Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.
Open accessAbstract
The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking. The commonly-used assessment criteria and publicly available protein-ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Scoring functions are a straightforward and fast strategy despite limited accuracy, remaining the main alternative to be applied in VS experiments.
“In this context, scoring functions emerge as a straightforward and fast strategy despite limited accuracy, remaining as the main alternative to be applied in VS experiments (Huang et al., 2010).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Huang et al., 2010
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
The Cambridge Structural Database: a quarter of a million crystal structures and rising
Allen, F. H. (2002) · Acta Crystallographica Section B Structural Science · doi:10.1107/s0108768102003890 · Cited by 10,317 · see citing works
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057 · see citing works
Electrostatics of nanosystems: Application to microtubules and the ribosome
Baker, N., Sept, D., Joseph, S., Holst, M., & McCammon, J. A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.181342398 · Cited by 7,125 · see citing works
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763 · see citing works
A new force field for molecular mechanical simulation of nucleic acids and proteins
Weiner, S. J., Kollman, P. A., Case, D. A., Singh, U. C., Ghio, C., Alagona, G., Profeta, S., & Weiner, P. K. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00315a051 · Cited by 4,641 · see citing works
Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges
Gasteiger, J., & Marsili, M. (1980) · Tetrahedron · doi:10.1016/0040-4020(80)80168-2 · Cited by 4,157 · see citing works
Semianalytical treatment of solvation for molecular mechanics and dynamics
Still, W. C., Tempczyk, A., Hawley, R. C., & Hendrickson, T. F. (1990) · Journal of the American Chemical Society · doi:10.1021/ja00172a038 · Cited by 3,653 · see citing works
An all atom force field for simulations of proteins and nucleic acids
Weiner, S. J., Kollman, P. A., Nguyen, D. T., & Case, D. A. (1986) · Journal of Computational Chemistry · doi:10.1002/jcc.540070216 · Cited by 3,440 · see citing works
A Fast Flexible Docking Method using an Incremental Construction Algorithm
Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0477 · Cited by 2,836 · see citing works
A semiempirical free energy force field with charge‐based desolvation
Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20634 · Cited by 2,246 · see citing works
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,145 · see citing works
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 1,840 · see citing works
Eldridge, M., Murray, C. W., Auton, T. R., Paolini, G. V., & Mee, R. P. (1997) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007996124545 · Cited by 1,692 · see citing works
Abagyan, R., Totrov, M., & Kuznetsov, D. (1994) · Journal of Computational Chemistry · doi:10.1002/jcc.540150503 · Cited by 1,667 · see citing works
Signal detection theory and ROC analysis
Egan, J. P. (1975) · Academic Press eBooks · Cited by 1,656 · see citing works
Miyazawa, S., & Jernigan, R. L. (1985) · Macromolecules · doi:10.1021/ma00145a039 · Cited by 1,588 · see citing works
Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine
Jain, A. N. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020406h · Cited by 1,351 · see citing works
Benchmarking sets for molecular docking.
Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318 · see citing works
Molecular Dynamics: Survey of Methods for Simulating the Activity of Proteins
Adcock, S. A., & McCammon, J. A. (2006) · Chemical Reviews · doi:10.1021/cr040426m · Cited by 1,279 · see citing works
DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
Ewing, T. J. A., Makino, S., Skillman, A. G., & Kuntz, I. D. (2001) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1011115820450 · Cited by 1,188 · see citing works
Wang, R., Lai, L., & Wang, S. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1016357811882 · Cited by 1,185 · see citing works
Knowledge-based scoring function to predict protein-ligand interactions
Gohlke, H., Hendlich, M., & Klebe, G. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3371 · Cited by 1,159 · see citing works
Wang, R., Fang, X., Lu, Y., & Wang, S. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030580l · Cited by 1,137 · see citing works
Calculation of conformational ensembles from potentials of mena force
Sippl, M. J. (1990) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80269-4 · Cited by 1,064 · see citing works
B�hm, H.-J. (1994) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00126743 · Cited by 1,013 · see citing works
A General and Fast Scoring Function for Protein−Ligand Interactions: A Simplified Potential Approach
Muegge, I., & Martin, Y. C. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980536j · Cited by 1,008 · see citing works
Ballester, P. J., & Mitchell, J. B. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq112 · Cited by 988 · see citing works
