Bibliography of computer-aided Drug Design

Updated on 7/18/2014. Currently 2130 references

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2013 / 2012 / 2011 / 2010 / 2009 / 2008 / 2007 / 2006 / 2004 / 2003 / 2001 /


2013

  • Rational Approaches to Improving Selectivity in Drug Design
    Huggins, David J. and Sherman, Woody and Tidor, Bruce
    Journal of medicinal chemistry, 2013, 55(4), 1424-1444
    PMID: 22239221    
     

  • Open source software and web services for designing therapeutic molecules.
    Singla, Deepak and Dhanda, Sandeep Kumar and Chauhan, Jagat Singh and Bhardwaj, Anshu and Brahmachari, Samir K andOpen Source Drug Discovery Consortium} and Raghava, Gajendra P S
    Current topics in medicinal chemistry, 2013, 13(10), 1172-1191
    PMID: 23647540    
     
    Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollar and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.

  • Computational methods for drug design and discovery: focus on China.
    Zheng, Mingyue and Liu, Xian and Xu, Yuan and Li, Honglin and Luo, Cheng and Jiang, Hualiang
    Trends in pharmacological sciences, 2013
    PMID: 24035675     doi: 10.1016/j.tips.2013.08.004
     
    In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.

2012

  • Can we really do computer-aided drug design?
    Segall, Matthew
    Journal of computer-aided molecular design, 2012, 26(1), 121-124
    PMID: 22160553     doi: 10.1007/s10822-011-9512-3
     
    In this article, we discuss what we mean by 'design' and contrast this with the application of computational methods in drug discovery. We suggest that the predictivity of the computational models currently applied in drug discovery is not yet sufficient to permit a true design paradigm, as demonstrated by the large number of compounds that must currently be synthesised and tested to identify a successful drug. However, despite the uncertainties in predictions, computational methods have enormous potential value in narrowing the range of compounds to consider, by eliminating those that have negligible chance of being a successful drug, while focussing efforts on chemistries with the best likelihood of success. Applied appropriately, computational approaches can support decision-makers in achieving multi-parameter optimisation to guide the selection and design of compounds with the best chance of achieving an appropriate balance of properties for a drug discovery project's objectives. Finally, we consider some approaches that may contribute over the next 25 years to improve the accuracy and transferability of computational models in drug discovery and move towards a genuine design process.

  • In silico design of small molecules.
    Bernardo, Paul H and Tong, Joo Chuan
    Methods in molecular biology (Clifton, N.J.), 2012, 800, 25-31
    PMID: 21964780     doi: 10.1007/978-1-61779-349-3_3
     
    Computational methods now play an integral role in modern drug discovery, and include the design and management of small molecule libraries, initial hit identification through virtual screening, optimization of the affinity and selectivity of hits, and improving the physicochemical properties of the lead compounds. In this chapter, we survey the most important data sources for the discovery of new molecular entities, and discuss the key considerations and guidelines for virtual chemical library design.

  • From theory to bench experiment by computer-assisted drug design.
    Schneider, Gisbert
    Chimia, 2012, 66(3), 120-124
    PMID: 22546255     doi: 10.2533/chimia.2012.120
     
    Tight integration of computer-assisted molecular design with practical realization by medicinal chemistry will be essential for finding next-generation drugs that are optimized for multiple pharmaceutically relevant properties. ETH Zürich has established an interdisciplinary research group devoted to exploring the potential of this scientific approach by combining expertise from pharmaceutical chemistry and computer sciences. In this article, some of the group's activities and projects are presented. A current focus is on machine-learning applications aiming at hit and lead structure identification by virtual screening and de novo design. The central concept of 'adaptive fitness landscapes' is highlighted along with practical examples from drug discovery projects.

  • State-of-the-art technology in modern computer-aided drug design
    Dalkas, G A and Vlachakis, D and Tsagkrasoulis, D and Kastania, A and Kossida, S
    Briefings in bioinformatics, 2012
    PMID: 23148324     doi: 10.1093/bib/bbs063
     
    The quest for small drug-like compounds that selectively inhibit the function of biological targets has always been a major focus in the pharmaceutical industry and in academia as well. High-throughput screening of compound libraries requires time, cost and resources. Therefore, the use of alternative methods is necessary for facilitating lead discovery. Computational techniques that dock small molecules into macromolecular targets and predict the affinity and activity of the small molecule are widely used in drug design and discovery, and have become an integral part of the industrial and academic research. In this review, we present an overview of some state-of-the-art technologies in modern drug design that have been developed for expediting the search for novel drug candidates.

