer treatment.

The biological contexts corresponding to the above-mentioned targets are introduced and summarized online in our Web site. (11)

Online Drug Design Workshop: Experiencing the Drug Design Process

For each target, we selected representative well-known approved or experimental drugs, whose binding modes are available in the Protein Databank, (43) or were precalculated using the Autodock Vina docking program. (19)Figure 3 shows the input page of the Drug Design Workshop Web site. Images representing the 3D-structure of target proteins are displayed on the left, and the 2D chemical structures of typical drugs are available in boxes, on the right. To visualize the complex between the protein target and one preselected drug, the user simply needs to drag the drug image and drop it on the protein image. The corresponding complex will be immediately displayed as an interactive 3D-session in the user's web browser thanks to the JSmol molecular visualization applet. (44)

Figure 3

Figure 3. Input page of the Drug Design Workshop Web site.

To design a molecule, the user is invited to click on the "Design your own molecule" box. This will open the Marvin4JS molecular sketcher (45) to draw a new virtual molecule. To simplify the process for users with little experience in organic chemistry, the sketcher can be filled automatically with one of the preselected drugs, by clicking on the corresponding "down" red arrow. Then, the user can modify these molecules within the sketcher. Thanks to this simplified process and the ability of the sketcher to indicate inconsistencies in chemical structures, we have experienced that even users without any knowledge of organic chemistry are capable of drawing relevant molecules.

Once the new molecule has been drawn and the "Done" button clicked, its image will appear on the corresponding box, and it is available for drag-and-drop. If the user requires the visualization of the complex between a target and a designed ligand, the molecule is automatically docked with Autodock Vina, after determination of the most probable microspecies (protonation state and tautomer) at physiological conditions. For the sake of simplicity, these steps are performed without any intervention from the user. Docking calculations can be time-consuming. Therefore, to allow the workshop to be executed on any computer, calculations are not performed on the user's workstation (desktop or laptop), but on one of our multicore machines, managed by a queuing system that allows several docking calculations in parallel. Users are informed about the waiting list and duration of the calculation. In our practice, this setup allows up to 15 users to perform basic operation of drug design at the same time and in good conditions.

Binding modes of the existing or virtual compounds in the protein target are displayed on a dedicated page, which also provides a score that evaluates the strength of the binding (Figure 4). This score is in fact the opposite of the binding free energy estimated by the Autodock Vina docking software. We chose to use this score rather than the actual binding free energy since it is easier for the user to follow the idea that "the larger the score, the better the ligand" rather than the more confusing notion that "the more negative the binding free energy, the better the ligand". Of course, this modification can be explained to more advanced students. For each compound, the calculated score of the designed molecule is compared to those of the preselected drugs, allowing the user to compete with "real drugs" in terms of affinity for the therapeutic target. We have experienced that this score creates a powerful incentive for the users to create better molecules, and thus to enter naturally and seamlessly into the typical iterative optimization cycle, which is one of the fundamental processes of CADD. It is noteworthy that the docking engine used involves a stochastic algorithm, which is necessary for having docking results quickly enough for true interactivity (approximately between 30 s to 3 min, depending on the size of molecule and binding site). Whereas reproducibility cannot be ensured, we have set parameters to maximize convergence. As a result, docking with Drug Design Workshop returned a significantly different binding mode in only 5–10% of the runs (related mainly to molecule flexibility and binding site size). To gain confidence in the predicted binding mode, it is advised to run the same docking several times or in some cases compare the results obtained by each student in the classroom.

Figure 4

Figure 4. Output page of the Drug Design Workshop Web site.

The usage of the Web site is supported by help pages and FAQs providing technical guidance.

Online Drug Design Workshop: Introducing the Multiobjective Character of Drug Design

The above-mentioned cyclic optimization process is limited to the enhancement of the affinity of the ligand for the protein by using a structure-based approach. As we discussed above, one pedagogical objective is also to introduce the multiobjective nature of the optimization process in (computer-assisted) drug design. This implies that, besides affinity, the pharmacokinetic and the pharmacodynamic properties of the small molecule are also to be optimized. To this end, we enable a seamless one-click submission of the user's molecule from the Drug Design Workshop to SwissTargetPrediction (24) or to SwissADME. (22) Both these online tools are in-house research-grade web services developed to predict possible targets, ADME, or toxicity properties of small drug-like molecules. Both rely on ligand-based approaches, allowing an introduction to this type of technology for the most advanced users. SwissTargetPrediction provides a list of the 15 most probable protein targets for the small molecule under consideration, giving an estimate of the selectivity of the molecule and a prediction of potential side effects (Figure 5). SwissADME calculates numerous molecular properties related to, e.g., pharmacokinetics, drug-likeness, physicochemistry, and synthetic accessibility (Figure 6). Of note, SwissADME models include the BOILED-Egg that we developed to predict the propensity of small molecules to be absorbed by the gastrointestinal tract or to access the brain. These predictions are made simply by plotting the small molecule on a 2D graph based on the lipophilicity and polarity of molecules, where the regions containing molecules able to cross specific biological barriers (gastrointestinal wall or blood-brain barrier) are delineated by ellipses (Figure 6). Thanks to simplicity and speed, the BOILED-Egg model is of great support for the users to apprehend the concepts of absorption and distribution, and to figure out what type of chemical modifications must be made to the small molecule to obtain the desired absorption and distribution, in an intuitive and iterative way.

Figure 5

Figure 5. Output page of SwissTargetPrediction, obtained upon one-click query from the Drug Design Workshop Web site. Target names, common names, Uniprot ID, ChEMBL ID, and Target classes are those defined in the ChEMBL database, (46) which was used to build the predictive model of protein targets for small molecules. (47)

Figure 6

Figure 6. Output page of SwissADME, obtained upon one-click query from the Drug Design Workshop Web site. The upper panel shows the BOILED-Egg, a graphical classification model to predict gastrointestinal absorption (HIA, white ellipse) and permeation through the blood–brain barrier (BBB, yolk). (22) The position of the molecule on this panel is shown as a dot, whose color reflects the prediction for the molecule to be the substrate of the multidrug resistance protein "P-glycoprotein 1" (PGP). The lower panel compiles all predicted ADME parameters for the molecule under study. (48)

The results from SwissTargetPredicition and SwissADME, notably the BOILED-Egg model, can be useful for Drug Design Workshop users through the guidance of experts in the field or educators previously trained in the topic. This information can be fruitfully taken into account during the global optimization process of one's own molecule, providing a better overview of the multiobjective character of CADD.

Discussion

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During the last two years, approximately 900 high-school students, 15–19 years old, attended this computer-aided drug design workshop, in about 50 different sessions. We proposed an anonymous online feedback form to the students with the aim of regular improvement. Student feedback was very satisfactory, with an overall appraisal of 5.11/6.00 based on 209 evaluations by pairs of users. Of note, students particularly appreciated the opportunity to use "professional" bioinformatics tools (5.30/6.00) and said they had learned a lot about drug design and CADD during the session (5.23/6.00). In view of these encouraging experiences, we decided to provide training to teachers who could use this material as a support for biology or chemistry classes.

In our experience, thanks to the simplicity of the user-friendly Web site and molecular sketcher, but also the incentive provided by the affinity score, the younger users also appreciate the workshop, even if they have little or no experience in organic chemistry. Due to the versatility of the approach, which provides an opportunity to introduce a large number of different concepts of CADD in relation to the users' backgrounds, we also successfully used this workshop as a rapid and simple hands-on introduction to the cyclic iterative optimization process in drug design for students from the doctoral school of pharmaceutical sciences from the University of Geneva or to bachelor and master students in biochemistry from the University of Fribourg, Switzerland.

Since the workshop requires very little material, i.e., a few standard computers connected to the Internet, it is easy to give it not only directly in schools but also during scientific exhibitions: hundreds of visitors have had the opportunity to successfully experience the early stages of designing drugs during various science fairs. In addition, the workshop has also been implemented into public laboratories and educational platforms, such as the Chimiscope of the University of Geneva (49) and l'Eprouvette of the University of Lausanne, (50) Switzerland.

Although high-school students and teachers are certainly our main target, those who have participated in our workshop, from families to doctoral students, have always shown great interest and enthusiasm whatever their scientific background and level. The subject not only is timely, but also concerns each and every one. Our workshop provides a simplified view of complex notions and allows a wide audience to discover the key stages in drug discovery as well as the importance of bioinformatics in life science today.