Calculation of Protein-Ligand Binding Affinities
Gilson, M. K., & Zhou, H. (2007) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.36.040306.132550 · Cited by 966 · see citing works
Qiu, D., Shenkin, P. S., Hollinger, F. P., & Still, W. C. (1997) · The Journal of Physical Chemistry A · doi:10.1021/jp961992r · Cited by 962 · see citing works
Automated docking with grid‐based energy evaluation
Meng, E. C., Shoichet, B. K., & Kuntz, I. D. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130412 · Cited by 956 · see citing works
The PDBbind database: methodologies and updates
Wang, R., Fang, X., Lu, Y., Yang, C.-Y., & Wang, S. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm048957q · Cited by 933 · see citing works
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 · see citing works
Molecular Recognition and Docking Algorithms
Brooijmans, N., & Kuntz, I. D. (2003) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.32.110601.142532 · Cited by 789 · see citing works
Bissantz, C., Folkers, G., & Rognan, D. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm001044l · Cited by 740 · see citing works
Pairwise solute descreening of solute charges from a dielectric medium
Hawkins, G. D., Cramer, C. J., & Truhlar, D. G. (1995) · Chemical Physics Letters · doi:10.1016/0009-2614(95)01082-k · Cited by 715 · see citing works
Wang, J., Morin, P. E., Wang, W., & Kollman, P. A. (2001) · Journal of the American Chemical Society · doi:10.1021/ja003834q · Cited by 709 · see citing works
Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060522a · Cited by 705 · see citing works
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698 · see citing works
Rocchia, W., Sridharan, S., Nicholls, A. O., Alexov, E. G., Chiabrera, A., & Honig, B. H. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1161 · Cited by 687 · see citing works
Gehlhaar, D. K., Verkhivker, G. M., Rejto, P. A., Sherman, C. J., Fogel, D. R., Fogel, L. J., & Freer, S. T. (1995) · Chemistry & Biology · doi:10.1016/1074-5521(95)90050-0 · Cited by 684 · see citing works
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 623 · see citing works
Jain, A. N. (1996) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00124474 · Cited by 609 · see citing works
Wang, W., Donini, O., Reyes, C. M., & Kollman, P. A. (2001) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.30.1.211 · Cited by 549 · see citing works
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 514 · see citing works
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 508 · see citing works
Kuhn, B., & Kollman, P. A. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000241h · Cited by 506 · see citing works
Detailed Analysis of Scoring Functions for Virtual Screening
Ståhl, M., & Rarey, M. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0003992 · Cited by 490 · see citing works
Assessing Scoring Functions for Protein−Ligand Interactions
Ferrara, P., Gohlke, H., Price, D. J., Klebe, G., & Brooks, C. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030489h · Cited by 481 · see citing works
Validation and Use of the MM-PBSA Approach for Drug Discovery
Kuhn, B., Gerber, P. R., Schulz‐Gasch, T., & Ståhl, M. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049081q · Cited by 465 · see citing works
A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
Perola, E., Walters, W. P., & Charifson, P. S. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20088 · Cited by 446 · see citing works
Development and validation of a modular, extensible docking program: DOCK 5
Moustakas, D. T., Lang, P. T., Pegg, S. C. ‐., Pettersen, E. F., Kuntz, I. D., Brooijmans, N., & Rizzo, R. C. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9060-4 · Cited by 442 · see citing works
Tanaka, S., & Scheraga, H. A. (1976) · Macromolecules · doi:10.1021/ma60054a013 · Cited by 437 · see citing works
Böhm, H. (1998) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007999920146 · Cited by 431 · see citing works
Statistical Potentials Extracted From Protein Structures: How Accurate Are They?
Thomas, P. D., & Dill, K. A. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0175 · Cited by 425 · see citing works
Consensus scoring for ligand/protein interactions
Clark, R. D., Strizhev, A., Leonard, J. M., Blake, J. F., & Matthew, J. B. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00125-5 · Cited by 401 · see citing works
Huang, S., & Zou, X. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21214 · Cited by 400 · see citing works
Generalized Born Model Based on a Surface Integral Formulation
Ghosh, A., Rapp, C. S., & Friesner, R. A. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp982533o · Cited by 386 · see citing works
General and targeted statistical potentials for protein–ligand interactions
Mooij, W. T. M., & Verdonk, M. L. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20588 · Cited by 369 · see citing works
Recommendations for evaluation of computational methods
Jain, A. N., & Nicholls, A. O. (2008) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-008-9196-5 · Cited by 350 · see citing works
Additivity principles in biochemistry.
