Cited by 763Computational Drug Discovery MethodsCholinesterase and Neurodegenerative DiseasesSynthesis and Characterization of Heterocyclic Compounds

The DOI matches its Crossref record; 154 of the 156 references it cites were found in a registry or an index.

Role of computer-aided drug design in modern drug discovery

Macalino, Stephani Joy Y., Gosu, Vijayakumar, Hong, Sunhye, Choi, Sun·2015·Archives of Pharmacal Research

DOI 10.1007/s12272-015-0640-5

Open this paper in CoChat — free account

Abstract

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Integrity signals

Registry

Crossref · 10.1007/s12272-015-0640-5

Cites

154 of 154 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 26,294
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M., Smith, J., Kasson, P., van der Spoel, D., et al. (2013) · Bioinform. · doi:10.1093/bioinformatics/btt055 · Cited by 7,255
The price of innovation: new estimates of drug development costs
DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00126-1 · Cited by 4,265
New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Application of a Theory of Enzyme Specificity to Protein Synthesis.
Koshland, D. (1958) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.44.2.98 · Cited by 2,140
LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters
Wolber, G., & Langer, T. (2004) · Journal of Chemical Information and Modeling · doi:10.1021/ci049885e · Cited by 1,989
A Critical Assessment of Docking Programs and Scoring Functions
Warren, G. L., Andrews, C. W., Capelli, A., Clarke, B., LaLonde, J. M., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050362n · Cited by 1,889
Identifying and Characterizing Binding Sites and Assessing Druggability
Halgren, T. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800324m · Cited by 1,808
CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues
Dundas, J., Ouyang, Z., Tseng, J., Binkowski, A., Turpaz, Y., & Liang, J. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl282 · Cited by 1,725
Fpocket: An open source platform for ligand pocket detection
Guilloux, V. L., Schmidtke, P., & Tufféry, P. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-168 · Cited by 1,724
A ‘Rule of Three’ for fragment-based lead discovery?
Congreve, M., Carr, R. A. E., Murray, C. W., & Jhoti, H. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(03)02831-9 · Cited by 1,415
Impact of high-throughput screening in biomedical research
Macarron, R., Banks, M. N., Bojanic, D., Burns, D. J., Cirovic, D. A., Garyantes, T., Green, D. V. S., Hertzberg, R. P., Janzen, W. P., Paslay, J. W., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3368 · Cited by 1,364
Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances
Lionta, E., Spyrou, G. M., Vassilatis, D. K., & Cournia, Z. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026614666140929124445 · Cited by 1,256
PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results
Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9087-6 · Cited by 1,207
Target identification and mechanism of action in chemical biology and drug discovery
Schenone, M., Dančík, V., Wagner, B. K., & Clemons, P. A. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1199 · Cited by 1,147
Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites
Laurie, A. T. R., & Jackson, R. M. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti315 · Cited by 1,049
The Process of Structure-Based Drug Design
Anderson, A. C. (2003) · Chemistry & Biology · doi:10.1016/j.chembiol.2003.09.002 · Cited by 965
Integration of virtual and high-throughput screening
Bajorath, J. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd941 · Cited by 935
Comparative evaluation of 11 scoring functions for molecular docking.
Wang, R., Lu, Y., & Wang, S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0203783 · Cited by 849
The Organic Chemistry of Drug Design and Drug Action
(1992) · Elsevier eBooks · doi:10.1016/c2009-0-22221-5 · Cited by 716
Virtual Screening Strategies in Drug Discovery: A Critical Review
Lavecchia, A., & Giovanni, C. D. (2013) · Current Medicinal Chemistry · doi:10.2174/09298673113209990001 · Cited by 712
Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding
Abel, R., Young, T. K., Farid, R., Berne, B. J., & Friesner, R. A. (2008) · Journal of the American Chemical Society · doi:10.1021/ja0771033 · Cited by 687
PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching
Dixon, S. L., Smondyrev, A. M., & Rao, S. N. (2006) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2006.00384.x · Cited by 676