Author Information

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• • Antoine Daina - Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

  • Marie-Claude Blatter - Outreach Team, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland; Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel Servet, 1211 Geneva 4, Switzerland; Training Group, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

  • Vivienne Baillie Gerritsen - Outreach Team, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland; Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel Servet, 1211 Geneva 4, Switzerland

  • Patricia M. Palagi - Outreach Team, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland; Training Group, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

  • Diana Marek - Outreach Team, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland; Training Group, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland; Vital-IT, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

  • Ioannis Xenarios - Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel Servet, 1211 Geneva 4, Switzerland; Vital-IT, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

  • Torsten Schwede - Computational Structural Biology, SIB Swiss Institute of Bioinformatics & Biozentrum, Universität Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland

  • Olivier Michielin - Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, Bâtiment Génopode, Quartier Sorge, 1015 Lausanne, Switzerland

• A.D. and M.-C.B. authors contributed equally to this work.

• The authors declare no competing financial interest.

  A short movie introducing the concepts mentioned in the pedagogical objectives, and recalling the nature and definition of a protein, is available in ref 11 under CC-BY-ND-NC license.

Acknowledgment

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This work was supported by the Swiss National Science Foundation through the Agora grant CRAGP3_151515, and by the SIB Swiss Institute of Bioinformatics.

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   Nature Reviews Drug Discovery (2013), 12 (8), 569CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

   There is no expanded citation for this reference.

   https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXht1SjtbvJ&md5=5c5ed93bf3e0badf0468ed364285dc12

6. 6

   Waring, M. J. ; Arrowsmith, J. ; Leach, A. R. ; Leeson, P. D. ; Mandrell, S. ; Owen, R. M. ; Pairaudeau, G. ; Pennie, W. D. ; Pickett, S. D. ; Wang, J. ; Wallace, O. ; Weir, A. An Analysis of the Attrition of Drug Candidates From Four Major Pharmaceutical Companies Nat. Rev. Drug Discovery 2015 , 14 ( 7 ) 475 – 486  DOI: 10.1038/nrd4609

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   An analysis of the attrition of drug candidates from four major pharmaceutical companies

   Waring, Michael J.; Arrowsmith, John; Leach, Andrew R.; Leeson, Paul D.; Mandrell, Sam; Owen, Robert M.; Pairaudeau, Garry; Pennie, William D.; Pickett, Stephen D.; Wang, Jibo; Wallace, Owen; Weir, Alex

   Nature Reviews Drug Discovery (2015), 14 (7), 475-486CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

   The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the no. of efficacy- and safety-related failures by analyzing possible links to the physicochem. properties of small-mol. drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and anal. of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The anal. reaffirms that control of physicochem. properties during compd. optimization is beneficial in identifying compds. of candidate drug quality and indicates for the first time a link between the physicochem. properties of compds. and clin. failure due to safety issues. The results also suggest that further control of physicochem. properties is unlikely to have a significant effect on attrition rates and that addnl. work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.

   https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtFeju7jM&md5=1fdc374d32816b1e91438152299dd1b1

7. 7

   Jorgensen, W. L. Science 2004 , 303 ( 5665 ) 1813 – 1818  DOI: 10.1126/science.1096361

8. 8

   Zoete, V. ; Grosdidier, A. ; Michielin, O. Docking, Virtual High Throughput Screening and in Silico Fragment-Based Drug Design J. Cell. Mol. Med. 2009 , 13 ( 2 ) 238 – 248  DOI: 10.1111/j.1582-4934.2008.00665.x

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   8

   Docking, virtual high throughput screening and in silico fragment-based drug design

   Zoete, Vincent; Grosdidier, Aurelien; Michielin, Olivier

   Journal of Cellular and Molecular Medicine (2009), 13 (2), 238-248CODEN: JCMMC9; ISSN:1582-1838. (Wiley-Blackwell)

   A review. The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomol. target of therapeutical interest, the selection or the design of new lead compds. and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on mol. docking, virtual high-throughput screening and fragment-based ligand design.

   https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXktFKnsLo%253D&md5=58e5056f1dc8deafd3b861ba83910595

9. 9

   Schneider, G. From Theory to Bench Experiment by Computer-Assisted Drug Design Chimia 2012 , 66 ( 3 ) 120 – 124  DOI: 10.2533/chimia.2012.120

10. 10

    Woltosz, W. S. If We Designed Airplanes Like We Design Drugs··· J. Comput.-Aided Mol. Des. 2012 , 26 ( 1 ) 159 – 163  DOI: 10.1007/s10822-011-9490-5

12. 12

    Newman, D. J. ; Cragg, G. M. Natural Products as Sources of New Drugs From 1981 to 2014 J. Nat. Prod. 2016 , 79 ( 3 ) 629 – 661  DOI: 10.1021/acs.jnatprod.5b01055

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    12

    Natural Products as Sources of New Drugs from 1981 to 2014

    Newman, David J.; Cragg, Gordon M.

    Journal of Natural Products (2016), 79 (3), 629-661CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)

    This contribution is a completely updated and expanded version of the four prior analogous reviews that were published in this journal in 1997, 2003, 2007, and 2012. In the case of all approved therapeutic agents, the time frame has been extended to cover the 34 years from Jan. 1, 1981, to Dec. 31, 2014, for all diseases worldwide, and from 1950 (earliest so far identified) to Dec. 2014 for all approved antitumor drugs worldwide. As mentioned in the 2012 review, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions and the designation "natural product botanical", or "NB", to cover those botanical "defined mixts." now recognized as drug entities by the U.S. FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from around the 1940s to the end of 2014, of the 175 small mols. approved, 131, or 75%, are other than "S" (synthetic), with 85, or 49%, actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked, with, as expected from prior information, the anti-infective area being dependent on natural products and their structures. We wish to draw the attention of readers to the rapidly evolving recognition that a significant no. of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated", and therefore it is considered that this area of natural product research should be expanded significantly.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xit1Kqu7k%253D&md5=c9f2a44ab6b66331b7ef6ca64029328a

13. 13

    Seddon, G. ; Lounnas, V. ; McGuire, R. ; van den Bergh, T. ; Bywater, R. P. ; Oliveira, L. ; Vriend, G. J. Comput.-Aided Mol. Des. 2012 , 26 ( 1 ) 137 – 150  DOI: 10.1007/s10822-011-9519-9

14. 14

    Schmidt, T. ; Bergner, A. ; Schwede, T. Modelling Three-Dimensional Protein Structures for Applications in Drug Design Drug Discovery Today 2014 , 19 ( 7 ) 890 – 897  DOI: 10.1016/j.drudis.2013.10.027

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    14

    Modelling three-dimensional protein structures for applications in drug design

    Schmidt, Tobias; Bergner, Andreas; Schwede, Torsten

    Drug Discovery Today (2014), 19 (7), 890-897CODEN: DDTOFS; ISSN:1359-6446. (Elsevier Ltd.)

    A review. A structural perspective of drug target and anti-target proteins, and their mol. interactions with biol. active mols., largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimization. In the absence of exptl. 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homol. modeling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estn. deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately dets. its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modeling practice are also provided.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvVKit77P&md5=a3b8cf16a54666c85194c2c5f4817840

15. 15

    von Itzstein, M. ; Wu, W. Y. ; Kok, G. B. ; Pegg, M. S. ; Dyason, J. C. ; Jin, B. ; Van Phan, T. ; Smythe, M. L. ; White, H. F. ; Oliver, S. W. Rational Design of Potent Sialidase-Based Inhibitors of Influenza Virus Replication Nature 1993 , 363 ( 6428 ) 418 – 423  DOI: 10.1038/363418a0

16. 16

    Kim, C. U. ; Lew, W. ; Williams, M. A. ; Liu, H. ; Zhang, L. ; Swaminathan, S. ; Bischofberger, N. ; Chen, M. S. ; Mendel, D. B. ; Tai, C. Y. ; Laver, W. G. ; Stevens, R. C. Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogues with Potent Anti-Influenza Activity J. Am. Chem. Soc. 1997 , 119 ( 4 ) 681 – 690  DOI: 10.1021/ja963036t

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    16

    Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogs with Potent Anti-Influenza Activity

    Kim, Choung U.; Lew, Willard; Williams, Matthew A.; Zhang, Lijun; Liu, Hongtao; Swaminathan, S.; Bischofberger, Norbert; Chen, Ming S.; Tai, Chun Y.; Mendel, Dirk B.; Laver, W. Graeme; Stevens, Raymond C.