  • Early phase drug discovery: cheminformatics and computational techniques in identifying lead series.
    Duffy, Bryan C and Zhu, Lei and Decornez, Hélène and Kitchen, Douglas B
    Bioorganic & Medicinal Chemistry, 2012, 20(18), 5324-5342
    PMID: 22938785     doi: 10.1016/j.bmc.2012.04.062
     
    Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

  • Is there a future for computational chemistry in drug research?
    Maggiora, Gerald M
    Journal of computer-aided molecular design, 2012, 26(1), 87-90
    PMID: 22101364     doi: 10.1007/s10822-011-9493-2
     
    Improvements in computational chemistry methods have had a growing impact on drug research. But will incremental improvements be sufficient to ensure this continues? Almost all existing efforts to discover new drugs depend on the classic one target, one drug paradigm, although the situation is changing slowly. A new paradigm that focuses on a more systems biology approach and takes account of the reality that most drugs exhibit some level of polypharmacology is beginning to emerge. This will bring about dramatic changes that can significantly influence the role that computational methods play in future drug research. But these changes require that current methods be augmented with those from bioinformatics and engineering if the field is to have a significant impact on future drug research.

  • If we designed airplanes like we design drugs....
    Woltosz, Walter S
    Journal of computer-aided molecular design, 2012, 26(1), 159-163
    PMID: 22139474     doi: 10.1007/s10822-011-9490-5
     
    In the early days, airplanes were put together with parts designed for other purposes (bicycles, farm equipment, textiles, automotive equipment, etc.). They were then flown by their brave designers to see if the design would work-often with disastrous results. Today, airplanes, helicopters, missiles, and rockets are designed in computers in a process that involves iterating through enormous numbers of designs before anything is made. Until very recently, novel drug-like molecules were nearly always made first like early airplanes, then tested to see if they were any good (although usually not on the brave scientists who created them!). The resulting extremely high failure rate is legendary. This article describes some of the evolution of computer-based design in the aerospace industry and compares it with the progress made to date in computer-aided drug design. Software development for pharmaceutical research has been largely entrepreneurial, with only relatively limited support from government and industry end-user organizations. The pharmaceutical industry is still about 30 years behind aerospace and other industries in fully recognizing the value of simulation and modeling and funding the development of the tools needed to catch up.

  • Does your model weigh the same as a duck?
    Jain, Ajay N and Cleves, Ann E
    Journal of computer-aided molecular design, 2012, 26(1), 57-67
    PMID: 22187141     doi: 10.1007/s10822-011-9530-1
     
    Computer-aided drug design is a mature field by some measures, and it has produced notable successes that underpin the study of interactions between small molecules and living systems. However, unlike a truly mature field, fallacies of logic lie at the heart of the arguments in support of major lines of research on methodology and validation thereof. Two particularly pernicious ones are cum hoc ergo propter hoc (with this, therefore because of this) and confirmation bias (seeking evidence that is confirmatory of the hypothesis at hand). These fallacies will be discussed in the context of off-target predictive modeling, QSAR, molecular similarity computations, and docking. Examples will be shown that avoid these problems.

  • Should medicinal chemists do molecular modelling?
    Ritchie, Timothy J and McLay, Iain M
    Drug discovery today, 2012, 17(11-12), 534-537
    PMID: 22269135     doi: 10.1016/j.drudis.2012.01.005
     
    In this article we discuss the pros and cons of medicinal chemists undertaking three-dimensional (3D) computer-aided drug design (CADD) activities for themselves, from the viewpoint of both medicinal chemists and computational chemists. We describe how best this can be implemented, the potential benefits that can be obtained and the pitfalls that are often encountered.

  • Computer-aided drug design: lead discovery and optimization.
    Xiang, Mingli and Cao, Yu and Fan, Wenjie and Chen, Lijuan and Mo, Yirong
    Combinatorial chemistry & high throughput screening, 2012, 15(4), 328-337
    PMID: 22221065    
     
    Over the past decade, there have been remarkable advances in the area of computer-aided drug design (CADD), which has been applied at almost all stages in the drug discovery pipeline. The generation of initial lead compounds and the subsequent optimization aimed at improving potency and pharmacological properties are the core activities among all. The development in these aspects over the past years will be the focus of this review.