Diverse, High-Quality Test Set for the Validation of Protein−Ligand Docking Performance
Hartshorn, M. J., Verdonk, M. L., Chessari, G., Brewerton, S. C., Mooij, W. T. M., Mortenson, P. N., & Murray, C. W. (2007) · Journal of Medicinal Chemistry · doi:10.1021/jm061277y · Cited by 639
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012) · The AAPS Journal · doi:10.1208/s12248-012-9322-0 · Cited by 587
Fast prediction and visualization of protein binding pockets with PASS
Brady, G. P., & Stouten, P. F. W. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008124202956 · Cited by 532
Comparative Assessment of Scoring Functions on a Diverse Test Set
Cheng, T., Li, X., Li, Y., Liu, Z., & Wang, R. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9000053 · Cited by 525
Lead discovery using molecular docking
Shoichet, B. K., McGovern, S. L., Wei, B. Q., & Irwin, J. J. (2002) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(02)00339-3 · Cited by 517
Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.
Huang, S.-Y., Grinter, S. Z., & Zou, X. (2010) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp00151a · Cited by 508 · cited 1×
“Frequently used scoring functions, as reported in several papers (Huang et al. 2010; Cheng et al. 2012; Lavecchia and Di Giovanni 2013; Liu and Wang 2015), are divided into three categories: (a) Force ﬁeld - based functions calculate binding afﬁnity derived from physical atomic contacts in the…”
Background
MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist
Cruciani, G., Carosati, E., Boeck, B. D., Ethirajulu, K., Mackie, C. E., Howe, T. J., & Vianello, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050529c · Cited by 497
Flexible ligand docking to multiple receptor conformations: a practical alternative
Totrov, M., & Abagyan, R. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.01.004 · Cited by 497
Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1B
Doman, T. N., McGovern, S. L., Witherbee, B. J., Kasten, T. P., Kurumbail, R. G., Stallings, W. C., Connolly, D. T., & Shoichet, B. K. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm010548w · Cited by 473
Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure
Capra, J. A., Laskowski, R. A., Thornton, J. M., Singh, M., & Funkhouser, T. A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000585 · Cited by 469
Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy
Cross, J. B., Thompson, D. C., Rai, B. K., Baber, J. C., Fan, K. Y., Hu, Y., & Humblet, C. C. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900056c · Cited by 466
LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation.
Huang, B., & Schroeder, M. (2006) · BMC Structural Biology · doi:10.1186/1472-6807-6-19 · Cited by 465
Recognizing Pitfalls in Virtual Screening: A Critical Review
Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., & Agrafiotis, D. K. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200528d · Cited by 438
RosettaLigand Docking with Full Ligand and Receptor Flexibility
Davis, I., & Baker, D. A. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.11.010 · Cited by 438
A detergent-based assay for the detection of promiscuous inhibitors
Feng, B. Y., & Shoichet, B. K. (2006) · Nature Protocols · doi:10.1038/nprot.2006.77 · Cited by 432
“Soft docking”: Matching of molecular surface cubes
Jiang, F., & Kim, S. (1991) · Journal of Molecular Biology · doi:10.1016/0022-2836(91)90859-5 · Cited by 417
MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories
Schmidtke, P., Bidon‐Chanal, A., Luque, F. J., & Barril, X. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr550 · Cited by 398
Recent advances in computer-aided drug design
Song, C. M., Lim, S. J., & Tong, J. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp023 · Cited by 349
Preclinical safety evaluation of biotechnology-derived pharmaceuticals
Cavagnaro, J. A. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd822 · Cited by 340
Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction
Zhang, Z., Li, Y., Lin, B., Schroeder, M., & Huang, B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr331 · Cited by 332
SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism
Rydberg, P., Gloriam, D. E. I., Zaretzki, J. M., Breneman, C. M., & Olsen, L. (2010) · ACS Medicinal Chemistry Letters · doi:10.1021/ml100016x · Cited by 331
An improved relaxed complex scheme for receptor flexibility in computer-aided drug design
Amaro, R. E., Baron, R., & McCammon, J. A. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9159-2 · Cited by 321