    Journal of the American Chemical Society (1997), 119 (4), 681-690CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)

    The design, synthesis, and in vitro evaluation of the novel carbocycles as transition-state-based inhibitors of influenza neuraminidase (NA) are described. The double bond position in the carbocyclic analogs plays an important role in NA inhibition as demonstrated by the antiviral activity of 8 (IC50 = 6.3 μM) vs 9 (IC50 > 200 μM). Structure-activity studies of a series of carbocyclic analogs, e.g. I (R = H, Me, Et, Pr, Bu), identified the 3-pentyloxy moiety as an apparent optimal group at the C3 position with an IC50 value of 1 nM for NA inhibition. The X-ray crystallog. structure of 6h bound to NA revealed the presence of a large hydrophobic pocket in the region corresponding to the glycerol subsite of sialic acid. The high antiviral potency obsd. for 6h appears to be attributed to a highly favorable hydrophobic interaction in this pocket. The practical prepn. of I starting from (-)-quinic acid is also described.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXitFWjuw%253D%253D&md5=cd90547fcba53d336a43900f8012d1a7

17. 17

    Grosdidier, A. ; Zoete, V. ; Michielin, O. SwissDock, a Protein-Small Molecule Docking Web Service Based on EADock DSS Nucleic Acids Res. 2011 , 39 , W270 – W277  DOI: 10.1093/nar/gkr366

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    17

    SwissDock, a protein-small molecule docking web service based on EADock DSS

    Grosdidier, Aurelien; Zoete, Vincent; Michielin, Olivier

    Nucleic Acids Research (2011), 39 (Web Server), W270-W277CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)

    Most life science processes involve, at the at. scale, recognition between two mols. The prediction of such interactions at the mol. level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small mols. on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for prepg. both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional mol. modeling community.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXosVOmsL4%253D&md5=3c241542cb9fa7286e67b9a9667c2657

18. 18

    Morris, G. M. ; Huey, R. ; Lindstrom, W. ; Sanner, M. F. ; Belew, R. K. ; Goodsell, D. S. ; Olson, A. J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility J. Comput. Chem. 2009 , 30 ( 16 ) 2785 – 2791  DOI: 10.1002/jcc.21256

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    18

    AutoDock and AutoDockTools: Automated docking with selective receptor flexibility

    Morris, Garrett M.; Huey, Ruth; Lindstrom, William; Sanner, Michel F.; Belew, Richard K.; Goodsell, David S.; Olson, Arthur J.

    Journal of Computational Chemistry (2009), 30 (16), 2785-2791CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

    We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking expt. with 188 diverse ligand-protein complexes and a cross-docking expt. using flexible sidechains in 87 HIV protease complexes. We also report its utility in anal. of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXht1GitrnK&md5=679ce22fc50e9291c9aa16e7a1855845

19. 19

    Trott, O. ; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading J. Comput. Chem. 2010 , 31 ( 2 ) 455 – 461  DOI: 10.1002/jcc.21334

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    19

    AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

    Trott, Oleg; Olson, Arthur J.

    Journal of Computational Chemistry (2010), 31 (2), 455-461CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

    AutoDock Vina, a new program for mol. docking and virtual screening, is presented. AutoDock Vina achieves an approx. 2 orders of magnitude speed-up compared with the mol. docking software previously developed in the authors' lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by the authors' tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calcs. the grid maps and clusters the results in a way transparent to the user.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhsFGnur3O&md5=c6974af8a1235f7aa09918d3e6f70dc4

20. 20

    Pettersen, E. F. ; Goddard, T. D. ; Huang, C. C. ; Couch, G. S. ; Greenblatt, D. M. ; Meng, E. C. ; Ferrin, T. E. UCSF Chimera--a Visualization System for Exploratory Research and Analysis J. Comput. Chem. 2004 , 25 ( 13 ) 1605 – 1612  DOI: 10.1002/jcc.20084

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    20

    UCSF Chimera-A visualization system for exploratory research and analysis

    Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.

    Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

    The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXmvVOhsbs%253D&md5=944b175f440c1ff323705987cf937ee7

21. 21

    Daina, A. ; Michielin, O. ; Zoete, V. iLOGP: a Simple, Robust, and Efficient Description of N-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach J. Chem. Inf. Model. 2014 , 54 ( 12 ) 3284 – 3301  DOI: 10.1021/ci500467k

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    21

    iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach

    Daina, Antoine; Michielin, Olivier; Zoete, Vincent

    Journal of Chemical Information and Modeling (2014), 54 (12), 3284-3301CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

    The n-octanol/water partition coeff. (log Po/w) is a key physicochem. parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the exptl. log Po/w on a cleansed data set of more than 17,500 mols. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of mols. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The phys. basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per mol.), and tridimensional mol. graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvVyru73K&md5=e04e337c0bbe76998c9fda9c79bdd88b

22. 22

    Daina, A. ; Zoete, V. A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules ChemMedChem 2016 , 11 ( 11 ) 1117 – 1121  DOI: 10.1002/cmdc.201600182

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    22

    A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

    Daina, Antoine; Zoete, Vincent

    ChemMedChem (2016), 11 (11), 1117-1121CODEN: CHEMGX; ISSN:1860-7179. (Wiley-VCH Verlag GmbH & Co. KGaA)

    Apart from efficacy and toxicity, many drug development failures are imputable to poor pharmacokinetics and bioavailability. Gastrointestinal absorption and brain access are two pharmacokinetic behaviors crucial to est. at various stages of the drug discovery processes. To this end, the Brain Or IntestinaL Estd. permeation method (BOILED-Egg) is proposed as an accurate predictive model that works by computing the lipophilicity and polarity of small mols. Concomitant predictions for both brain and intestinal permeation are obtained from the same two physicochem. descriptors and straightforwardly translated into mol. design, owing to the speed, accuracy, conceptual simplicity and clear graphical output of the model. The BOILED-Egg can be applied in a variety of settings, from the filtering of chem. libraries at the early steps of drug discovery, to the evaluation of drug candidates for development.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XosFWit78%253D&md5=2cf19e6fe089ef1c0d8f38f0fdb528cc

23. 23

    Gfeller, D. ; Michielin, O. ; Zoete, V. Shaping the Interaction Landscape of Bioactive Molecules Bioinformatics 2013 , 29 ( 23 ) 3073 – 3079  DOI: 10.1093/bioinformatics/btt540

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    23

    Shaping the interaction landscape of bioactive molecules

    Gfeller, David; Michielin, Olivier; Zoete, Vincent

    Bioinformatics (2013), 29 (23), 3073-3079CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)

    Motivation: Most bioactive mols. perform their action by interacting with proteins or other macromols. However, for a significant fraction of them, the primary target remains unknown. In addn., the majority of bioactive mols. have more than one target, many of which are poorly characterized. Computational predictions of bioactive mol. targets based on similarity with known ligands are powerful to narrow down the no. of potential targets and to rationalize side effects of known mols. Results: Using a ref. set of 224 412 mols. active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chem. similarity based on both chem. structure and mol. shape. Our results indicate that the combined approach is esp. efficient when no ligand with the same scaffold or from the same chem. series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different mol. properties, such as the no. of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new mols. and known ligands to accurately predict their targets.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvVarsbzP&md5=837ea7b88de4196af28e6e2af5ae85bb

24. 24

    Gfeller, D. ; Grosdidier, A. ; Wirth, M. ; Daina, A. ; Michielin, O. ; Zoete, V. SwissTargetPrediction: a Web Server for Target Prediction of Bioactive Small Molecules Nucleic Acids Res. 2014 , 42 ( W1 ) W32 – W38  DOI: 10.1093/nar/gku293

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    Gfeller, D. ; Zoete, V. Protein Homology Reveals New Targets for Bioactive Small Molecules Bioinformatics 2015 , 31 ( 16 ) 2721 – 2727  DOI: 10.1093/bioinformatics/btv214

26. 26

    Oprea, T. I. ; Bauman, J. E. ; Bologa, C. G. ; Buranda, T. ; Chigaev, A. ; Edwards, B. S. ; Jarvik, J. W. ; Gresham, H. D. ; Haynes, M. K. ; Hjelle, B. ; Hromas, R. ; Hudson, L. ; Mackenzie, D. A. ; Muller, C. Y. ; Reed, J. C. ; Simons, P. C. ; Smagley, Y. ; Strouse, J. ; Surviladze, Z. ; Thompson, T. ; Ursu, O. ; Waller, A. ; Wandinger-Ness, A. ; Winter, S. S. ; Wu, Y. ; Young, S. M. ; Larson, R. S. ; Willman, C. ; Sklar, L. A. Drug Repurposing From an Academic Perspective Drug Discovery Today: Ther. Strategies 2011 , 8 ( 3–4 ) 61 – 69  DOI: 10.1016/j.ddstr.2011.10.002

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    26

    Drug Repurposing from an Academic Perspective

    Oprea Tudor I; Bauman Julie E; Bologa Cristian G; Buranda Tione; Chigaev Alexandre; Edwards Bruce S; Jarvik Jonathan W; Gresham Hattie D; Haynes Mark K; Hjelle Brian; Hromas Robert; Hudson Laurie; Mackenzie Debra A; Muller Carolyn Y; Reed John C; Simons Peter C; Smagley Yelena; Strouse Juan; Surviladze Zurab; Thompson Todd; Ursu Oleg; Waller Anna; Wandinger-Ness Angela; Winter Stuart S; Wu Yang; Young Susan M; Larson Richard S; Willman Cheryl; Sklar Larry A

    Drug discovery today. Therapeutic strategies (2011), 8 (3-4), 61-69 ISSN:1740-6773.