Dill, K. A. (1997) · Journal of Biological Chemistry · doi:10.1074/jbc.272.2.701 · Cited by 347 · see citing works
Velec, H. F. G., Gohlke, H., & Klebe, G. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050436v · Cited by 346 · see citing works
An iterative knowledge‐based scoring function for protein–protein recognition
Huang, S., & Zou, X. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21949 · Cited by 336 · see citing works
A smooth permittivity function for Poisson–Boltzmann solvation methods
Grant, J., Pickup, B. T., & Nicholls, A. O. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1032 · Cited by 335 · see citing works
An Alternative Method for the Evaluation of Docking Performance: RSR vs RMSD
Yusuf, D., Davis, A. M., Kleywegt, G. J., & Schmitt, S. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800084x · Cited by 334 · see citing works
How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment
Wang, R., & Wang, S. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010025x · Cited by 312 · see citing works
Muegge, I. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050038s · Cited by 306 · see citing works
A Knowledge-Based Energy Function for Protein−Ligand, Protein−Protein, and Protein−DNA Complexes
Zhang, C., Liu, S., Zhu, Q., & Zhou, Y. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049314d · Cited by 287 · see citing works
How to Derive a Protein Folding Potential? A New Approach to an Old Problem
Mirny, L. A., & Shakhnovich, E. I. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0704 · Cited by 277 · see citing works
DeWitte, R. S., & Shakhnovich, E. I. (1996) · Journal of the American Chemical Society · doi:10.1021/ja960751u · Cited by 264 · see citing works
New Class IV Charge Model for Extracting Accurate Partial Charges from Wave Functions
Li, J., Zhu, T., Cramer, C. J., & Truhlar, D. G. (1998) · The Journal of Physical Chemistry A · doi:10.1021/jp972682r · Cited by 260 · see citing works
Scoring functions for protein-ligand docking.
Bentham Science Publisher, B. S. P. (2006) · Current Protein & Peptide Science · doi:10.2174/138920306778559395 · Cited by 258 · see citing works
An iterative method for extracting energy-like quantities from protein structures.
Thomas, P. D., & Dill, K. A. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.21.11628 · Cited by 254 · see citing works
SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex
Wang, R., Liu, L., Lai, L., & Youqi, T. (1998) · Journal of Molecular Modeling · doi:10.1007/s008940050096 · Cited by 249 · see citing works
VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands
Head, R. D., Smythe, M. L., Oprea, T. I., Waller, C. L., Green, S. M., & Marshall, G. R. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9539002 · Cited by 247 · see citing works
A model binding site for testing scoring functions in molecular docking.
Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., & Shoichet, B. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00777-5 · Cited by 237 · see citing works
MM-GB/SA Rescoring of Docking Poses in Structure-Based Lead Optimization
Guimarães, C. R. W., & Cardozo, M. G. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800004w · Cited by 220 · see citing works
Huang, S., & Zou, X. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20504 · Cited by 215 · see citing works
Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model
Zou, X., Yaxiong, & Kuntz, I. D. (1999) · Journal of the American Chemical Society · doi:10.1021/ja984102p · Cited by 214 · see citing works
MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening
Yin, S., Biedermannová, L., Vondrášek, J., & Dokholyan, N. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001167 · Cited by 204 · see citing works
Pearlman, D. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050306m · Cited by 193 · see citing works
Statistical potentials and scoring functions applied to protein–ligand binding
Gohlke, H., & Klebe, G. (2001) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(00)00195-0 · Cited by 181 · see citing works
Binding MOAD, a high-quality protein ligand database
Benson, M. L., Smith, R. D., Khazanov, N. A., DIMCHEFF, B., Beaver, J., DRESSLAR, P., Nerothin, J., & Carlson, H. A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm911 · Cited by 178 · see citing works
Exhaustive docking of molecular fragments with electrostatic solvation
Majeux, N., Scarsi, M., Apostolakis, J., Ehrhardt, C., & Caflisch, A. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19991001)37:1 · Cited by 175 · see citing works
Huang, N., Kalyanaraman, C., Irwin, J. J., & Jacobson, M. P. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0502855 · Cited by 169 · see citing works
Oda, A., Tsuchida, K., Takakura, T., Yamaotsu, N., & Hirono, S. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050283k · Cited by 167 · see citing works
Huang, S., & Zou, X. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20505 · Cited by 163 · see citing works
Ishchenko, A. V., & Shakhnovich, E. I. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm0105833 · Cited by 157 · see citing works
Free energy calculations in rational drug design
Reddy, M. R., & Erion, M. D. (2001) · Medical Entomology and Zoology · Cited by 154 · see citing works
Huang, S., & Zou, X. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci9002987 · Cited by 136 · see citing works