Structure-based drug design to augment hit discovery
Kalyaanamoorthy, S., & Chen, Y. P. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.07.006 · Cited by 311
Classification of Current Scoring Functions
Liu, J., & Wang, R. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500731a · Cited by 311
The Organic Chemistry of Drug Design and Drug Action
Cooper, M. E. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03199-x · Cited by 294
Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context
Houston, D. R., & Walkinshaw, M. D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300399w · Cited by 274
FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects
Lagorce, D., Spérandio, O., Galons, H., Miteva, M. A., & Villoutreix, B. O. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-396 · Cited by 272
Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design
Grinter, S. Z., & Zou, X. (2014) · Molecules · doi:10.3390/molecules190710150 · Cited by 271
Development of a Virtual Screening Method for Identification of “Frequent Hitters” in Compound Libraries
Roche, O., Schneider, P., Zuegge, J., Guba, W., Kansy, M., Alanine, A. I., Bleicher, K., Danel, F., Gutknecht, E.-M., Rogers‐Evans, M., et al. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm010934d · Cited by 249
Effect of Detergent on “Promiscuous” Inhibitors
Ryan, A. J., Gray, N. M., Lowe, P. N., & Chung, C. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0340896 · Cited by 231
Allosite: a method for predicting allosteric sites
Huang, W., Lu, S., Huang, Z., Liu, X., Mou, L., Luo, Y., Zhao, Y., Liu, Y., Chen, Z., Hou, T., et al. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt399 · Cited by 227
Docking Ligands into Flexible and Solvated Macromolecules. 1. Development and Validation of FITTED 1.0
Corbeil, C. R., Englebienne, P., & Moitessier, N. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6002637 · Cited by 225
Combination of ligand- and structure-based methods in virtual screening
Drwal, M. N., & Griffith, R. (2013) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2013.02.002 · Cited by 214
PepSite: prediction of peptide-binding sites from protein surfaces
Trabuco, L. G., Lise, S., Petsalaki, E., & Russell, R. B. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks398 · Cited by 211
Topomer CoMFA: A Design Methodology for Rapid Lead Optimization
CRAMER, R. D. I. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020194o · Cited by 198
Target Identification in Chemical Genetics
Burdine, L. J., & Kodadek, T. J. (2004) · Chemistry & Biology · doi:10.1016/j.chembiol.2004.05.001 · Cited by 191
Do Structurally Similar Ligands Bind in a Similar Fashion?
Boström, J., Hogner, A., & Schmitt, S. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060167o · Cited by 184
The holistic integration of virtual screening in drug discovery
Tanrikulu, Y., Krüger, B., & Proschak, E. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.01.007 · Cited by 184
Ensemble-Based Virtual Screening Reveals Potential Novel Antiviral Compounds for Avian Influenza Neuraminidase
Cheng, L. S., Amaro, R. E., Xu, D., Li, W. W., ARZBERGER, P. W., & McCammon, J. A. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm8001197 · Cited by 184
Methods for the Prediction of Protein-Ligand Binding Sites for Structure-Based Drug Design and Virtual Ligand Screening.
Publisher, B. S. P. B. S. (2006) · Current Protein and Peptide Science · doi:10.2174/138920306778559386 · Cited by 178
Potential and Limitations of Ensemble Docking
Korb, O., Olsson, T. S. G., Bowden, S. J., Hall, R. J., Verdonk, M. L., Liebeschuetz, J. W., & Cole, J. C. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005934 · Cited by 175
Integrating Structure-Based and Ligand-Based Approaches for Computational Drug Design
Wilson, G. L., & Lill, M. A. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.18 · Cited by 167
Structure-Based Drug Design Strategies in Medicinal Chemistry
Andricopulo, A. D., Salum, L. B., & Abraham, D. J. (2009) · Current Topics in Medicinal Chemistry · doi:10.2174/156802609789207127 · Cited by 162
Hierarchical virtual screening approaches in small molecule drug discovery
Kumar, A., & Zhang, K. Y. J. (2014) · Methods · doi:10.1016/j.ymeth.2014.07.007 · Cited by 161
Exploring the role of receptor flexibility in structure-based drug discovery
Feixas, F., Lindert, S., Sinko, W., & McCammon, J. A. (2014) · Biophysical Chemistry · doi:10.1016/j.bpc.2013.10.007 · Cited by 156
Ligand−Protein Docking with Water Molecules