    Academia and small business research units are poised to play an increasing role in drug discovery, with drug repurposing as one of the major areas of activity. Here we summarize project status for a number of drugs or classes of drugs: raltegravir, cyclobenzaprine, benzbromarone, mometasone furoate, astemizole, R-naproxen, ketorolac, tolfenamic acid, phenothiazines, methylergonovine maleate and beta-adrenergic receptor drugs, respectively. Based on this multi-year, multi-project experience we discuss strengths and weaknesses of academic-based drug repurposing research. Translational, target and disease foci are strategic advantages fostered by close proximity and frequent interactions between basic and clinical scientists, which often result in discovering new modes of action for approved drugs. On the other hand, lack of integration with pharmaceutical sciences and toxicology, lack of appropriate intellectual coverage and issues related to dosing and safety may lead to significant drawbacks. The development of a more streamlined regulatory process world-wide, and the development of pre-competitive knowledge transfer systems such as a global healthcare database focused on regulatory and scientific information for drugs world-wide, are among the ideas proposed to improve the process of academic drug discovery and repurposing, and to overcome the "valley of death" by bridging basic to clinical sciences.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2srisFelsw%253D%253D&md5=0b9776709e006061f82fb900a39f441d

27. 27

    Bertolini, F. ; Sukhatme, V. P. ; Bouche, G. Drug Repurposing in Oncology–Patient and Health Systems Opportunities Nat. Rev. Clin. Oncol. 2015 , 12 ( 12 ) 732 – 742  DOI: 10.1038/nrclinonc.2015.169

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    27

    Drug repurposing in oncology--patient and health systems opportunities

    Bertolini Francesco; Sukhatme Vikas P; Bouche Gauthier

    Nature reviews. Clinical oncology (2015), 12 (12), 732-42 ISSN:.

    In most countries, healthcare service budgets are not likely to support the current explosion in the cost of new oncology drugs. Repurposing the large arsenal of approved, non-anticancer drugs is an attractive strategy to offer more-effective options to patients with cancer, and has the substantial advantages of cheaper, faster and safer preclinical and clinical validation protocols. The potential benefits are so relevant that funding of academically and/or independently driven preclinical and clinical research programmes should be considered at both national and international levels. To date, successes in oncology drug repurposing have been limited, despite strong evidence supporting the use of many different drugs. A lack of financial incentives for drug developers and limited drug development experience within the non-profit sector are key reasons for this lack of success. We discuss these issues and offer solutions to finally seize this opportunity in the interest of patients and societies, globally.

    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC28zjtlCrtw%253D%253D&md5=f98990d04206495553bb97f5e4121b49

28. 28

    Wild, D. J. Cheminformatics for the Masses: a Chance to Increase Educational Opportunities for the Next Generation of Cheminformaticians J. Cheminf. 2013 , 5 ( 1 ) 32  DOI: 10.1186/1758-2946-5-32

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• Figures
• References

• Abstract

  

  Figure 1

  

  Figure 1. As a clear example of the molecular docking concept, educators can let participants dock a drug in the binding site of the protein target manually. Both the drug and the protein must be printed in 3D at the same scale. Here, we selected an example related to the first online workshop: an anti-inflammatory drug (ibuprofen in yellow) to be placed inside the cyclooxygenase 1 protein (COX1 in white). The printed protein model can be opened, exposing the ibuprofen binding site. The protein file, retrieved from the protein databank (PDB), (43) can be converted into a 3D-object and saved as STL, VMRL, or X3D files compatible with most 3D-printers, by using a molecular visualization software such as UCSF Chimera. (20)

  Figure 2

  

  Figure 2. General principle of the online Drug Design Workshop exemplified in the context of the inhibition of indolamine 2,3-dioxygenase (IDO1) by the optimization of a newly discovered inhibitor (PIM) to obtain a drug candidate (MMG-0358). Several cycles of optimization can be performed, during which the molecules are drawn by the users, automatically docked into the protein, scored for molecular complementarity, and analyzed for some ADMET properties and possible secondary targets. All technical aspects have been simplified and can be performed by one-click or drag-and-drop actions. NLG-919, L1MT, and AMG-1 are other known ligands of IDO1 used as examples in the workshop and defined in the Web site.

  Figure 3

  

  Figure 3. Input page of the Drug Design Workshop Web site.

  Figure 4

  

  Figure 4. Output page of the Drug Design Workshop Web site.

  Figure 5

  

  Figure 5. Output page of SwissTargetPrediction, obtained upon one-click query from the Drug Design Workshop Web site. Target names, common names, Uniprot ID, ChEMBL ID, and Target classes are those defined in the ChEMBL database, (46) which was used to build the predictive model of protein targets for small molecules. (47)

  Figure 6

  

  Figure 6. Output page of SwissADME, obtained upon one-click query from the Drug Design Workshop Web site. The upper panel shows the BOILED-Egg, a graphical classification model to predict gastrointestinal absorption (HIA, white ellipse) and permeation through the blood–brain barrier (BBB, yolk). (22) The position of the molecule on this panel is shown as a dot, whose color reflects the prediction for the molecule to be the substrate of the multidrug resistance protein "P-glycoprotein 1" (PGP). The lower panel compiles all predicted ADME parameters for the molecule under study. (48)

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     Bunker, J. P. The Role of Medical Care in Contributing to Health Improvements Within Societies Int. J. Epidemiol 2001 , 30 ( 6 ) 1260 – 1263  DOI: 10.1093/ije/30.6.1260

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     1

     The role of medical care in contributing to health improvements within societies

     Bunker J P

     International journal of epidemiology (2001), 30 (6), 1260-3 ISSN:0300-5771.

     There is no expanded citation for this reference.

     https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD38%252Fpt1ahtQ%253D%253D&md5=1fa2b3331f51e5d47498957bfdfffd9c

  2. 2

     Lichtenberg, F. R. The Impact of New Drug Launches on Longevity: Evidence From Longitudinal, Disease-Level Data From 52 Countries, 1982–2001 Int. J. Health Care Finance Econ 2005 , 5 ( 1 ) 47 – 73  DOI: 10.1007/s10754-005-6601-7

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     2

     The impact of new drug launches on longevity: evidence from longitudinal, disease-level data from 52 countries, 1982-2001

     Lichtenberg Frank R

     International journal of health care finance and economics (2005), 5 (1), 47-73 ISSN:1389-6563.

     We perform an econometric analysis of the effect of new drug launches on longevity, using data from the IMS Health Drug Launches database and the WHO Mortality Database. Under conservative assumptions, our estimates imply that the average annual increase in life expectancy of the entire population resulting from new drug launches is about one week, and that the incremental cost effectiveness ratio (new drug expenditure per person per year divided by the increase in life-years per person per year attributable to new drug launches) is about $6750--far lower than most estimates of the value of a statistical life-year.

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  3. 3

     Munos, B. Lessons From 60 Years of Pharmaceutical Innovation Nat. Rev. Drug Discovery 2009 , 8 ( 12 ) 959 – 968  DOI: 10.1038/nrd2961

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     3

     Lessons from 60 years of pharmaceutical innovation

     Munos, Bernard

     Nature Reviews Drug Discovery (2009), 8 (12), 959-968CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

     Despite unprecedented investment in pharmaceutical research and development (R&D), the no. of new drugs approved by the US Food and Drug Administration (FDA) remains low. To help understand this conundrum, this article investigates the record of pharmaceutical innovation by analyzing data on the companies that introduced the ∼1,200 new drugs that have been approved by the FDA since 1950. This anal. shows that the new-drug output from pharmaceutical companies in this period has essentially been const., and remains so despite the attempts to increase it. This suggests that, contrary to common perception, the new-drug output is not depressed, but may simply reflect the limitations of the current R&D model. The implications of these findings and options to achieve sustain-ability for the pharmaceutical industry are discussed.

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  4. 4

     Paul, S. M. ; Mytelka, D. S. ; Dunwiddie, C. T. ; Persinger, C. C. ; Munos, B. H. ; Lindborg, S. R. ; Schacht, A. L. How to Improve R&D Productivity: the Pharmaceutical Industry's Grand Challenge Nat. Rev. Drug Discovery 2010 , 9 ( 3 ) 203 – 214  DOI: 10.1038/nrd3078

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     4

     How to improve R&D productivity: the pharmaceutical industry's grand challenge

     Paul, Steven M.; Mytelka, Daniel S.; Dunwiddie, Christopher T.; Persinger, Charles C.; Munos, Bernard H.; Lindborg, Stacy R.; Schacht, Aaron L.