Ligand−Protein DataBase: Linking Protein−Ligand Complex Structures to Binding Data
Roche, O., Kiyama, R., & Brooks, C. L. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm000467k · Cited by 132 · see citing works
Investigation of MM-PBSA Rescoring of Docking Poses
Thompson, D. C., Humblet, C. C., & Joseph‐McCarthy, D. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700470c · Cited by 127 · see citing works
Evaluation of PMF Scoring in Docking Weak Ligands to the FK506 Binding Protein
Muegge, I., Martin, Y. C., Hajduk, P. J., & Fesik, S. W. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990073x · Cited by 125 · see citing works
Kroemer, R. T., Vulpetti, A., McDonald, J. J., Rohrer, D. C., Trosset, J., Giordanetto, F., Cotesta, S., McMartin, C., Kihlén, M., & Stouten, P. F. W. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049970m · Cited by 124 · see citing works
Scoring functions for protein–ligand interactions: a critical perspective
Schulz‐Gasch, T., & Ståhl, M. (2004) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2004.08.004 · Cited by 120 · see citing works
SFCscore: Scoring functions for affinity prediction of protein–ligand complexes
Sotriffer, C., Sanschagrin, P. C., Matter, H., & Klebe, G. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22058 · Cited by 118 · see citing works
Jain, A. N. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008100132405 · Cited by 116 · see citing works
Efficient molecular docking of NMR structures: Application to HIV‐1 protease
Huang, S., & Zou, X. (2006) · Protein Science · doi:10.1110/ps.062501507 · Cited by 103 · see citing works
Docking into Knowledge-Based Potential Fields: A Comparative Evaluation of DrugScore
Sotriffer, C., Gohlke, H., & Klebe, G. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm025507u · Cited by 103 · see citing works
Efficient Evaluation of Binding Free Energy Using Continuum Electrostatics Solvation
Huang, D., & Caflisch, A. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm049726m · Cited by 98 · see citing works
Mitchell, J. B. O., Laskowski, R. A., Alex, A., Forster, M. J., & Thornton, J. M. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199908)20:11 · Cited by 98 · see citing works
Terp, G. E., Johansen, B. N., Christensen, I. T., & Jørgensen, F. S. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm001090l · Cited by 97 · see citing works
Puvanendrampillai, D., & Mitchell, J. B. O. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg243 · Cited by 95 · see citing works
Contact area difference (CAD): a robust measure to evaluate accuracy of protein models
Abagyan, R. A., & Totrov, M. M. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0994 · Cited by 95 · see citing works
AffinDB: a freely accessible database of affinities for protein-ligand complexes from the PDB
Block, P. C. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj039 · Cited by 90 · see citing works
Deng, W., Breneman, C. M., & Embrechts, M. J. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034246+ · Cited by 88 · see citing works
Supervised Consensus Scoring for Docking and Virtual Screening
Teramoto, R., & Fukunishi, H. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6004993 · Cited by 86 · see citing works
Rajamani, R., & Good, A. C. (2007) · PubMed · Cited by 81 · see citing works
Self-consistently optimized energy functions for protein structure prediction by molecular dynamics
Koretke, K. K., Luthey-Schulten, Z. A., & Wolynes, P. G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.6.2932 · Cited by 81 · see citing works
Danzhi Huang, A., Lüthi, U., Kolb, P., Edler, K., Cecchini, M., Audetat, S., Barberis, A., & Caflisch, A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050499d · Cited by 80 · see citing works
Knowledge-based potentials--back to the roots.
Wa, K., & Sippl, M. J. (1998) · PubMed · Cited by 68 · see citing works
Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors
Ha, S., Andreani, R., Robbins, A. H., & Muegge, I. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008137707965 · Cited by 65 · see citing works
Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study
Thomas, A. M., Hinsen, K., Field, M. J., & Pérahia, D. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990101)34:1 · Cited by 64 · see citing works
M-Score: A Knowledge-Based Potential Scoring Function Accounting for Protein Atom Mobility
Yang, C., Wang, R., & Wang, S. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050043w · Cited by 63 · see citing works
Automated docking of highly flexible ligands by genetic algorithms: A critical assessment
Cecchini, M., Kolb, P., Majeux, N., & Caflisch, A. (2003) · Journal of Computational Chemistry · doi:10.1002/jcc.10384 · Cited by 58 · see citing works
GARD: A Generally Applicable Replacement for RMSD
Baber, J. C., Thompson, D. C., Cross, J. B., & Humblet, C. C. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9001074 · Cited by 58 · see citing works
Pairwise GB/SA Scoring Function for Structure-based Drug Design
Liu, H., Kuntz, I. D., & Zou, X. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp0312518 · Cited by 55 · see citing works
Liu, H., & Zou, X. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp060334w · Cited by 52 · see citing works
GFscore: a general nonlinear consensus scoring function for high-throughput docking.
Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., & Morelli, X. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600758 · Cited by 51 · see citing works
Zhao, X., Liu, X., Wang, Y., Chen, Z., Kang, L., Zhang, H., Luo, X., Zhu, W., Chen, K., Li, H., et al. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7004719 · Cited by 50 · see citing works
Virtual high-throughput screening of molecular databases.
Seifert, M. H. J., Kraus, J., & Kramer, B. (2007) · PubMed · Cited by 48 · see citing works
Raub, S., Steffen, A., Kämper, A., & Marian, C. M. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7004669 · Cited by 40 · see citing works
SeleX-CS: A New Consensus Scoring Algorithm for Hit Discovery and Lead Optimization
Bar‐Haim, S., Aharon, A., Ben-Moshe, T., Marantz, Y., & Senderowitz, H. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800335j · Cited by 37 · see citing works
A potential function for protein folding
Seetharamulu, P., & Crippen, G. M. (1991) · Journal of Mathematical Chemistry · doi:10.1007/bf01192576 · Cited by 35 · see citing works
The MPSim‐Dock hierarchical docking algorithm: Application to the eight trypsin inhibitor cocrystals
Cho, A. E., Wendel, J. A., Vaidehi, N., Kekenes-Huskey, P. M., Floriano, W. B., Maiti, P. K., & Goddard, W. A. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20118 · Cited by 32 · see citing works
A Computational Model of Binding Thermodynamics: The Design of Cyclin-dependent Kinase 2 Inhibitors
Sims, P. A., Wong, C. F., & McCammon, J. A. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm0205043 · Cited by 30 · see citing works
Protein fold recognition without Boltzmann statistics or explicit physical basis
Huber, T., & Torda, A. E. (1998) · Protein Science · doi:10.1002/pro.5560070115 · Cited by 25 · see citing works
Knowledge-Based Energy Functions for Computational Studies of Proteins
Li, X., & Liang, J. (2007) · Springer eBooks · doi:10.1007/978-0-387-68372-0_3 · Cited by 24 · see citing works
Evaluation of a knowledge‐based potential of mean force for scoring docked protein–ligand complexes
Nobeli, I., Mitchell, J. B. O., Alex, A., & Thornton, J. M. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1036 · Cited by 20 · see citing works
Multiscale Generalized Born Modeling of Ligand Binding Energies for Virtual Database Screening
Liu, H., Grinter, S. Z., & Zou, X. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp901212t · Cited by 15 · see citing works
Kirtay, C. K., Mitchell, J. B. O., & Lumley, J. A. (2005) · QSAR & Combinatorial Science · doi:10.1002/qsar.200430926 · Cited by 14 · see citing works
Flagged first, then by citations
1 of the 133 references couldn’t be matched to a known paper.
Research works printed but not linked: 31
- [2]Böhm HJ and Stahl M, Rev. Comput. Chem, 2002, 18, 41– 87.
- [6]Seifert MHJ, Kraus J and Kramer B, Curr. Opin. Drug Discov. Devel, 2007, 10, 298– 307 17554856
- [7]Jain AN, Curr. Protein Pept. Sci, 2006, 7, 407– 420. 17073693
- [8]Schulz-Gasch T and Stahl M, Drug Discov. Today: Tech, 2004, 1, 231– 239. 24981490
- [9]Rajamani R and Good AC, Curr. Opin. Drug. Discov. Devel, 2007, 10, 308– 315 17554857
- [12]Meng EC, Shoichet BK and Kuntz ID, J. Comput. Chem, 1992, 13, 505– 524.
- [15]Zou X, Sun Y and Kuntz ID, J. Am. Chem. Soc, 1999, 121, 8033– 8043.
- [16]Liu H-Y, Kuntz ID and Zou X, J. Phys. Chem. B, 2004, 108, 5453– 5462.
- [18]Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK and Olson AJ, J. Comput. Chem, 1998, 19, 1639– 1662.
- [23]Abagyan R, Totrov M and Kuznetzov D, J. Comput. Chem, 1994, 15, 488– 506.
- [24]Böhm HJ, J. Comput.-Aided Mol. Des, 1994, 8, 243– 256. 7964925
- [29]Wang R, Liu L, Lai L and Tang Y, J. Mol. Model, 1998, 4, 379– 394.
- [44]DeWitte RS and Shakhnovich EI, J. Am. Chem. Soc, 1996, 118, 11733– 11744.
- [46]Mitchell JBO, Laskowski RA, Alex A and Thornton JM, J. Comput. Chem, 1999, 20, 1165– 1176.