Roberts, B. C., & Mancera, R. L. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700285e · Cited by 155
PARS: a web server for the prediction of Protein Allosteric and Regulatory Sites
Panjkovich, A., & Daura, X. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu002 · Cited by 139
Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling
Vuorinen, A., & Schuster, D. (2014) · Methods · doi:10.1016/j.ymeth.2014.10.013 · Cited by 132
Protein–protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein
Betzi, S., Restouin, A., Opi, S., Arold, S. T., Parrot, I., Guerlesquin, F., Morelli, X., & Collette, Y. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707130104 · Cited by 129
idock: A multithreaded virtual screening tool for flexible ligand docking
Li, H., Leung, K., & Wong, M. (2012) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2012.6217214 · Cited by 128
How far can virtual screening take us in drug discovery?
Kar, S., & Roy, K. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.761204 · Cited by 128
Assessment of the blood–brain barrier in CNS drug discovery
Jeffrey, P., & Summerfield, S. G. (2009) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.07.033 · Cited by 125
APoc: large-scale identification of similar protein pockets
Gao, M., & Skolnick, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt024 · Cited by 125
istar: A Web Platform for Large-Scale Protein-Ligand Docking
Li, H., Leung, K., Ballester, P. J., & Wong, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085678 · Cited by 123
DrugScore Meets CoMFA: Adaptation of Fields for Molecular Comparison (AFMoC) or How to Tailor Knowledge-Based Pair-Potentials to a Particular Protein
Gohlke, H., & Klebe, G. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020808p · Cited by 122
FLIPDock: Docking flexible ligands into flexible receptors
Zhao, Y., & Sanner, M. F. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21423 · Cited by 121
Rational design and validation of a Tip60 histone acetyltransferase inhibitor
Gao, C., Bourke, E., Scobie, M., Famme, M. A., Koolmeister, T., Helleday, T., Eriksson, L. A., Lowndes, N. F., & Brown, J. A. L. (2014) · Scientific Reports · doi:10.1038/srep05372 · Cited by 118
Detection of peptide‐binding sites on protein surfaces: The first step toward the modeling and targeting of peptide‐mediated interactions
Lavi, A., Ngan, C. H., Movshovitz‐Attias, D., Bohnuud, T., Yueh, C., Beglov, D., Schueler‐Furman, O., & Kozakov, D. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24422 · Cited by 118
Outstanding challenges in protein–ligand docking and structure‐based virtual screening
Waszkowycz, B., Clark, D. E., & Gancia, E. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.18 · Cited by 117
Docking Ligands into Flexible and Solvated Macromolecules. 3. Impact of Input Ligand Conformation, Protein Flexibility, and Water Molecules on the Accuracy of Docking Programs
Corbeil, C. R., & Moitessier, N. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8004176 · Cited by 111
Flexibility and binding affinity in protein–ligand, protein–protein and multi-component protein interactions: limitations of current computational approaches
Tufféry, P., & Derreumaux, P. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0584 · Cited by 109
RosettaBackrub--a web server for flexible backbone protein structure modeling and design
Lauck, F., Smith, C. A., Friedland, G. F., Humphris, E. L., & Kortemme, T. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq369 · Cited by 108
Emerging Methods for Ensemble-Based Virtual Screening
Amaro, R. E., & Li, W. W. (2010) · Current Topics in Medicinal Chemistry · doi:10.2174/156802610790232279 · Cited by 106
Computational Approaches for Drug Discovery
Hung, C., & Chen, C. (2014) · Drug Development Research · doi:10.1002/ddr.21222 · Cited by 105
Recent Advances in Multidimensional QSAR (4D-6D): A Critical Review
Damale, M. G., Harke, S. N., Khan, F. A. K., Shinde, D. B., & Sangshetti, J. N. (2014) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/13895575113136660104 · Cited by 104
A phase II study of the oral factor Xa inhibitor LY517717 for the prevention of venous thromboembolism after hip or knee replacement
Agnelli, G., Haas, S. K., Ginsberg, J. S., Krueger, K., Dmitrienko, A. A., & Brandt, J. T. (2007) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2007.02436.x · Cited by 103
SPACER: server for predicting allosteric communication and effects of regulation
Goncearenco, A., Mitternacht, S., Yong, T., Eisenhaber, B., Eisenhaber, F., & Berezovsky, I. N. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt460 · Cited by 102