  • Gazing into the crystal ball; the future of computer-aided drug design.
    Martin, Eric and Ertl, Peter and Hunt, Peter and Duca, Jose and Lewis, Richard
    Journal of computer-aided molecular design, 2012, 26(1), 77-79
    PMID: 22089332     doi: 10.1007/s10822-011-9487-0
     

  • Some thoughts on the "A" in computer-aided molecular design.
    Rarey, Matthias
    Journal of computer-aided molecular design, 2012, 26(1), 113-114
    PMID: 22160587     doi: 10.1007/s10822-011-9507-0
     

  • The perspectives of computational chemistry modeling.
    Tetko, Igor V
    Journal of computer-aided molecular design, 2012, 26(1), 135-136
    PMID: 22160554     doi: 10.1007/s10822-011-9513-2
     
    The on-line tools for computational chemistry modeling will be increasingly used in the future. This will bring the advantages both for the authors and the readers.

  • Chemoinformatics: a view of the field and current trends in method development.
    Vogt, Martin and Bajorath, Jürgen
    Bioorganic & Medicinal Chemistry, 2012, 20(18), 5317-5323
    PMID: 22483841     doi: 10.1016/j.bmc.2012.03.030
     
    The chemoinformatics field continues to evolve at the interface between computer science and chemistry. Chemical information and computational approaches in pharmaceutical research are major focal points of chemoinformatics. However, the boundaries of this discipline are rather fluid and the chemoinformatics spectrum is difficult to delineate. The field is in flux, which also provides opportunities for further developments. As a lead-in to the Chemoinformatics Symposium-in-Print, we present a brief view of this discipline (with a little anecdotal touch), highlight current trends in method development, and discuss a number of representative examples.

  • Chemoinformatics: recent advances at the interfaces between computer and chemical information sciences, chemistry, and drug discovery.
    Bajorath, Jürgen
    Bioorganic & Medicinal Chemistry, 2012, 20(18), 5316
    PMID: 22980097     doi: 10.1016/j.bmc.2012.08.051
     

  • Drug design for ever, from hype to hope.
    Seddon, G and Lounnas, V and McGuire, R and van den Bergh, T and Bywater, R P and Oliveira, L and Vriend, G
    Journal of computer-aided molecular design, 2012, 26(1), 137-150
    PMID: 22252446     doi: 10.1007/s10822-011-9519-9
     
    In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

  • The errors of our ways: taking account of error in computer-aided drug design to build confidence intervals for our next 25 years.
    Stouch, Terry Richard
    Journal of computer-aided molecular design, 2012, 6(1), 125-134
    PMID: 22246296     doi: 10.1007/s10822-012-9541-6
     
    The future of the advancement as well as the reputation of computer-aided drug design will be guided by a more thorough understanding of the domain of applicability of our methods and the errors and confidence intervals of their results. The implications of error in current force fields applied to drug design are given are given as an example. Even as our science advances and our hardware become increasingly more capable, our software will be perhaps the most important aspect in this realization. Some recommendations for the future are provided. Education of users is essential for proper use and interpretation of computational results in the future.

2011

  • Molecular dynamics simulations and drug discovery.
    Durrant, Jacob D and McCammon, J Andrew
    BMC biology, 2011, 9, 71
    PMID: 22035460     doi: 10.1186/1741-7007-9-71
     
    This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

  • IN SILICO DRUG DESIGN-TOOL FOR OVERCOMING THE INNOVATION DEFICIT IN THE DRUG DISCOVERY PROCESS
    Bharath, E N and Manjula, S N and Vijaychand, A
    International Journal of Pharmacy and Pharmaceutical Sciences, 2011, 3(2), 8-12
     
    Increasing costs of drug development and reduced number of new chemical entities have been a growing concern for new drug development in recent years. A number of potential reasons for this outcome have been considered. One of them is a general perception that applied sciences have not kept pace with the advances of basic sciences. Therefore, there is a need for the use of alternative tools to get answers on efficacy and safety faster, with more certainty and at lower cost. One such alternative tool is the in silico drug design or the computer aided drug design (CADD). In silico drug design can play a significant role in all stages of drug development from the preclinical discovery stage to late stage clinical development. Its use in drug development helps in selecting only a potent lead molecule and may thus prevent the late stage clinical failures; thereby a significant reduction in cost can be achieved. This article gives an insight to all the aspects of in silico drug design; its potential, drivers and restraints, current scenario and the future prospects.