- [47]Mitchell JBO, Laskowski RA, Alex A, Forster MJ and Thornton JM, J. Comput. Chem, 1999, 20, 1177– 1185.
- [52]Weiner SJ, Kollman PA and Case DA, J. Am. Chem. Soc, 1984, 106, 765– 784.
- [56]Grant JA, Pickup BT and Nicholls A, J. Comput. Chem, 2001, 22, 608– 640.
- [58]Still WC, Tempczyk A, Hawley RC and Hendrickson T, J. Am. Chem. Soc, 1990, 112, 6127– 6129.
- [59]Hawkins GD, Cramer CJ and Truhlar DG, Chem. Phys. Lett, 1995, 246, 122– 129.
- [60]Qiu D, Shenkin PS, Hollinger FP and Still WC, J. Phys. Chem. A, 1997, 101, 3005– 3014.
- [61]Gasteiger J and Marsili M, Tetrahedron, 1980, 36, 3219– 3228.
- [62]Li JB, Zhu TH, Cramer CJ, and Truhlar DG, J. Phys. Chem. A, 1998, 102, 1820– 1831.
- [75]Ghosh A, Rapp CS and A Friesner R, J. Phys. Chem. B, 1998, 102, 10983– 10990.
- [82]Head RD, Smythe ML, Oprea TI, Waller CL, Green SM and Marshall GR, J. Am. Chem. Soc, 1996, 118, 3959– 3969.
- [86]Miyazawa S and Jernigan RL, Macromolecules, 1985, 18, 534– 552.
- [89]Koppensteiner WA and Sippl MJ, Biochemistry (Moscow), 1998, 63, 247– 252. 9526121
- [93]Kirtay CK, Mitchell JBO and Lumley JA, QSAR & Combinatorial Sci,, 2005, 4, 527– 536.
- [98]Nobeli I, Mitchell JBO, Alex A and Thornton JM, J. Comput. Chem, 2001, 22, 673– 688.
- [104]Seetharamulu P and Crippen GM, J. Math. Chem, 1991, 6, 91– 110
- [105]Mimy LA and Shakhnovich EI, J. Mol. Biol, 1996, 264, 1164– 1179. 9000638
- [115]Bar-Haim S, Aharon A, Ben-Moshe T, Marantz Y and Senderowitz H, J. Chem. Inf. Model, 2009, 49, 623– 633. 19231809
Cited by
Sorted by citations
Molecular Docking and Structure-Based Drug Design Strategies
Ferreira, L. L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015) · Molecules · doi:10.3390/molecules200713384 · Cited by 2,642 · see citing works
“Scoring functions are categorized in the three following groups: force-field-based, empirical, and knowledge-based functions [56].”
BackgroundInsights into Protein–Ligand Interactions: Mechanisms, Models, and Methods
Du, X., Li, Y., Xia, Y.-L., Ai, S.-M., Liang, J., Sang, P., Ji, X., & Liu, S.-Q. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17020144 · Cited by 1,669 · see citing works
“Since empirical scoring functions comprise many energy terms, another challenge is how to avoid double-counting problems (over-fitting) [183].”
BackgroundMethodConcepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946 · see citing works
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762 · see citing works
“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…”
BackgroundA Structure-Based Drug Discovery Paradigm
Batool, M., Ahmad, S. B., & Choi, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112783 · Cited by 754 · see citing works
Virtual Screening Strategies in Drug Discovery: A Critical Review
Lavecchia, A., & Giovanni, C. D. (2013) · Current Medicinal Chemistry · doi:10.2174/09298673113209990001 · Cited by 687 · see citing works
“Scoring functions developed during the last few years have been reviewed in other works [67, 68].”
BackgroundComputational methods in drug discovery
Leelananda, S. P., & Lindert, S. (2016) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.12.267 · Cited by 647 · see citing works
Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113705 · Cited by 631 · see citing works
Use of molecular docking computational tools in drug discovery
Stanzione, F., Giangreco, I., & Cole, J. C. (2021) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2021.01.004 · Cited by 649 · see citing works
Receptor–ligand molecular docking
Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2013) · Biophysical Reviews · doi:10.1007/s12551-013-0130-2 · Cited by 611 · see citing works
“The search for ligands in virtual screening studies can be performed using diverse compound libraries that are available online, e.g. the ZINC database (Irwin et al. 2012), BindingDB (Liu et al. 2007), PubChem (Bolton et al. 2008), SuperNatural (Dunkel 2006) and ChEMBL (Gaulton et al. 2011).”