Discovery of Novel Human Histamine H4 Receptor Ligands by Large-Scale Structure-Based Virtual Screening
Kiss, R., Kiss, B., Könczöl, Á., Szalai, F., Jelinek, I., László, V., Noszál, B., Falus, A., & Keserű, G. M. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm7014777 · Cited by 98
PEP-SiteFinder: a tool for the blind identification of peptide binding sites on protein surfaces
Saladin, A., Rey, J., Thévenet, P., Zacharias, M., Moroy, G., & Tufféry, P. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku404 · Cited by 97
The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections
Lagorce, D., Maupetit, J., Baell, J. B., Spérandio, O., Tufféry, P., Miteva, M. A., Galons, H., & Villoutreix, B. O. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr333 · Cited by 95
Identifying targets for drug discovery using bioinformatics
Chen, Y. P., & Chen, F. (2008) · Expert Opinion on Therapeutic Targets · doi:10.1517/14728222.12.4.383 · Cited by 94
De novodesign: balancing novelty and confined chemical space
Kutchukian, P. S., & Shakhnovich, E. I. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.497534 · Cited by 93
Ligand−Protein Cross-Docking with Water Molecules
Thilagavathi, R., & Mancera, R. L. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900345h · Cited by 87
PRO_SELECT: Combining Structure-Based Drug Design and Array-Based Chemistry for Rapid Lead Discovery. 2. The Development of a Series of Highly Potent and Selective Factor Xa Inhibitors
Liebeschuetz, J. W., Jones, S. D., Morgan, P. J., Murray, C. W., Rimmer, A. D., Roscoe, J. M. E., Waszkowycz, B., Welsh, P. M., Wylie, W. A., Young, S. C., et al. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm010944e · Cited by 86
Docking Ligands into Flexible and Solvated Macromolecules. 4. Are Popular Scoring Functions Accurate for this Class of Proteins?
Englebienne, P., & Moitessier, N. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8004308 · Cited by 85
A Critical Comparative Assessment of Predictions of Protein-Binding Sites for Biologically Relevant Organic Compounds
Chen, K., Mizianty, M. J., Gao, J., & Kurgan, L. (2011) · Structure · doi:10.1016/j.str.2011.02.015 · Cited by 82
Molecular Docking with Ligand Attached Water Molecules
Lie, M. A., Thomsen, R., Pedersen, C. N. S., Schiøtt, B., & Christensen, M. H. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100510m · Cited by 80
A Virtual Screening Approach to Finding Novel and Potent Antagonists at the Melanin-Concentrating Hormone 1 Receptor
Clark, D. E., Higgs, C. J., Wren, S. P., Dyke, H. J., Wong, M. H. L., Norman, D., Lockey, P. M., & Roach, A. G. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm040762v · Cited by 79
Structural Insight into Tetrameric hTRPV1 from Homology Modeling, Molecular Docking, Molecular Dynamics Simulation, Virtual Screening, and Bioassay Validations
Feng, Z., Pearce, L. V., Xu, X., Yang, X., Yang, P., Blumberg, P. M., & Xie, X. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci5007189 · Cited by 78
Accounting for Induced-Fit Effects in Docking: What is Possible and What is Not?
Sotriffer, C. (2011) · Current Topics in Medicinal Chemistry · doi:10.2174/156802611794863544 · Cited by 76
AcquaAlta: A Directional Approach to the Solvation of Ligand–Protein Complexes
Rossato, G., Ernst, B., Vedani, A., & Smieško, M. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200150p · Cited by 75
eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands
Bryliński, M., & Feinstein, W. P. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9663-5 · Cited by 74
Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets
Xu, W., Lucke, A. J., & Fairlie, D. P. (2015) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2015.01.009 · Cited by 74
Comparative Virtual and Experimental High-Throughput Screening for Glycogen Synthase Kinase-3β Inhibitors
Polgár, T., Baki, A., Szendrei, G. I., & Keserű, G. M. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050504d · Cited by 73
Automatic clustering of docking poses in virtual screening process using self-organizing map
Bouvier, G., Evrard‐Todeschi, N., Girault, J., & Bertho, G. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp623 · Cited by 73
MCPath: Monte Carlo path generation approach to predict likely allosteric pathways and functional residues
Kaya, C., Armutlulu, A., Ekesan, Ş., & Haliloǧlu, T. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt284 · Cited by 72