  • Improving Drug Candidates by Design: A Focus on Physicochemical Properties As a Means of Improving Compound Disposition and Safety
    Meanwell, Nicholas A
    Chemical Research in Toxicology, 2011, 24(9), 1420-1456
    PMID: 21790149     doi: 10.1021/tx200211v
     

  • In Silico Toxicology - Non-Testing Methods
    Pelkonen, Olavi
    Frontiers in Pharmacology, 2011, 2, 1-8
    PMID: 21772821     doi: 10.3389/fphar.2011.00033
     
    In silico toxicology in its broadest sense means ``anything that we can do with a computer in toxicology.'' Many different types of in silico methods have been developed to characterize and predict toxic outcomes in humans and environment. The term non-testing methods denote grouping approaches, structure-activity relationship, and expert systems. These methods are already used for regulatory purposes and it is anticipated that their role will be much more prominent in the near future. This Perspective will delineate the basic prin- ciples of non-testing methods and evaluate their role in current and future risk assessment of chemical compounds.

  • Web-based services for drug design and discovery
    Frey, Jeremy G and Bird, Colin L
    Expert opinion on drug discovery, 2011, 6(9), 885-895
     
    Introduction: Reviews of the development of drug discovery through the 20th century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply `the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. Areas covered: This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that...

  • What do medicinal chemists actually make? A 50-year retrospective.
    Walters, W Patrick and Green, Jeremy and Weiss, Jonathan R and Murcko, Mark A
    Journal of medicinal chemistry, 2011, 54(19), 6405-6416
    PMID: 21755928     doi: 10.1021/jm200504p
     

  • How were new medicines discovered?
    Swinney, David C and Anthony, Jason
    Nature reviews. Drug discovery, 2011, 10(7), 507-519
    PMID: 21701501     doi: 10.1038/nrd3480
     
    Preclinical strategies that are used to identify potential drug candidates include target-based screening, phenotypic screening, modification of natural substances and biologic-based approaches. To investigate whether some strategies have been more successful than others in the discovery of new drugs, we analysed the discovery strategies and the molecular mechanism of action (MMOA) for new molecular entities and new biologics that were approved by the US Food and Drug Administration between 1999 and 2008. Out of the 259 agents that were approved, 75 were first-in-class drugs with new MMOAs, and out of these, 50 (67%) were small molecules and 25 (33%) were biologics. The results also show that the contribution of phenotypic screening to the discovery of first-in-class small-molecule drugs exceeded that of target-based approaches - with 28 and 17 of these drugs coming from the two approaches, respectively - in an era in which the major focus was on target-based approaches. We postulate that a target-centric approach for first-in-class drugs, without consideration of an optimal MMOA, may contribute to the current high attrition rates and low productivity in pharmaceutical research and development.

  • Computational medicinal chemistry.
    Schneider, Gisbert
    Future medicinal chemistry, 2011, 3(4), 393-394
    PMID: 21452974     doi: 10.4155/fmc.11.10
     

  • Informatics, machine learning and computational medicinal chemistry.
    Mitchell, John B O
    Future medicinal chemistry, 2011, 3(4), 451-467
    PMID: 21452981     doi: 10.4155/fmc.11.11
     
    This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein-ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility.

  • An active role for machine learning in drug development.
    Murphy, Robert F
    Nature chemical biology, 2011, 7(6), 327-330
    PMID: 21587249     doi: 10.1038/nchembio.576
     

  • Structure-based drug design to augment hit discovery.
    Kalyaanamoorthy, Subha and Chen, Yi-Ping Phoebe
    Drug discovery today, 2011, 16(17-18), 831-839
    PMID: 21810482     doi: 10.1016/j.drudis.2011.07.006
     
    Several technology-based strategies have been developed to address the significance of the two phases of drug discovery: hit identification and lead identification. Structure-based drug design (SBDD), a method that depends on possessing the knowledge of 3D structures of biological targets, is growing swiftly with the development of new technologies for searching potential ways to combat disease. The past decade has evidenced a threefold increase in the amount of software and tools in the online repositories. Herein, we review the in silico strategies and modules applied at the level of hit identification and confer the different challenges with possible solutions in enhancing the success rate of the 'hit-to-lead' phase that could eventually help the progress of SBDD in the drug discovery arena.

  • Modern Methods & Web Resources in Drug Design & Discovery
    Khan, Feroz and Yadav, Dharmendra Kumar and Maurya, Anupam andSonia} and Srivastava, Santosh Kumar
    Letters in Drug Design & Discovery, 2011, 8(5), 469-490
     
    Traditionally, the process of drug development has revolved around a screening approach and trial-and-error method, as no body knew which compound or approach could serve as a drug or therapy. This discovery process was very time consuming and laborious and discovery of a new drug used to take around 8-14 years and costs about US $1.8 billion. In order to minimize the time and cost in this drug discovery process, scientists around the world contributed tremendously and come up with a modern drug-designing program. The beauty of this modern drug designing is that now we can tailor the drug with desired combinations computationally before going for experimental laboratory work. In this review, traditional to modern methods of drug designing & discovery have been discussed. It covers the available web tools/databases and in silico techniques used in virtual screening and drug discovery processes to reduce the wet lab economy and time. Studies suggest that the best method for molecular docking based target identification is probably a hybrid of various types of algorithm encompassing novel search and scoring strategies e. g., PMF score, Dock score, Gold score etc. However, apart from in vitro assays and in vivo experiments, application of in silico QSAR & ADMET in the prediction of biological activity & bioavailability are proving beneficial in drug discovery process.