MethodStructure-Based Virtual Screening: From Classical to Artificial Intelligence
Maia, E. H. B., de Assis, L. C., de Oliveira, T. A., da Silva, A. M., & Taranto, A. G. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00343 · Cited by 591 · see citing works
“A linear regression is then performed as a way of predicting the values of some variables (Huang et al., 2010).”
BackgroundMethodMolecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective
Saikia, S., & Bordoloi, M. (2018) · Current Drug Targets · doi:10.2174/1389450119666181022153016 · Cited by 575 · see citing works
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 575 · see citing works
“Current scoring functions, as reviewed in other works (23,72), can be roughly classified into three types: (a) Force field-based scoring functions employ classic force field to compute the noncovalent ligand–target interactions, such as van der Waals and electrostatic energies.”
BackgroundForging the basis for developing protein-ligand interaction scoring functions.
Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00491 · Cited by 493 · see citing works
Chen, F., Liu, H., Sun, H., Pan, P., Li, Y., Li, D., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp03670h · Cited by 463 · see citing works
An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
Li, J., Fu, A., & Zhang, L. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00327-w · Cited by 440 · see citing works
Computer Aided Drug Design: Success and Limitations
Baig, M. H., Ahmad, K., Roy, S., Ashraf, J. M., Adil, M., Siddiqui, M. H., Khan, S. A., Kamal, M. A., Provazník, I., & Choi, I. (2016) · Current Pharmaceutical Design · doi:10.2174/1381612822666151125000550 · Cited by 426 · see citing works
Performance of machine-learning scoring functions in structure-based virtual screening.
Wójcikowski, M., Ballester, P. J., & Siedlecki, P. (2017) · Scientific Reports · doi:10.1038/srep46710 · Cited by 364 · see citing works
Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015) · WIREs Computational Molecular Science · doi:10.1002/wcms.1225 · Cited by 357 · see citing works
“…praised the ability of machine-learning SFs for effectively exploiting very large volumes of structural and interaction data, was by Huang et al. 21 In a review of recent advances and applications of structure-based virtual screening, Cheng et al. highlighted that a pio-neering machine-learning…”
BackgroundMolecular dynamics-driven drug discovery: leaping forward with confidence
Ganesan, A., Coote, M. L., & Barakat, K. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.11.001 · Cited by 358 · see citing works
Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331 · see citing works
“In this context, scoring functions emerge as a straightforward and fast strategy despite limited accuracy, remaining as the main alternative to be applied in VS experiments (Huang et al., 2010).”
MethodHollmann, A., Martínez, M. M. B., Maturana, P., Semorile, L., & Maffía, P. C. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00204 · Cited by 335 · see citing works
“Molecular docking programs through a cyclical evaluated ligand conformation by specific scoring functions until converging to a solution of minimum energy (Huang et al., 2010; Mulholland et al., 2016) have recently studied by docking the nisin2:lipid II complex in bacterial membranes, which has been put forward as the…”
BackgroundImproving scoring-docking-screening powers of protein-ligand scoring functions using random forest
Wang, C., & Zhang, Y. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24667 · Cited by 333 · see citing works
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032026 · Cited by 347 · see citing works
“As stated earlier, the fundamental attribute of molecular docking happens to be the scoring function, which is crafted to appraise the binding proclivities of the drug-like moieties for a target of relevance [101].”
Discovering Anti-Cancer Drugs via Computational Methods
Cui, W., Aouidate, A., Wang, S., Yu, Q., Li, Y.-H., & Yuan, S. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00733 · Cited by 310 · see citing works
“Finally, these programs evaluate the putative binding-free energy, which associates the scoring function to determine which compounds are more likely to bind to targets during the molecular docking (Huang et al., 2010).”
BackgroundStructure-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 297 · see citing works
Docking Challenge: Protein Sampling and Molecular Docking Performance
Elokely, K. M., & Doerksen, R. J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400040d · Cited by 288 · see citing works
Artificial intelligence in drug design
Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9342-2 · Cited by 288 · see citing works
Grinter, S. Z., & Zou, X. (2014) · Molecules · doi:10.3390/molecules190710150 · Cited by 264 · see citing works
“Finally, there is also a need for rigorous, wide evaluations of new docking methodologies, which are rapidly being introduced [40,153].”