Molecular Modeling Studies of the Novel Inhibitors of DNA Methyltransferases SGI-1027 and CBC12: Implications for the Mechanism of Inhibition of DNMTs
Yoo, J., Choi, S., & Medina‐Franco, J. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062152 · Cited by 68
Self-citation
Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap.
Yang, Y., Lightstone, F. C., & Wong, S. E. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.749853 · Cited by 65
Sequence-Derived Three-Dimensional Pharmacophore Models for G-Protein-Coupled Receptors and Their Application in Virtual Screening
Klabunde, T., Giegerich, C., & Evers, A. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm9001346 · Cited by 63
Docking Ligands into Flexible and Solvated Macromolecules. 2. Development and Application of F itted 1.5 to the Virtual Screening of Potential HCV Polymerase Inhibitors
Corbeil, C. R., Englebienne, P., Yannopoulos, C. G., Chan, L. C., Das, S. K., Bilimoria, D., L’Heureux, L., & Moitessier, N. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700398h · Cited by 61
ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery
Athanasiadis, E. I., Cournia, Z., & Spyrou, G. M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts551 · Cited by 61
3D-QSAR Approaches in Drug Design: Perspectives to Generate Reliable CoMFA Models
Melo-Filho, C. C., Braga, R. C., & Andrade, C. H. (2014) · Current Computer - Aided Drug Design · doi:10.2174/1573409910666140410111043 · Cited by 60
Conversion of yeast phosphoglycerate kinase into amyloid-like structure
Damaschun, G., Damaschun, H., Fabian, H., Gast, K., Kr�ber, R., Wieske, M., & Zirwer, D. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(20000515)39:3 · Cited by 59
Computer-Aided Drug Design: Integration of Structure-Based and Ligand-Based Approaches in Drug Design
Prathipati, P., Dixit, A., & Saxena, A. K. (2007) · Current Computer - Aided Drug Design · doi:10.2174/157340907780809516 · Cited by 57
Automated QSPR through Competitive Workflow
Cartmell, J., Enoch, S. J., Krstajić, D., & Leahy, D. E. (2005) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-005-9029-8 · Cited by 53
Docking Ligands into Flexible and Solvated Macromolecules. 6. Development and Application to the Docking of HDACs and other Zinc Metalloenzymes Inhibitors
Pottel, J., Therrien, E., Gleason, J. L., & Moitessier, N. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400550m · Cited by 48 · cited 1×
“Existing methods that incorporate partial protein ﬂexibility include Glide, RosettaLigand (Davis and Baker 2009), FLIPDock (Zhao and Sanner 2007), and FITTED (Corbeil et al. 2007, 2008; Corbeil and Moitessier 2009; Englebienne and Moitessier 2009a, b; Pottel et al. 2014; Therrien et al. 2014).”
Method
Incorporating Backbone Flexibility in MedusaDock Improves Ligand-Binding Pose Prediction in the CSAR2011 Docking Benchmark
Ding, F., & Dokholyan, N. V. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300478y · Cited by 47
Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands
Datar, P. A., Khedkar, S. A., Malde, A. K., & Coutinho, E. C. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9051-5 · Cited by 46
Introducing Spectral Structure Activity Relationship (S-SAR) Analysis. Application to Ecotoxicology
Putz, M. V., & Putz, A.-M. (2007) · International Journal of Molecular Sciences · doi:10.3390/i8050363 · Cited by 46
Integration of Virtual and High Throughput Screening in Lead Discovery Settings
Polgár, T., & Keserű, G. M. (2011) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620711797537148 · Cited by 46
Docking Ligands into Flexible and Solvated Macromolecules. 7. Impact of Protein Flexibility and Water Molecules on Docking-Based Virtual Screening Accuracy
Therrien, É., Weill, N., Tomberg, A., Corbeil, C. R., Lee, D., & Moitessier, N. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500299h · Cited by 45
Docking Ligands into Flexible and Solvated Macromolecules. 5. Force-Field-Based Prediction of Binding Affinities of Ligands to Proteins
Englebienne, P., & Moitessier, N. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900251k · Cited by 42
POOL server: machine learning application for functional site prediction in proteins
Somarowthu, S., & Ondrechen, M. J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts321 · Cited by 42
Improving the Scoring of Protein–Ligand Binding Affinity by Including the Effects of Structural Water and Electronic Polarization
Liu, J., He, X. D., & Zhang, J. Z. H. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400067c · Cited by 41