  • Software and resources for computational medicinal chemistry.
    Liao, Chenzhong and Sitzmann, Markus and Pugliese, Angelo and Nicklaus, Marc C
    Future medicinal chemistry, 2011, 3(8), 1057-1085
    PMID: 21707404     doi: 10.4155/fmc.11.63
     
    Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

  • Computational medicinal chemistry.
    Stahl, Martin and Bajorath, Jürgen
    Journal of medicinal chemistry, 2011, 54(1), 1-2
    PMID: 20973560     doi: 10.1021/jm1013055
     

  • The role of computational methods in the identification of bioactive compounds
    Glick, Meir and Jacoby, Edgar
    Current opinion in chemical biology, 2011, 15(4), 540-546
    doi: 10.1016/j.cbpa.2011.02.021
     
    ... Chemical space as a source fron new drugs . Med Chem Commun, 1 ( 2010 ), pp. ... DJ Abraham, DP Rotella (Eds.), Burger's Medicinal Chemistry, Drug Discovery , and Development (edn 7), John Wiley & Sons, Inc, Malden ( 2010 ), pp. 573-592. ... Chem Biol Drug Des, 76 ( 2010 ), pp. ...

  • The role of computational methods in the identification of bioactive compounds
    Glick, Meir and Jacoby, Edgar
    Current opinion in chemical biology, 2011, 15(4), 540-546
    doi: 10.1016/j.cbpa.2011.02.021
     
    ... Chemical space as a source fron new drugs . Med Chem Commun, 1 ( 2010 ), pp. ... DJ Abraham, DP Rotella (Eds.), Burger's Medicinal Chemistry, Drug Discovery , and Development (edn 7), John Wiley & Sons, Inc, Malden ( 2010 ), pp. 573-592. ... Chem Biol Drug Des, 76 ( 2010 ), pp. ...

  • Designing the molecular future.
    Schneider, Gisbert
    Journal of computer-aided molecular design, 2011, 26(1), 115-120
    PMID: 22127731     doi: 10.1007/s10822-011-9485-2
     
    Approximately 25 years ago the first computer applications were conceived for the purpose of automated 'de novo' drug design, prominent pioneering tools being ALADDIN, CAVEAT, GENOA, and DYLOMMS. Many of these early concepts were enabled by innovative techniques for ligand-receptor interaction modeling like GRID, MCSS, DOCK, and CoMFA, which still provide the theoretical framework for several more recently developed molecular design algorithms. After a first wave of software tools and groundbreaking applications in the 1990s-expressly GROW, GrowMol, LEGEND, and LUDI representing some of the key players-we are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor and ligand-based drug design have recently been published. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate dispute.

2010

  • Troubleshooting computational methods in drug discovery.
    Kortagere, Sandhya and Ekins, Sean
    Journal of pharmacological and toxicological methods, 2010, 61(2), 67-75
    PMID: 20176118     doi: 10.1016/j.vascn.2010.02.005
     
    Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen as an efficient approach for lead discovery as well as providing insights on absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). What is perhaps less well known and widely described are the limitations of the different technologies. We have therefore presented a troubleshooting approach to QSAR, homology modeling, docking as well as hybrid methods. If such computational or cheminformatics methods are to become more widely used by non-experts it is critical that such limitations are brought to the user's attention and addressed during their workflows. This could improve the quality of the models and results that are obtained.

  • In silico fragment-based drug design
    Konteatis, Zenon D.
    Expert opinion on drug discovery, 2010, 5(11), 1047-1065
    PMID: 22827744     doi: 10.1517/17460441.2010.523697
     
    Importance of the field: In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates.Areas covered in this review: Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed.What the reader will gain: The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs.Take home message: In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein-protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.