     Nature Reviews Drug Discovery (2010), 9 (3), 203-214CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

     A review. The pharmaceutical industry is under growing pressure from a range of environmental issues, including major losses of revenue owing to patent expirations, increasingly cost-constrained healthcare systems and more demanding regulatory requirements. In our view, the key to tackling the challenges such issues pose to both the future viability of the pharmaceutical industry and advances in healthcare is to substantially increase the no. and quality of innovative, cost-effective new medicines, without incurring unsustainable R&D costs. However, it is widely acknowledged that trends in industry R&D productivity have been moving in the opposite direction for a no. of years. Here, we present a detailed anal. based on comprehensive, recent, industry-wide data to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity. We then propose specific strategies that could have the most substantial impact in improving R&D productivity.

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  5. 5

     Arrowsmith, J. ; Miller, P. Trial Watch: Phase II and Phase III Attrition Rates 2011–2012 Nat. Rev. Drug Discovery 2013 , 12 , 569 – 569  DOI: 10.1038/nrd4090

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     5

     Trial Watch Phase II and Phase III attrition rates 2011-2012

     Arrowsmith, John; Miller, Philip

     Nature Reviews Drug Discovery (2013), 12 (8), 569CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

     There is no expanded citation for this reference.

     https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXht1SjtbvJ&md5=5c5ed93bf3e0badf0468ed364285dc12

  6. 6

     Waring, M. J. ; Arrowsmith, J. ; Leach, A. R. ; Leeson, P. D. ; Mandrell, S. ; Owen, R. M. ; Pairaudeau, G. ; Pennie, W. D. ; Pickett, S. D. ; Wang, J. ; Wallace, O. ; Weir, A. An Analysis of the Attrition of Drug Candidates From Four Major Pharmaceutical Companies Nat. Rev. Drug Discovery 2015 , 14 ( 7 ) 475 – 486  DOI: 10.1038/nrd4609

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     6

     An analysis of the attrition of drug candidates from four major pharmaceutical companies

     Waring, Michael J.; Arrowsmith, John; Leach, Andrew R.; Leeson, Paul D.; Mandrell, Sam; Owen, Robert M.; Pairaudeau, Garry; Pennie, William D.; Pickett, Stephen D.; Wang, Jibo; Wallace, Owen; Weir, Alex

     Nature Reviews Drug Discovery (2015), 14 (7), 475-486CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

     The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the no. of efficacy- and safety-related failures by analyzing possible links to the physicochem. properties of small-mol. drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and anal. of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The anal. reaffirms that control of physicochem. properties during compd. optimization is beneficial in identifying compds. of candidate drug quality and indicates for the first time a link between the physicochem. properties of compds. and clin. failure due to safety issues. The results also suggest that further control of physicochem. properties is unlikely to have a significant effect on attrition rates and that addnl. work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.

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  7. 7

     Jorgensen, W. L. Science 2004 , 303 ( 5665 ) 1813 – 1818  DOI: 10.1126/science.1096361

  8. 8

     Zoete, V. ; Grosdidier, A. ; Michielin, O. Docking, Virtual High Throughput Screening and in Silico Fragment-Based Drug Design J. Cell. Mol. Med. 2009 , 13 ( 2 ) 238 – 248  DOI: 10.1111/j.1582-4934.2008.00665.x

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     8

     Docking, virtual high throughput screening and in silico fragment-based drug design

     Zoete, Vincent; Grosdidier, Aurelien; Michielin, Olivier

     Journal of Cellular and Molecular Medicine (2009), 13 (2), 238-248CODEN: JCMMC9; ISSN:1582-1838. (Wiley-Blackwell)

     A review. The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomol. target of therapeutical interest, the selection or the design of new lead compds. and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on mol. docking, virtual high-throughput screening and fragment-based ligand design.

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  9. 9

     Schneider, G. From Theory to Bench Experiment by Computer-Assisted Drug Design Chimia 2012 , 66 ( 3 ) 120 – 124  DOI: 10.2533/chimia.2012.120

  10. 10

      Woltosz, W. S. If We Designed Airplanes Like We Design Drugs··· J. Comput.-Aided Mol. Des. 2012 , 26 ( 1 ) 159 – 163  DOI: 10.1007/s10822-011-9490-5

  12. 12

      Newman, D. J. ; Cragg, G. M. Natural Products as Sources of New Drugs From 1981 to 2014 J. Nat. Prod. 2016 , 79 ( 3 ) 629 – 661  DOI: 10.1021/acs.jnatprod.5b01055

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      12

      Natural Products as Sources of New Drugs from 1981 to 2014

      Newman, David J.; Cragg, Gordon M.

      Journal of Natural Products (2016), 79 (3), 629-661CODEN: JNPRDF; ISSN:0163-3864. (American Chemical Society-American Society of Pharmacognosy)

      This contribution is a completely updated and expanded version of the four prior analogous reviews that were published in this journal in 1997, 2003, 2007, and 2012. In the case of all approved therapeutic agents, the time frame has been extended to cover the 34 years from Jan. 1, 1981, to Dec. 31, 2014, for all diseases worldwide, and from 1950 (earliest so far identified) to Dec. 2014 for all approved antitumor drugs worldwide. As mentioned in the 2012 review, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions and the designation "natural product botanical", or "NB", to cover those botanical "defined mixts." now recognized as drug entities by the U.S. FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from around the 1940s to the end of 2014, of the 175 small mols. approved, 131, or 75%, are other than "S" (synthetic), with 85, or 49%, actually being either natural products or directly derived therefrom. In other areas, the influence of natural product structures is quite marked, with, as expected from prior information, the anti-infective area being dependent on natural products and their structures. We wish to draw the attention of readers to the rapidly evolving recognition that a significant no. of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated", and therefore it is considered that this area of natural product research should be expanded significantly.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xit1Kqu7k%253D&md5=c9f2a44ab6b66331b7ef6ca64029328a

  13. 13

      Seddon, G. ; Lounnas, V. ; McGuire, R. ; van den Bergh, T. ; Bywater, R. P. ; Oliveira, L. ; Vriend, G. J. Comput.-Aided Mol. Des. 2012 , 26 ( 1 ) 137 – 150  DOI: 10.1007/s10822-011-9519-9

  14. 14

      Schmidt, T. ; Bergner, A. ; Schwede, T. Modelling Three-Dimensional Protein Structures for Applications in Drug Design Drug Discovery Today 2014 , 19 ( 7 ) 890 – 897  DOI: 10.1016/j.drudis.2013.10.027

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      14

      Modelling three-dimensional protein structures for applications in drug design

      Schmidt, Tobias; Bergner, Andreas; Schwede, Torsten

      Drug Discovery Today (2014), 19 (7), 890-897CODEN: DDTOFS; ISSN:1359-6446. (Elsevier Ltd.)

      A review. A structural perspective of drug target and anti-target proteins, and their mol. interactions with biol. active mols., largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimization. In the absence of exptl. 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homol. modeling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estn. deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately dets. its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modeling practice are also provided.

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  15. 15

      von Itzstein, M. ; Wu, W. Y. ; Kok, G. B. ; Pegg, M. S. ; Dyason, J. C. ; Jin, B. ; Van Phan, T. ; Smythe, M. L. ; White, H. F. ; Oliver, S. W. Rational Design of Potent Sialidase-Based Inhibitors of Influenza Virus Replication Nature 1993 , 363 ( 6428 ) 418 – 423  DOI: 10.1038/363418a0

  16. 16

      Kim, C. U. ; Lew, W. ; Williams, M. A. ; Liu, H. ; Zhang, L. ; Swaminathan, S. ; Bischofberger, N. ; Chen, M. S. ; Mendel, D. B. ; Tai, C. Y. ; Laver, W. G. ; Stevens, R. C. Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogues with Potent Anti-Influenza Activity J. Am. Chem. Soc. 1997 , 119 ( 4 ) 681 – 690  DOI: 10.1021/ja963036t

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      16

      Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogs with Potent Anti-Influenza Activity

      Kim, Choung U.; Lew, Willard; Williams, Matthew A.; Zhang, Lijun; Liu, Hongtao; Swaminathan, S.; Bischofberger, Norbert; Chen, Ming S.; Tai, Chun Y.; Mendel, Dirk B.; Laver, W. Graeme; Stevens, Raymond C.