BP-Dock: A Flexible Docking Scheme for Exploring Protein–Ligand Interactions Based on Unbound Structures
Bolia, A., Gerek, Z. N., & Ozkan, S. B. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4004927 · Cited by 38
SiteComp: a server for ligand binding site analysis in protein structures
Lin, Y., Yoo, S., & Sánchez, R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts095 · Cited by 38
The Discovery of Kv1.5 Blockers as a Case Study for the Application of Virtual Screening Approaches
Pirard, B., Brendel, J., & Peukert, S. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0400011 · Cited by 37
Scaffold‐Hopping Cascade Yields Potent Inhibitors of 5‐Lipoxygenase
Hofmann, B., Franke, L., Proschak, E., Tanrikulu, Y., Schneider, P., Steinhilber, D., & Schneider, G. (2008) · ChemMedChem · doi:10.1002/cmdc.200800153 · Cited by 34
Learning from PAINful lessons
Erlanson, D. A. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00294 · Cited by 33
McQSAR: A Multiconformational Quantitative Structure−Activity Relationship Engine Driven by Genetic Algorithms
Vainio, M. J., & Johnson, M. S. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0501847 · Cited by 30
Quantitative Structure–Activity Relationship Models That Stand the Test of Time
Davis, A. M., & Wood, D. (2013) · Molecular Pharmaceutics · doi:10.1021/mp300466n · Cited by 26
How to Efficiently Include Receptor Flexibility During Computational Docking
May, A., Sieker, F., & Zacharias, M. (2008) · Current Computer - Aided Drug Design · doi:10.2174/157340908784533265 · Cited by 24
The design of phenylglycine containing benzamidine carboxamides as potent and selective inhibitors of factor Xa
Jones, S. D., Liebeschuetz, J. W., Morgan, P. J., Murray, C. W., Rimmer, A. D., Roscoe, J. M. E., Waszkowycz, B., Welsh, P. M., Wylie, W. A., Young, S. C., et al. (2001) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(01)00042-7 · Cited by 22
Strategies for the generation, validation and application ofin silicoADMET models in lead generation and optimization
Gleeson, M. P., & Montanari, D. (2012) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2012.711317 · Cited by 22
Impact of Computational Structure-based Methods on Drug Discovery
Reynolds, C. H. (2014) · Current Pharmaceutical Design · doi:10.2174/138161282020140528105532 · Cited by 22
3D-QSAR studies of heterocyclic quinones with inhibitory activity on vascular smooth muscle cell proliferation using pharmacophore-based alignment
Ryu, C., Lee, Y., Park, S., You, H., Lee, R.-Y., Lee, S.-Y., & Choi, S. (2008) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2008.09.062 · Cited by 17
Self-citation
Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins
Doppelt‐Azeroual, O., Delfaud, F., Moriaud, F., & de Brevern, A. G. (2010) · Protein Science · doi:10.1002/pro.364 · Cited by 15
High-throughput screening and structure-based approaches to hit discovery: is there a clear winner?
Jhoti, H., Rees, S., & Solari, R. C. E. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.857654 · Cited by 15
Combining 2D and 3D in silico methods for rapid selection of potential PDE5 inhibitors from multimillion compounds’ repositories: biological evaluation
Tömöri, T., Hajdú, I., Barna, L., Lőrincz, Z., Cseh, S., & Dormán, G. (2011) · Molecular Diversity · doi:10.1007/s11030-011-9335-0 · Cited by 14
Strategies for generating less toxic P-selectin inhibitors: Pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits
Ananthula, R. S., Ravikumar, M., Pramod, A. B., Kishore, M., & Mahmood, S. (2008) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2008.09.007 · Cited by 13
Flashback Forward: Reaction-Driven De Novo Design of Bioactive Compounds
Schneider, G., & Rodrigues, T. (2013) · Synlett · doi:10.1055/s-0033-1340216 · Cited by 11
Multistep Virtual Screening for Rapid and Efficient Identification of Non‐Nucleoside Bacterial Thymidine Kinase Inhibitors
Zander, J., Hartenfeller, M., Hähnke, V. D., Proschak, E., Besier, S. M., Wichelhaus, T. A., & Schneider, G. (2010) · Chemistry - A European Journal · doi:10.1002/chem.201001347 · Cited by 8
Review of the Target-Specific Oral Anticoagulants in Development for the Treatment and Prevention of Venous Thromboembolism
Devabhakthuni, S., Yoon, C. H., & Pincus, K. J. (2015) · Journal of Pharmacy Practice · doi:10.1177/0897190014568388 · Cited by 2
3D‐QSAR studies of cytotoxic heterocyclic quinones using calculated reduction potential
Lee, Y., Kim, S., Rhee, H., Doh, K., Park, J., Lee, C. O., Choi, S., & Choo, H. P. (2009) · Drug Development Research · doi:10.1002/ddr.20320 · Cited by 1
Self-citation