2009

  • The multiple roles of computational chemistry in fragment-based drug design.
    Law, Richard and Barker, Oliver and Barker, John J and Hesterkamp, Thomas and Godemann, Robert and Andersen, Ole and Fryatt, Tara and Courtney, Steve and Hallett, Dave and Whittaker, Mark
    Journal of computer-aided molecular design, 2009, 23(8), 459-473
    PMID: 19533374     doi: 10.1007/s10822-009-9284-1
     
    Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.

  • Recent advances in computer-aided drug design.
    Song, Chun Meng and Lim, Shen Jean and Tong, Joo Chuan
    Briefings in bioinformatics, 2009, 10(5), 579-591
    PMID: 19433475     doi: 10.1093/bib/bbp023
     
    Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

  • Rational drug design.
    Mandal, Soma and Moudgil, Mee'nal and Mandal, Sanat K
    European journal of pharmacology, 2009, 625(1-3), 90-100
    PMID: 19835861     doi: 10.1016/j.ejphar.2009.06.065
     
    In this article, current knowledge of drug design is reviewed and an approach of rational drug design is presented. The process of drug development is challenging, expensive, and time consuming, although this process has been accelerated due to the development of computational tools and methodologies. The current target based drug design approach is incomplete because most of the drugs developed by structure guided approaches have been shown to have serious toxic side effects. Otherwise these drugs would have been an ideal choice for the treatment of diseases. Hence, rational drug design would require a multidisciplinary approach. In this regard, incorporation of gene expression technology and bioinformatics tools would be indispensable in the structure based drug design. Global gene expression data and analysis of such data using bioinformatics tools will have numerous benefits such as efficiency, cost effectiveness, time saving, and will provide strategies for combination therapy in addition to overcoming toxic side effects. As a result of incorporation of gene expression data, partial benefit of the structure based drug design is slowly emerging and rapidly changing the approach of the drug development process. To achieve the full benefit of developing a successful drug, multidisciplinary approaches (approaches such as computational chemistry and gene expression analysis, as discussed in this article) would be necessary. In the future, there is adequate room for the development of more sophisticated methodologies.

  • Docking, virtual high throughput screening and in silico fragment-based drug design.
    Zoete, Vincent and Grosdidier, Aurélien and Michielin, Olivier
    Journal of cellular and molecular medicine, 2009, 13(2), 238-248
    PMID: 19183238     doi: 10.1111/j.1582-4934.2008.00665.x
     
    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 biomolecular target of therapeutical interest, the selection or the design of new lead compounds 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 molecular docking, virtual high-throughput screening and fragment-based ligand design.

2008

  • Computational toxicology in drug development.
    Muster, Wolfgang and Breidenbach, Alexander and Fischer, Holger and Kirchner, Stephan and Müller, Lutz and Pähler, Axel
    Drug discovery today, 2008, 13(7-8), 303-310
    PMID: 18405842     doi: 10.1016/j.drudis.2007.12.007
     
    Computational tools for predicting toxicity have been envisaged for their potential to considerably impact the attrition rate of compounds in drug discovery and development. In silico techniques like knowledge-based expert systems (quantitative) structure activity relationship tools and modeling approaches may therefore help to significantly reduce drug development costs by succeeding in predicting adverse drug reactions in preclinical studies. It has been shown that commercial as well as proprietary systems can be successfully applied in the pharmaceutical industry. As the prediction has been exhaustively optimized for early safety-relevant endpoints like genotoxicity, future activities will now be directed to prevent the occurrence of undesired toxicity in patients by making these tools more relevant to human disease.

  • Network pharmacology: the next paradigm in drug discovery.
    Hopkins, Andrew L
    Nature chemical biology, 2008, 4(11), 682-690
    PMID: 18936753     doi: 10.1038/nchembio.118
     
    The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development-efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

  • Current topics in computer-aided drug design.
    Taft, Carlton A and Da Silva, Vinicius Barreto and Da Silva, Carlos Henrique Tomich De Paula
    Journal of pharmaceutical sciences, 2008, 97(3), 1089-1098
    PMID: 18214973     doi: 10.1002/jps.21293
     
    The addition of computer-aided drug design (CADD) technologies to the research and drug discovery approaches could lead to a reduction of up to 50% in the cost of drug design. Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Development and drug discovery is a time-consuming, expensive, and interdisciplinary process whereas scientific advancements during the past two decades have altered the way pharmaceutical research produces new bioactive molecules. Advances in computational techniques and hardware solutions have enabled in silico methods to speed up lead optimization and identification. We will review current topics in computer-aided molecular design underscoring some of the most recent approaches and interdisciplinary processes. We will discuss some of the most efficient pathways and design.