      Journal of the American Chemical Society (1997), 119 (4), 681-690CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)

      The design, synthesis, and in vitro evaluation of the novel carbocycles as transition-state-based inhibitors of influenza neuraminidase (NA) are described. The double bond position in the carbocyclic analogs plays an important role in NA inhibition as demonstrated by the antiviral activity of 8 (IC50 = 6.3 μM) vs 9 (IC50 > 200 μM). Structure-activity studies of a series of carbocyclic analogs, e.g. I (R = H, Me, Et, Pr, Bu), identified the 3-pentyloxy moiety as an apparent optimal group at the C3 position with an IC50 value of 1 nM for NA inhibition. The X-ray crystallog. structure of 6h bound to NA revealed the presence of a large hydrophobic pocket in the region corresponding to the glycerol subsite of sialic acid. The high antiviral potency obsd. for 6h appears to be attributed to a highly favorable hydrophobic interaction in this pocket. The practical prepn. of I starting from (-)-quinic acid is also described.

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  17. 17

      Grosdidier, A. ; Zoete, V. ; Michielin, O. SwissDock, a Protein-Small Molecule Docking Web Service Based on EADock DSS Nucleic Acids Res. 2011 , 39 , W270 – W277  DOI: 10.1093/nar/gkr366

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      17

      SwissDock, a protein-small molecule docking web service based on EADock DSS

      Grosdidier, Aurelien; Zoete, Vincent; Michielin, Olivier

      Nucleic Acids Research (2011), 39 (Web Server), W270-W277CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)

      Most life science processes involve, at the at. scale, recognition between two mols. The prediction of such interactions at the mol. level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small mols. on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for prepg. both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional mol. modeling community.

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  18. 18

      Morris, G. M. ; Huey, R. ; Lindstrom, W. ; Sanner, M. F. ; Belew, R. K. ; Goodsell, D. S. ; Olson, A. J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility J. Comput. Chem. 2009 , 30 ( 16 ) 2785 – 2791  DOI: 10.1002/jcc.21256

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      18

      AutoDock and AutoDockTools: Automated docking with selective receptor flexibility

      Morris, Garrett M.; Huey, Ruth; Lindstrom, William; Sanner, Michel F.; Belew, Richard K.; Goodsell, David S.; Olson, Arthur J.

      Journal of Computational Chemistry (2009), 30 (16), 2785-2791CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

      We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking expt. with 188 diverse ligand-protein complexes and a cross-docking expt. using flexible sidechains in 87 HIV protease complexes. We also report its utility in anal. of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009.

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  19. 19

      Trott, O. ; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading J. Comput. Chem. 2010 , 31 ( 2 ) 455 – 461  DOI: 10.1002/jcc.21334

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      19

      AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

      Trott, Oleg; Olson, Arthur J.

      Journal of Computational Chemistry (2010), 31 (2), 455-461CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

      AutoDock Vina, a new program for mol. docking and virtual screening, is presented. AutoDock Vina achieves an approx. 2 orders of magnitude speed-up compared with the mol. docking software previously developed in the authors' lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by the authors' tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calcs. the grid maps and clusters the results in a way transparent to the user.

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  20. 20

      Pettersen, E. F. ; Goddard, T. D. ; Huang, C. C. ; Couch, G. S. ; Greenblatt, D. M. ; Meng, E. C. ; Ferrin, T. E. UCSF Chimera--a Visualization System for Exploratory Research and Analysis J. Comput. Chem. 2004 , 25 ( 13 ) 1605 – 1612  DOI: 10.1002/jcc.20084

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      20

      UCSF Chimera-A visualization system for exploratory research and analysis

      Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.

      Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

      The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.

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  21. 21

      Daina, A. ; Michielin, O. ; Zoete, V. iLOGP: a Simple, Robust, and Efficient Description of N-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach J. Chem. Inf. Model. 2014 , 54 ( 12 ) 3284 – 3301  DOI: 10.1021/ci500467k

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      21

      iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach

      Daina, Antoine; Michielin, Olivier; Zoete, Vincent

      Journal of Chemical Information and Modeling (2014), 54 (12), 3284-3301CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

      The n-octanol/water partition coeff. (log Po/w) is a key physicochem. parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the exptl. log Po/w on a cleansed data set of more than 17,500 mols. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of mols. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The phys. basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per mol.), and tridimensional mol. graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

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  22. 22

      Daina, A. ; Zoete, V. A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules ChemMedChem 2016 , 11 ( 11 ) 1117 – 1121  DOI: 10.1002/cmdc.201600182

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      22

      A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

      Daina, Antoine; Zoete, Vincent

      ChemMedChem (2016), 11 (11), 1117-1121CODEN: CHEMGX; ISSN:1860-7179. (Wiley-VCH Verlag GmbH & Co. KGaA)

      Apart from efficacy and toxicity, many drug development failures are imputable to poor pharmacokinetics and bioavailability. Gastrointestinal absorption and brain access are two pharmacokinetic behaviors crucial to est. at various stages of the drug discovery processes. To this end, the Brain Or IntestinaL Estd. permeation method (BOILED-Egg) is proposed as an accurate predictive model that works by computing the lipophilicity and polarity of small mols. Concomitant predictions for both brain and intestinal permeation are obtained from the same two physicochem. descriptors and straightforwardly translated into mol. design, owing to the speed, accuracy, conceptual simplicity and clear graphical output of the model. The BOILED-Egg can be applied in a variety of settings, from the filtering of chem. libraries at the early steps of drug discovery, to the evaluation of drug candidates for development.

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  23. 23

      Gfeller, D. ; Michielin, O. ; Zoete, V. Shaping the Interaction Landscape of Bioactive Molecules Bioinformatics 2013 , 29 ( 23 ) 3073 – 3079  DOI: 10.1093/bioinformatics/btt540

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      23

      Shaping the interaction landscape of bioactive molecules

      Gfeller, David; Michielin, Olivier; Zoete, Vincent

      Bioinformatics (2013), 29 (23), 3073-3079CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)

      Motivation: Most bioactive mols. perform their action by interacting with proteins or other macromols. However, for a significant fraction of them, the primary target remains unknown. In addn., the majority of bioactive mols. have more than one target, many of which are poorly characterized. Computational predictions of bioactive mol. targets based on similarity with known ligands are powerful to narrow down the no. of potential targets and to rationalize side effects of known mols. Results: Using a ref. set of 224 412 mols. active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chem. similarity based on both chem. structure and mol. shape. Our results indicate that the combined approach is esp. efficient when no ligand with the same scaffold or from the same chem. series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different mol. properties, such as the no. of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new mols. and known ligands to accurately predict their targets.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvVarsbzP&md5=837ea7b88de4196af28e6e2af5ae85bb

  24. 24

      Gfeller, D. ; Grosdidier, A. ; Wirth, M. ; Daina, A. ; Michielin, O. ; Zoete, V. SwissTargetPrediction: a Web Server for Target Prediction of Bioactive Small Molecules Nucleic Acids Res. 2014 , 42 ( W1 ) W32 – W38  DOI: 10.1093/nar/gku293

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      Gfeller, D. ; Zoete, V. Protein Homology Reveals New Targets for Bioactive Small Molecules Bioinformatics 2015 , 31 ( 16 ) 2721 – 2727  DOI: 10.1093/bioinformatics/btv214

  26. 26

      Oprea, T. I. ; Bauman, J. E. ; Bologa, C. G. ; Buranda, T. ; Chigaev, A. ; Edwards, B. S. ; Jarvik, J. W. ; Gresham, H. D. ; Haynes, M. K. ; Hjelle, B. ; Hromas, R. ; Hudson, L. ; Mackenzie, D. A. ; Muller, C. Y. ; Reed, J. C. ; Simons, P. C. ; Smagley, Y. ; Strouse, J. ; Surviladze, Z. ; Thompson, T. ; Ursu, O. ; Waller, A. ; Wandinger-Ness, A. ; Winter, S. S. ; Wu, Y. ; Young, S. M. ; Larson, R. S. ; Willman, C. ; Sklar, L. A. Drug Repurposing From an Academic Perspective Drug Discovery Today: Ther. Strategies 2011 , 8 ( 3–4 ) 61 – 69  DOI: 10.1016/j.ddstr.2011.10.002

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      26

      Drug Repurposing from an Academic Perspective

      Oprea Tudor I; Bauman Julie E; Bologa Cristian G; Buranda Tione; Chigaev Alexandre; Edwards Bruce S; Jarvik Jonathan W; Gresham Hattie D; Haynes Mark K; Hjelle Brian; Hromas Robert; Hudson Laurie; Mackenzie Debra A; Muller Carolyn Y; Reed John C; Simons Peter C; Smagley Yelena; Strouse Juan; Surviladze Zurab; Thompson Todd; Ursu Oleg; Waller Anna; Wandinger-Ness Angela; Winter Stuart S; Wu Yang; Young Susan M; Larson Richard S; Willman Cheryl; Sklar Larry A

      Drug discovery today. Therapeutic strategies (2011), 8 (3-4), 61-69 ISSN:1740-6773.