Sorted by citations

Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,537
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
Recent Developments on Therapeutic and Diagnostic Approaches for COVID-19
Majumder, J., & Minko, T. (2021) · The AAPS Journal · doi:10.1208/s12248-020-00532-2 · Cited by 531
Computational Approaches in Preclinical Studies on Drug Discovery and Development
Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X., Liang, X., Tan, M., & Huang, Z. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00726 · Cited by 494
Chalcone Derivatives: Promising Starting Points for Drug Design
Gomes, M. D. N., Muratov, E. N., Pereira, M., Peixoto, J. D. C., Rosseto, L. P., Cravo, P., Andrade, C. H., & Neves, B. J. (2017) · Molecules · doi:10.3390/molecules22081210 · Cited by 440

See all 763

Authors: Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S.
Affiliations: Ewha Womans University (KR)
Corresponding author: Choi, Sun
ORCID: Macalino, Stephani Joy Y.
Gosu, Vijayakumar
Choi, Sun
Venue: Archives of Pharmacal Research
Publisher: Springer Science and Business Media LLC
Published: 2015-07-25
Volume: 38
Issue: 9
Pages: 1686-1701
ISSN: 0253-6269 (ISSN-L)
1976-3786
DOI: 10.1007/s12272-015-0640-5
OpenAlex: W1854810044
Cited by: 762
References: 156
Citations by year: 2026: 65 · 2025: 80 · 2024: 109 · 2023: 146 · 2022: 105 · 2021: 96 · 2020: 60 · 2019: 41 · 2018: 31 · 2017: 15 · 2016: 10 · 2015: 2
FWCI: 2.08
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Archives of Pharmacal Research (publishedVersion)
PubMed (publishedVersion)
Funders: The Ministry of Science, ICT & Future Planning (MSIP) and National Research Foundation of Korea (2011-0028885)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Health Sciences › Medicine › Pharmacology (medical) › Cholinesterase and Neurodegenerative Diseases
Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
Keywords: computer-aided drug design, drug discovery, structure-based drug design, ligand-based drug design, virtual screening, chemical biology, lead optimization, rational drug design
MeSH terms: Animals, Pharmaceutical Preparations, Humans, Drug Design, Computer-Aided Design, Protein Structure, Secondary, Protein Structure, Tertiary, Quantitative Structure-Activity Relationship, Drug Discovery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Role of computer-aided drug design in modern drug discovery

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks