Bibliography of computer-aided Drug Design

Updated on 7/18/2014. Currently 2130 references

Docking / Web services

2014 / 2013 / 2012 / 2011 / 2009 / 2008 / 2007 / 2006 / 2005 /


  • istar: a web platform for large-scale protein-ligand docking.
    Li, Hongjian and Leung, Kwong-Sak and Ballester, Pedro J and Wong, Man-Hon
    PloS one, 2014, 9(1), e85678
    PMID: 24475049     doi: 10.1371/journal.pone.0085678
    Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at


  • CovalentDock Cloud: a web server for automated covalent docking.
    Ouyang, Xuchang and Zhou, Shuo and Ge, Zemei and Li, Runtao and Kwoh, Chee Keong
    Nucleic acids research, 2013, 41(W1), W329-W332
    PMID: 23677616     doi: 10.1093/nar/gkt406
    Covalent binding is an important mechanism for many drugs to gain its function. We developed a computational algorithm to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online without any local installation and configuration. It provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis. This web server is free and open to all users at


  • Accessible high-throughput virtual screening molecular docking software for students and educators.
    Jacob, Reed B. and Andersen, Tim and McDougal, Owen M.
    PLoS computational biology, 2012, 8(5), e1002499
    PMID: 22693435     doi: 10.1371/journal.pcbi.1002499
    We survey low cost high- throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms.

  • Rosetta Ligand docking with flexible XML protocols.
    Lemmon, Gordon and Meiler, Jens
    Methods in molecular biology (Clifton, N.J.), 2012, 819, 143-155
    PMID: 22183535     doi: 10.1007/978-1-61779-465-0_10
    RosettaLigand is premiere software for predicting how a protein and a small molecule interact. Benchmark studies demonstrate that 70% of the top scoring RosettaLigand predicted interfaces are within 2{\AA} RMSD from the crystal structure [1]. The latest release of Rosetta ligand software includes many new features, such as (1) docking of multiple ligands simultaneously, (2) representing ligands as fragments for greater flexibility, (3) redesign of the interface during docking, and (4) an XML script based interface that gives the user full control of the ligand docking protocol.

  • idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach.
    Wang, Jui-Chih and Chu, Pei-Ying and Chen, Chung-Ming and Lin, Jung-Hsin
    Nucleic acids research, 2012, 40(Web Server issue), W393-9
    PMID: 22649057     doi: 10.1093/nar/gks496
    Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at


  • AADS - An Automated Active Site Identification, Docking, and Scoring Protocol for Protein Targets Based on Physicochemical Descriptors.
    Singh, Tanya and Biswas, D and Jayaram, B.
    Journal of chemical information and modeling, 2011, 51(10), 2515-2527
    PMID: 21877713     doi: 10.1021/ci200193z
    We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at .

  • Rosetta FlexPepDock web server-high resolution modeling of peptide-protein interactions
    London, Nir and Raveh, Barak and Cohen, Eyal and Fathi, Guy and Schueler-Furman, Ora
    Nucleic acids research, 2011, 39(Web Server issue), W249-W253
    PMID: 21622962     doi: 10.1093/nar/gkr431
    Peptide-protein interactions are among the most prevalent and important interactions in the cell, but a large fraction of those interactions lack detailed structural characterization. The Rosetta FlexPepDock web server ( provides an interface to a high-resolution peptide docking (refinement) protocol for the modeling of peptide-protein complexes, implemented within the Rosetta framework. Given a protein receptor structure and an approximate, possibly inaccurate model of the peptide within the receptor binding site, the FlexPepDock server refines the peptide to high resolution, allowing full flexibility to the peptide backbone and to all side chains. This protocol was extensively tested and benchmarked on a wide array of non-redundant peptide-protein complexes, and was proven effective when applied to peptide starting conformations within 5.5 angstrom backbone root mean square deviation from the native conformation. FlexPepDock has been applied to several systems that are mediated and regulated by peptide-protein interactions. This easy to use and general web server interface allows non-expert users to accurately model their specific peptide-protein interaction of interest.