      Academia and small business research units are poised to play an increasing role in drug discovery, with drug repurposing as one of the major areas of activity. Here we summarize project status for a number of drugs or classes of drugs: raltegravir, cyclobenzaprine, benzbromarone, mometasone furoate, astemizole, R-naproxen, ketorolac, tolfenamic acid, phenothiazines, methylergonovine maleate and beta-adrenergic receptor drugs, respectively. Based on this multi-year, multi-project experience we discuss strengths and weaknesses of academic-based drug repurposing research. Translational, target and disease foci are strategic advantages fostered by close proximity and frequent interactions between basic and clinical scientists, which often result in discovering new modes of action for approved drugs. On the other hand, lack of integration with pharmaceutical sciences and toxicology, lack of appropriate intellectual coverage and issues related to dosing and safety may lead to significant drawbacks. The development of a more streamlined regulatory process world-wide, and the development of pre-competitive knowledge transfer systems such as a global healthcare database focused on regulatory and scientific information for drugs world-wide, are among the ideas proposed to improve the process of academic drug discovery and repurposing, and to overcome the "valley of death" by bridging basic to clinical sciences.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2srisFelsw%253D%253D&md5=0b9776709e006061f82fb900a39f441d

  27. 27

      Bertolini, F. ; Sukhatme, V. P. ; Bouche, G. Drug Repurposing in Oncology–Patient and Health Systems Opportunities Nat. Rev. Clin. Oncol. 2015 , 12 ( 12 ) 732 – 742  DOI: 10.1038/nrclinonc.2015.169

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      27

      Drug repurposing in oncology--patient and health systems opportunities

      Bertolini Francesco; Sukhatme Vikas P; Bouche Gauthier

      Nature reviews. Clinical oncology (2015), 12 (12), 732-42 ISSN:.

      In most countries, healthcare service budgets are not likely to support the current explosion in the cost of new oncology drugs. Repurposing the large arsenal of approved, non-anticancer drugs is an attractive strategy to offer more-effective options to patients with cancer, and has the substantial advantages of cheaper, faster and safer preclinical and clinical validation protocols. The potential benefits are so relevant that funding of academically and/or independently driven preclinical and clinical research programmes should be considered at both national and international levels. To date, successes in oncology drug repurposing have been limited, despite strong evidence supporting the use of many different drugs. A lack of financial incentives for drug developers and limited drug development experience within the non-profit sector are key reasons for this lack of success. We discuss these issues and offer solutions to finally seize this opportunity in the interest of patients and societies, globally.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC28zjtlCrtw%253D%253D&md5=f98990d04206495553bb97f5e4121b49

  28. 28

      Wild, D. J. Cheminformatics for the Masses: a Chance to Increase Educational Opportunities for the Next Generation of Cheminformaticians J. Cheminf. 2013 , 5 ( 1 ) 32  DOI: 10.1186/1758-2946-5-32

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      28

      Cheminformatics for the masses: a chance to increase educational opportunities for the next generation of cheminformaticians

      Wild, David J.

      Journal of Cheminformatics (2013), 5 (), 32CODEN: JCOHB3; ISSN:1758-2946. (Chemistry Central Ltd.)

      A review. This paper describes the cheminformatics for masses and a chance to increase educational opportunities for next generation of cheminformaticians.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXht1WmtbbI&md5=f6380ceebdafb6ca6b5fffa3e27f5c35

  29. 29

      Tsai, C. S. Using Computer Applications and Online Resources to Teach and Learn Pharmaceutical Chemistry J. Chem. Educ. 2007 , 84 ( 12 ) 2019  DOI: 10.1021/ed084p2019

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      29

      Using computer applications and online resources to teach and learn pharmaceutical chemistry

      Tsai, C. Stan

      Journal of Chemical Education (2007), 84 (12), 2019-2023CODEN: JCEDA8; ISSN:0021-9584. (Journal of Chemical Education, Dept. of Chemistry)

      A lecture and workshop course for teaching computer applications in pharmaceutical chem. to upper-level undergraduate chem. and biochem. students were developed. The course introduces the principles of pharmaceutical chem. in drug discovery and design with an emphasis on the use of computers to solve pharmaceutical chem. problems. The lectures deal with pharmacokinetics, pharmacodynamics, receptor biochem., structure-activity relationships, pharmacophore anal., pharmacoinformatics, and computer-aided drug design.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtlWmu7bE&md5=2d7ab4155b9497f8c51d3e0f8ee1c8f5

  30. 30

      Rodrigues, R. P. ; Andrade, S. F. ; Mantoani, S. P. ; Eifler-Lima, V. L. ; Silva, V. B. ; Kawano, D. F. Using Free Computational Resources to Illustrate the Drug Design Process in an Undergraduate Medicinal Chemistry Course J. Chem. Educ. 2015 , 92 ( 5 ) 827 – 835  DOI: 10.1021/ed500195d

      [ACS Full Text ], [CAS], Google Scholar

      30

      Using Free Computational Resources To Illustrate the Drug Design Process in an Undergraduate Medicinal Chemistry Course

      Rodrigues, Ricardo P.; Andrade, Saulo F.; Mantoani, Susimaire P.; Eifler-Lima, Vera L.; Silva, Vinicius B.; Kawano, Daniel F.

      Journal of Chemical Education (2015), 92 (5), 827-835CODEN: JCEDA8; ISSN:0021-9584. (American Chemical Society and Division of Chemical Education, Inc.)

      Advances in, and dissemination of, computer technologies in the field of drug research now enable the use of mol. modeling tools to teach important concepts of drug design to chem. and pharmacy students. A series of computer labs. is described to introduce undergraduate students to commonly adopted in silico drug design methods, such as mol. geometry optimization, pharmacophore modeling, protein-ligand docking simulations, homol. modeling, virtual screening, and pharmacokinetics/toxicity predictions. Freely available software and web servers are selected to compose this pedagogical resource, such that it can be easily implemented in any institution equipped with an Internet connection and Windows OS computers. This material is an illustration of a drug discovery pipeline, starting from the structure of known drugs to obtain novel bioactive compds., and, therefore, is a valid pedagogical instrument for educating future professionals in the field of drug development.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXjtF2mtbo%253D&md5=de4385b5d0d53595cb1d04891ed8a142

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      Price, G. W. ; Gould, P. S. ; Marsh, A. Use of Freely Available and Open Source Tools for in Silico Screening in Chemical Biology J. Chem. Educ. 2014 , 91 ( 4 ) 602 – 604  DOI: 10.1021/ed400302u

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      31

      Use of Freely Available and Open Source Tools for In Silico Screening in Chemical Biology

      Price, Gareth W.; Gould, Phillip S.; Marsh, Andrew

      Journal of Chemical Education (2014), 91 (4), 602-604CODEN: JCEDA8; ISSN:0021-9584. (American Chemical Society and Division of Chemical Education, Inc.)

      Automated computational docking of large libraries of chem. compds. to a protein can aid in pharmaceutical drug design and gives scientists with basic computer experience a tool to help plan wet lab. investigations when exploring the combination of chem. and pharmacol. spaces. The use of open source tools to develop and select ligands for subsequent screening is outlined. A protocol leveraging the power of Open Babel and AutoDock Vina to perform file conversion, minimization, and docking implemented as a Python script is offered.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXisV2gtLo%253D&md5=6e584ccd855dbd92f9dbedf446b47281

  32. 32

      Sutch, B. T. ; Romero, R. M. ; Neamati, N. ; Haworth, I. S. Integrated Teaching of Structure-Based Drug Design and Biopharmaceutics: a Computer-Based Approach J. Chem. Educ. 2012 , 89 ( 1 ) 45 – 51  DOI: 10.1021/ed200151b

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      32

      Integrated Teaching of Structure-Based Drug Design and Biopharmaceutics: A Computer-Based Approach

      Sutch, Brian T.; Romero, Rebecca M.; Neamati, Nouri; Haworth, Ian S.

      Journal of Chemical Education (2012), 89 (1), 45-51CODEN: JCEDA8; ISSN:0021-9584. (American Chemical Society and Division of Chemical Education, Inc.)

      Rational drug design requires expertise in structural biol., medicinal chem., physiol., and related fields. In teaching structure-based drug design, it is important to develop an understanding of the need for early recognition of mols. with "drug-like" properties as a key component. That is, it is not merely sufficient to teach students how to design an effective inhibitor for a particular protein; instead, it is important to convey the need for simultaneous consideration of biopharmaceutical properties that will optimize the chances of the inhibitor becoming a drug. These are advanced concepts, but they can be addressed through computer-based methods. Here, an educational approach using a case study is described in which students "design" a potential drug through use of software, most of which is Web-based and freely available.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhtFelu7bE&md5=b88c5d33f38a423c29e353d366b553d2

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      Carvalho, I. ; Borges, Á. D. L. ; Bernardes, L. S. C. Medicinal Chemistry and Molecular Modeling: an Integration to Teach Drug Structure–Activity Relationship and the Molecular Basis of Drug Action J. Chem. Educ. 2005 , 82 ( 4 ) 588  DOI: 10.1021/ed082p588

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      Hayes, J. M. An Integrated Visualization and Basic Molecular Modeling Laboratory for First-Year Undergraduate Medicinal Chemistry J. Chem. Educ. 2014 , 91 ( 6 ) 919 – 923  DOI: 10.1021/ed400486d

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      34

      An Integrated Visualization and Basic Molecular Modeling Laboratory for First-Year Undergraduate Medicinal Chemistry

      Hayes, Joseph M.