2007

  • Free resources to assist structure-based virtual ligand screening experiments.
    Villoutreix, Bruno O. and Renault, Nicolas and Lagorce, David and Sperandio, Olivier and Montes, Matthieu and Miteva, Maria A.
    Current Protein & Peptide Science, 2007, 8(4), 381-411
    PMID: 17696871    
     
    In today's research environment, a wealth of experimental/theoretical structural data is available and the number of therapeutically relevant macromolecular structures is growing rapidly. This, coupled with the huge number of small non-peptide potential drug candidates easily available (over 7 million compounds), highlight the need of using computer-aided techniques for the efficient identification and optimization of novel hit compounds. Virtual (or in silico) ligand screening based on the three-dimensional structure of macromolecular targets (SB-VLS) is firmly established as an important approach to identify chemical entities that have a high likelihood of binding to a target molecule to elicit desired biological responses. A myriad of free applications and services facilitating the drug discovery process have been posted on the Web. In this review, we cite over 350 URLs that are useful for SB-VLS projects and essentially free for academic groups. We attempt to provide links for in silico ADME/tox prediction tools, compound collections, some ligand-based methods, characterization/simulation of 3D targets and homology modeling tools, druggable pocket predictions, active site comparisons, analysis of macromolecular interfaces, protein docking tools to help identify binding pockets and protein-ligand docking/scoring methods. As such, we aim at providing both, methods pertaining to the field of Structural Bioinformatics (defined here as tools to study macromolecules) and methods pertaining to the field of Chemoinformatics (defined here as tools to make better decisions faster in the arena of drug/lead identification and optimization). We also report several recent success stories using these free computer methods. This review should help readers finding free computer tools useful for their projects. Overall, we are confident that these tools will facilitate rapid and cost-effective identification of new hit compounds. The URLs presented in this review will be updated regularly at www.vls3d.com in the coming months, "Links" section.

  • Ligand efficiency indices for effective drug discovery
    Abad-Zapatero, Cele
    Expert opinion on drug discovery, 2007, 2(4), 469-488
     
    Successful drug discovery requires the optimization of a large number of var- iables ranging from strictly physicochemical parameters such as molecular weight to more complex parameters related to toxicity and bioavailability. Presently, structure-based methodologies influence many aspects of the drug discovery process from lead discovery to the final preclinical characterization. However, critical biological issues along the path to the market have dimin- ished the impact and power of this methodology. The physicochemical prop- erties of the novel chemical entities designed and guided by structural methods have become the subject of intense scrutiny from lead discovery to drug candidate. The idea of ligand efficiency (binding energy/non-hydrogen atoms) has recently emerged as a useful guide to optimize fragment and lead selection in the discovery process. More generalized concepts of ligand effi- ciency, related to efficiency per dalton and per unit of polar surface area, have also been introduced and will be discussed in the broader context. Preliminary results and trends obtained using ligand efficiencies as guides are reviewed and their future application to guide drug discovery will be discussed, as well as their integration into the structure-based drug design methods to make them more effective and numerically robust.

  • Designing drugs on the internet? Free web tools and services supporting medicinal chemistry.
    Ertl, Peter and Jelfs, Stephen
    Current topics in medicinal chemistry, 2007, 7(15), 1491-1501
    PMID: 17897035    
     
    The drug discovery process is supported by a multitude of freely available tools on the Internet. This paper summarizes some of the databases and tools that are of particular interest to medicinal chemistry. These include numerous data collections that provide access to valuable chemical data resources, allowing complex queries of compound structures, associated physicochemical properties and biological activities to be performed and, in many cases, providing links to commercial chemical suppliers. Further applications are available for searching protein-ligand complexes and identifying important binding interactions that occur. This is particularly useful for understanding the molecular recognition of ligands in the lead optimization process. The Internet also provides access to databases detailing metabolic pathways and transformations which can provide insight into disease mechanism, identify new targets entities or the potential off-target effects of a drug candidate. Furthermore, sophisticated online cheminformatics tools are available for processing chemical structures, predicting properties, and generating 2D or 3D structure representations-often required prior to more advanced analyses. The Internet provides a wealth of valuable resources that, if fully exploited, can greatly benefit the drug discovery community. In this paper, we provide an overview of some of the more important of these and, in particular, the freely accessible resources that are currently available.

  • Protein Flexibility and Mobility in Structure-Based Drug Design
    Ahmed, Aqeel and Kazemi, Sina and Gohlke, Holger
    , 2007, 3(11), 455-476
     
    ... Protein Flexibility and Mobility Frontiers in Drug Design & Discovery, 2007, Vol. ... IFREDA was tested for seven protein kinase complexes from four different subfamilies for which it ... The method was also tested in a small virtual screening scenario where the known active ligands ...

  • Protein Flexibility and Mobility in Structure-Based Drug Design
    Ahmed, Aqeel and Kazemi, Sina and Gohlke, Holger
    , 2007, 3(11), 455-476
     
    ... Protein Flexibility and Mobility Frontiers in Drug Design & Discovery, 2007, Vol. ... IFREDA was tested for seven protein kinase complexes from four different subfamilies for which it ... The method was also tested in a small virtual screening scenario where the known active ligands ...

2006

  • Can we rationally design promiscuous drugs?
    Hopkins, Andrew L and Mason, Jonathan S and Overington, John P
    Current opinion in structural biology, 2006, 16(1), 127-136
    PMID: 16442279     doi: 10.1016/j.sbi.2006.01.013
     
    Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.

  • Structural e-bioinformatics and drug design
    Carpy, AJM and Marchand-Geneste, N
    Current Protein & Peptide Science, 2006, 7(5), 1-10
    doi: 10.1080/10659360600560966
     
    Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

  • Structural e-bioinformatics and drug design
    Carpy, AJM and Marchand-Geneste, N
    Current Protein & Peptide Science, 2006, 7(5), 1-10
    doi: 10.1080/10659360600560966
     
    Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

  • Optimizing the use of open-source software applications in drug discovery 10.1016/S1359-6446(05)03692-5 : Drug Discovery Today | ScienceDirect.com
    Geldenhuys, WJ and Gaasch, KE and Watson, M
    Drug discovery\ldots}, 2006, 11(3-4), 127-132
     
    Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs.

2004

  • The many roles of computation in drug discovery.
    Jorgensen, William L
    Science (New York, N.Y.), 2004, 303(5665), 1813-1818
    PMID: 15031495     doi: 10.1126/science.1096361
     
    An overview is given on the diverse uses of computational chemistry in drug discovery. Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.

2003

  • Hit and lead generation: beyond high-throughput screening.
    Bleicher, Konrad H and Böhm, Hans-Joachim and Müller, Klaus and Alanine, Alexander I
    Nature reviews. Drug discovery, 2003, 2(5), 369-378
    PMID: 12750740     doi: 10.1038/nrd1086
     
    The identification of small-molecule modulators of protein function, and the process of transforming these into high-content lead series, are key activities in modern drug discovery. The decisions taken during this process have far-reaching consequences for success later in lead optimization and even more crucially in clinical development. Recently, there has been an increased focus on these activities due to escalating downstream costs resulting from high clinical failure rates. In addition, the vast emerging opportunities from efforts in functional genomics and proteomics demands a departure from the linear process of identification, evaluation and refinement activities towards a more integrated parallel process. This calls for flexible, fast and cost-effective strategies to meet the demands of producing high-content lead series with improved prospects for clinical success.

  • Selection criteria for drug-like compounds
    Muegge, Ingo
    Medicinal research reviews, 2003, 23(3), 302-321
    doi: 10.1002/med.10041
     
    ... C. Bioisosteres For the medicinal chemist, the bioisosteric replacement of functional groups of drug candidates is often essential due to, for example, pharmacokinetic ... Many bioisosteric replacements such as tetrazole for carboxylate or thiophene for phenyl are broadly used. ...

  • Selection criteria for drug-like compounds
    Muegge, Ingo
    Medicinal research reviews, 2003, 23(3), 302-321
    doi: 10.1002/med.10041
     
    ... C. Bioisosteres For the medicinal chemist, the bioisosteric replacement of functional groups of drug candidates is often essential due to, for example, pharmacokinetic ... Many bioisosteric replacements such as tetrazole for carboxylate or thiophene for phenyl are broadly used. ...

2001

  • GROMACS 3.0: A package for molecular simulation and trajectory analysis
    Lindahl, E and Hess, B and van der Spoel, D
    J. Mol. Mod., 2001, 7, 306
     
    ... Free energy calculations GROMACS can calculate the free energy difference be- tween two systems A and B, characterized by hamiltoni- ans HA and HB, using a coupling parameter approach. ... ( 3 ) such that H( 0 )

  • GROMACS 3.0: A package for molecular simulation and trajectory analysis
    Lindahl, E and Hess, B and van der Spoel, D
    J. Mol. Mod., 2001, 7, 306
     
    ... Free energy calculations GROMACS can calculate the free energy difference be- tween two systems A and B, characterized by hamiltoni- ans HA and HB, using a coupling parameter approach. ... ( 3 ) such that H( 0 )