  • SwissDock, a protein-small molecule docking web service based on EADock DSS.
    Grosdidier, Aurélien and Zoete, Vincent and Michielin, Olivier
    Nucleic acids research, 2011, 39(Web Server issue), W270-7
    PMID: 21624888     doi: 10.1093/nar/gkr366
    Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular 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 molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing 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 We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

  • SwissParam: a fast force field generation tool for small organic molecules.
    Zoete, Vincent and Cuendet, Michel A and Grosdidier, Aurélien and Michielin, Olivier
    Journal of computational chemistry, 2011, 32(11), 2359-2368
    PMID: 21541964     doi: 10.1002/jcc.21816
    The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at


  • FINDSITE: a threading-based approach to ligand homology modeling.
    Brylinski, Michal and Skolnick, Jeffrey
    PLoS computational biology, 2009, 5(6), e1000405
    PMID: 19503616     doi: 10.1371/journal.pcbi.1000405
    Ligand virtual screening is a widely used tool to assist in new pharmaceutical discovery. In practice, virtual screening approaches have a number of limitations, and the development of new methodologies is required. Previously, we showed that remotely related proteins identified by threading often share a common binding site occupied by chemically similar ligands. Here, we demonstrate that across an evolutionarily related, but distant family of proteins, the ligands that bind to the common binding site contain a set of strongly conserved anchor functional groups as well as a variable region that accounts for their binding specificity. Furthermore, the sequence and structure conservation of residues contacting the anchor functional groups is significantly higher than those contacting ligand variable regions. Exploiting these insights, we developed FINDSITE(LHM) that employs structural information extracted from weakly related proteins to perform rapid ligand docking by homology modeling. In large scale benchmarking, using the predicted anchor-binding mode and the crystal structure of the receptor, FINDSITE(LHM) outperforms classical docking approaches with an average ligand RMSD from native of approximately 2.5 A. For weakly homologous receptor protein models, using FINDSITE(LHM), the fraction of recovered binding residues and specific contacts is 0.66 (0.55) and 0.49 (0.38) for highly confident (all) targets, respectively. Finally, in virtual screening for HIV-1 protease inhibitors, using similarity to the ligand anchor region yields significantly improved enrichment factors. Thus, the rather accurate, computationally inexpensive FINDSITE(LHM) algorithm should be a useful approach to assist in the discovery of novel biopharmaceuticals.

  • Automated Docking Screens: A Feasibility Study
    Irwin, John J and Shoichet, Brian K and Mysinger, Michael M. and Huang, Niti and Colizzi, Francesco and Wassam, Pascal and Cao, Yiqun
    Journal of medicinal chemistry, 2009, 52(18), 5712-5720
    PMID: 19719084     doi: 10.1021/jm9006966
    Molecular docking is the most practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCK Blaster recapitulates the crystal ligand pose within 2 angstrom rmsd 50-60% of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5% of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5% of 100 property-matched decoys while also posing within 2 angstrom rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available at

  • [DockingServer: molecular docking calculations online].
    Hazai, Eszter and Kovács, Sándor and Demkó, László and Bikádi, Zsolt
    Acta pharmaceutica Hungarica, 2009, 79(1), 17-21
    PMID: 19526678    
    Over the last years, the use of bioinformatics tools such as molecular docking has become an essential part of research focused at prediction of the binding of small molecules to their target proteins. DockingServer offers a web-based, easy to use interface that handles all aspects of molecular docking from ligand and pro-tein set-up through results representation integrating a number of software frequently used in computational chemistry. While its user friendly interface enables docking calculation and results evaluation carried out by researchers coming from all fields of biochemistry, DockingServer also provides full control on the setting of specific parameters of ligand and protein set up and docking calculations for more advanced users. The application can be used for docking and analysis of single ligands as well as for high throughput docking of ligand libraries to target proteins. The use of "DockingServer" is illustrated by the formation of acetaminophene (paracetamol)-CYP2E1 complex.


  • PDTD: a web-accessible protein database for drug target identification
    Gao, Zhenting and Li, Honglin and Zhang, Hailei and Liu, Xiaofeng and Kang, Ling and Luo, Xiaomin and Zhu, Weiliang and Chen, Kaixian and Wang, Xicheng and Jiang, Hualiang
    Bmc Bioinformatics, 2008, 9, -
    PMID: 18282303     doi: 10.1186/1471-2105-9-104
    Background: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking), which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.Description: PDTD is a web-accessible protein database for in silico target identification. It currently contains > 1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of > 830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores.Conclusion: PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at


  • ParDOCK: An all atom energy based Monte Carlo docking protocol for protein-ligand complexes
    Gupta, A. and Gandhimathi, A. and Sharma, P. and Jayaram, B.
    Protein and peptide letters, 2007, 14(7), 632-646
    PMID: 17897088    
    We report here an all-atom energy based Monte Carlo docking procedure tested on a dataset of 226 protein-ligand complexes. Average root mean square deviation ( RMSD) from crystal conformation was observed to be similar to 0.53 angstrom. The correlation coefficient (r(2)) for the predicted binding free energies calculated using the docked structures against experimental binding affinities was 0.72. The docking protocol is web-enabled as a free software at


  • kinDOCK: a tool for comparative docking of protein kinase ligands.
    Martin, Laetitia and Catherinot, Vincent and Labesse, Gilles
    Nucleic acids research, 2006, 34(Web Server issue), W325-9
    PMID: 16845019     doi: 10.1093/nar/gkl211
    KinDOCK is a new web server for the analysis of ATP-binding sites of protein kinases. This characterization is based on the docking of ligands already co-crystallized with other protein kinases. A structural library of protein kinase-ligand complexes has been extracted from the Protein Data Bank (PDB). This library can provide both potential ligands and their putative binding orientation for a given protein kinase. After protein-protein structural superposition, the ligands are transferred from the template complexes to the target protein kinase. The resulting complexes are evaluated using the program SCORE to compute a theoretical affinity. They can be dynamically visualized to allow a rapid mapping of important steric clashes and potential substitutions relevant for specificity and affinity. These characteristics allow a quick characterization of protein kinase active sites including conformation changes potentially required to accommodate particular ligands. Additionally, promising pharmacophores can be identified in the focussed library. These features will help to rationalize or optimize virtual screening (VS) on larger chemical compound libraries. The server and its documentation are freely available at

  • TarFisDock: a web server for identifying drug targets with docking approach
    Li, Honglin and Gao, Zhenting and Kang, Ling and Zhang, Hailei and Yang, Kun and Yu, Kunqian and Luo, Xiaomin and Zhu, Weiliang and Chen, Kaixian and Shen, Jianhua and Wang, Xicheng and Jiang, Hualiang
    Nucleic acids research, 2006, 34(Web Server issue), W219-W224
    PMID: 16844997     doi: 10.1093/nar/gkl114
    TarFisDock is a web-based tool for automating the procedure of searching for small molecule-protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand protein docking program. In contrast to conventional ligand-protein docking, reverse ligand-protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand-protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at


  • MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm
    Chang, DTH and Oyang, YJ and Lin, JH
    Nucleic acids research, 2005, 33(Web Server issue), W233-W238
    PMID: 15991337     doi: 10.1093/nar/gki586
    The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drug's adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding-modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at and

  • PatchDock and SymmDock: servers for rigid and symmetric docking.
    Schneidman-Duhovny, Dina and Inbar, Yuval and Nussinov, Ruth and Wolfson, Haim J
    Nucleic acids research, 2005, 33(Web Server issue), W363-7
    PMID: 15980490     doi: 10.1093/nar/gki481
    Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein-protein and protein-small molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit. The inputs to the servers are either protein PDB codes or uploaded protein structures. The services are available at The methods behind the servers are very efficient, allowing large-scale docking experiments.