      Journal of Chemical Education (2014), 91 (6), 919-923CODEN: JCEDA8; ISSN:0021-9584. (American Chemical Society and Division of Chemical Education, Inc.)

      A 3D model visualization and basic mol. modeling lab. suitable for first-year undergraduates studying introductory medicinal chem. is presented. The 2 h practical is embedded within a series of lectures on drug design, target-drug interactions, enzymes, receptors, nucleic acids, and basic pharmacokinetics. Serving as a teaching aid to the lecture material, 3D models of biol. macromols. exploiting Schroedinger software and the Maestro graphical user interface (GUI) is explored to enhance student learning. A considerably pos. response was received from the participants. Background and details of the lab. are outlined, while the student handout with answers is included as Supporting Information.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmtFWlu78%253D&md5=1b27ac2b4afcabe2709fd16489e0ddc7

  35. 35

      Gledhill, R. ; Kent, S. ; Hudson, B. ; Richards, W. G. ; Essex, J. W. ; Frey, J. G. A Computer-Aided Drug Discovery System for Chemistry Teaching J. Chem. Inf. Model. 2006 , 46 ( 3 ) 960 – 970  DOI: 10.1021/ci050383q

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      A Computer-Aided Drug Discovery System for Chemistry Teaching

      Gledhill, Robert; Kent, Sarah; Hudson, Brian; Richards, W. Graham; Essex, Jonathan W.; Frey, Jeremy G.

      Journal of Chemical Information and Modeling (2006), 46 (3), 960-970CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

      The Schools Malaria Project (http://emalaria.soton.ac.uk/) brings together school students with university researchers in the hunt for a new antimalaria drug. The design challenge being offered to students is to use a distributed drug search and selection system to design potential antimalaria drugs. The system is accessed via a Web interface. This e-science project displays the results of the trials in an accessible manner, giving students an opportunity for discussion and debate both with peers and with the university contacts. The project has been implemented by using distributed computing techniques, spreading computer load over a network of machines that cross institutional boundaries, forming a grid. This provides access to greater computing power and allows a much more complex and detailed formulation of the drug design problem to be tackled for research, teaching, and learning.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XmtF2lsA%253D%253D&md5=71141b601517f25d0d53fb59b2a88c96

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      Zoete, V. ; Cuendet, M.A. ; Grosdider, A. ; Michielin, O. SwissParam: A Fast Force Field Generation Tool for Small Organic Moleules J. Comput. Chem. 2011 , 32 ( 11 ) 2359 – 2368  DOI: 10.1002/jcc.21816

  37. 37

      Gfeller, D. ; Michielin, O. ; Zoete, V. SwissSidechain: a Molecular and Structural Database of Non-Natural Sidechains Nucleic Acids Res. 2013 , 41 ( D1 ) D327 – D332  DOI: 10.1093/nar/gks991

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      Wirth, M. ; Zoete, V. ; Michielin, O. ; Sauer, W. H. B. SwissBioisostere: a Database of Molecular Replacements for Ligand Design Nucleic Acids Res. 2013 , 41 ( D1 ) D1137 – D1143  DOI: 10.1093/nar/gks1059

  39. 39

      Zoete, V. ; Daina, A. ; Bovigny, C. ; Michielin, O. SwissSimilarity: a Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening J. Chem. Inf. Model. 2016 , 56 ( 8 ) 1399 – 1404  DOI: 10.1021/acs.jcim.6b00174

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      39

      SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening

      Zoete, Vincent; Daina, Antoine; Bovigny, Christophe; Michielin, Olivier

      Journal of Chemical Information and Modeling (2016), 56 (8), 1399-1404CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

      SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small mols. Screenable compds. include drugs, bioactive and com. mols., as well as 205 million of virtual compds. readily synthesizable from com. available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D mol. fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field. SwissSimilarity is accessible free of charge or login at http://www.swisssimilarity.ch.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtFegsbnL&md5=f8fb4fd88ef476ec5c9d530d7a23844a

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      Bollag, G. ; Tsai, J. ; Zhang, J. ; Zhang, C. ; Ibrahim, P. ; Nolop, K. ; Hirth, P. Vemurafenib: the First Drug Approved for BRAF-Mutant Cancer Nat. Rev. Drug Discovery 2012 , 11 ( 11 ) 873 – 886  DOI: 10.1038/nrd3847

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      41

      Vemurafenib: the first drug approved for BRAF-mutant cancer

      Bollag, Gideon; Tsai, James; Zhang, Jiazhong; Zhang, Chao; Ibrahim, Prabha; Nolop, Keith; Hirth, Peter

      Nature Reviews Drug Discovery (2012), 11 (11), 873-886CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)

      A review. The identification of driver oncogenes has provided important targets for drugs that can change the landscape of cancer therapies. One such example is the BRAF oncogene, which is found in about half of all melanomas as well as several other cancers. As a druggable kinase, oncogenic BRAF has become a crucial target of small-mol. drug discovery efforts. Following a rapid clin. development path, vemurafenib (Zelboraf; Plexxikon/Roche) was approved for the treatment of BRAF-mutated metastatic melanoma in the United States in August 2011 and the European Union in Feb. 2012. This Review describes the underlying biol. of BRAF, the technol. used to identify vemurafenib and its clin. development milestones, along with future prospects based on lessons learned during its development.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsV2rsLjL&md5=b1b38cad542be9b3a30060b77c584bc1

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      Röhrig, U. F. ; Majjigapu, S. R. ; Vogel, P. ; Zoete, V. ; Michielin, O. Challenges in the Discovery of Indoleamine 2,3-Dioxygenase 1 (IDO1) Inhibitors J. Med. Chem. 2015 , 58 ( 24 ) 9421 – 9437  DOI: 10.1021/acs.jmedchem.5b00326

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      Berman, H. M. The Protein Data Bank Nucleic Acids Res. 2000 , 28 ( 1 ) 235 – 242  DOI: 10.1093/nar/28.1.235

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      43

      The Protein Data Bank

      Berman, Helen M.; Westbrook, John; Feng, Zukang; Gilliland, Gary; Bhat, T. N.; Weissig, Helge; Shindyalov, Ilya N.; Bourne, Philip E.

      Nucleic Acids Research (2000), 28 (1), 235-242CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)

      The Protein Data Bank (PDB; http://www.rcsb.org/pdb/)is the single worldwide archive of structural data of biol. macromols. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXhvVKjt7w%253D&md5=227fb393f754be2be375ab727bfd05dc

  46. 46

      Bento, A. P. ; Gaulton, A. ; Hersey, A. ; Bellis, L. J. ; Chambers, J. ; Davies, M. ; Krüger, F. A. ; Light, Y. ; Mak, L. ; McGlinchey, S. ; Nowotka, M. ; Papadatos, G. ; Santos, R. ; Overington, J. P. The ChEMBL Bioactivity Database: an Update Nucleic Acids Res. 2014 , 42 ( D1 ) D1083 – D1090  DOI: 10.1093/nar/gkt1031

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      46

      The ChEMBL bioactivity database: an update

      Bento, A. Patricia; Gaulton, Anna; Hersey, Anne; Bellis, Louisa J.; Chambers, Jon; Davies, Mark; Krueger, Felix A.; Light, Yvonne; Mak, Lora; McGlinchey, Shaun; Nowotka, Michal; Papadatos, George; Santos, Rita; Overington, John P.

      Nucleic Acids Research (2014), 42 (D1), D1083-D1090CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)

      ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compds. from research stages through clin. development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a no. of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addn. to the web-based interface, data downloads and web services.

      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXoslWl&md5=31b832d03d56ea3065d7aa29618362bc

  47. 47

      Gfeller, D. ; Grosdidier, A. ; Wirth, M. ; Daina, A. ; Michielin, O. ; Zoete, V. SwissTargetPrediction: a Web Server for Target Prediction of Bioactive Small Molecules Nucleic Acids Res. 2014 , 42 ( W1 ) W32 – W38  DOI: 10.1093/nar/gku293

Structure Based Drug Design Tools

Source: https://pubs.acs.org/doi/10.1021/acs.jchemed.6b00596

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