BackgroundLi, H., Leung, K., Wong, M., & Ballester, P. J. (2015) · Molecular Informatics · doi:10.1002/minf.201400132 · Cited by 252 · see citing works
Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results
Fischer, A., Smieško, M., Sellner, M. S., & Lill, M. A. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c02227 · Cited by 243 · see citing works
Cao, Y., & Li, L. (2014) · Bioinform. · doi:10.1093/bioinformatics/btu104 · Cited by 201 · see citing works
The many faces of halogen bonding: a review of theoretical models and methods
Wolters, L. P., Schyman, P., Pavan, M. J., Jorgensen, W. L., Bickelhaupt, F. M., & Kozuch, S. (2014) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1189 · Cited by 219 · see citing works
Protein–Ligand Docking in the Machine-Learning Era
Yang, C., Chen, E. A., & Zhang, Y. (2022) · Molecules · doi:10.3390/molecules27144568 · Cited by 206 · see citing works
“Scoring functions are a family of computational methods that have been widely applied in SBDD for fast evaluation of protein–ligand interactions [30,31].”
BackgroundMethodRamírez, D., & Caballero, J. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17040525 · Cited by 190 · see citing works
“Several classes of scoring functions [9] which consider the protein as a rigid body [10,11], or as a soft body [12–14] have been developed.”
BackgroundExponential consensus ranking improves the outcome in docking and receptor ensemble docking
Palacio‐Rodríguez, K., Lans, I., Cavasotto, C. N., & Cossio, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-41594-3 · Cited by 186 · see citing works
Hu, N., Wang, C., Dai, X., Zhou, M., Gong, L., Yu, L., Peng, C. Y., & Li, Y. (2019) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2019.112361 · Cited by 184 · see citing works
Docking-based inverse virtual screening: methods, applications, and challenges
Xu, X., Huang, M., & Zou, X. (2018) · Biophysics Reports · doi:10.1007/s41048-017-0045-8 · Cited by 164 · see citing works
“A detailed review about scoring functions for protein–ligand docking can be found in a recent review (Huang et al. 2010).”
BackgroundSearch strategies and evaluation in protein–protein docking: principles, advances and challenges
Huang, S. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.02.005 · Cited by 164 · see citing works
NMR-based analysis of protein–ligand interactions
Cala, O., Guillière, F., & Krimm, I. (2013) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-013-6931-0 · Cited by 165 · see citing works
“In silico approaches are applied to search for ligands for a protein target (virtual screening) or to propose 3D models of protein–ligand complexes (docking calculations) [10, 11], whereas X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are both experimental techniques for resolving atomic stru…”
BackgroundProtein flexibility in docking and surface mapping
Lexa, K. W., & Carlson, H. A. (2012) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583512000066 · Cited by 168 · see citing works
Riyaphan, J., Pham, D., Leong, M. K., & Weng, C. (2021) · Biomolecules · doi:10.3390/biom11121877 · Cited by 176 · see citing works
“Moreover, a novel in silico study revealed a new class of triazoloquinazolines that are potent α-glucosidase inhibitors [252].”
BackgroundMeli, R., Morris, G. M., & Biggin, P. C. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.885983 · Cited by 164 · see citing works
“For example, the dielectric constant can be distance-dependent to take into account electrostatic screening due to the solvent and the lower dielectric constant in protein-ligand binding sites (Hingerty et al., 1985; Gilson and Honig 1986; Huang et al., 2010).”
BackgroundMethodInfluentialReverse Docking: A Powerful Tool for Drug Repositioning and Drug Rescue
Kharkar, P. S., Warrier, S. B., & Gaud, R. S. (2014) · Future Medicinal Chemistry · doi:10.4155/fmc.13.207 · Cited by 145 · see citing works
D3R Grand Challenge 3: blind prediction of protein–ligand poses and affinity rankings
Gaieb, Z., Parks, C. D., Chiu, M. W., Yang, H., Shao, C., Walters, W. P., Lambert, M. H., Nevins, N., Bembenek, S. D., Ameriks, M. K., et al. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0180-4 · Cited by 137 · see citing works
de Oliveira, T. A., da Silva, M. P., Maia, E. H. B., da Silva, A. M., & Taranto, A. G. (2023) · Drugs and Drug Candidates · doi:10.3390/ddc2020017 · Cited by 138 · see citing works
Damm-Ganamet, K. L., Smith, R. D., Dunbar, J. B., Stuckey, J. A., & Carlson, H. A. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400025f · Cited by 133 · see citing works
Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods
Choudhury, C., Murugan, N. A., & Priyakumar, U. D. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.03.006 · Cited by 132 · see citing works
“46 In the fi rst class, different types of polar and nonpolar inter-molecular interactions are extracted from a training set comprising the reported experimental structures, and parameters equivalent to each type of interactions are standardized with a certain weightage.”
BackgroundFrom directed evolution to computational enzyme engineering—A review
Chowdhury, R., & Maranas, C. D. (2019) · AIChE Journal · doi:10.1002/aic.16847 · Cited by 122 · see citing works
“There is an ever expanding literature of scoring function 54,55 and combinatorial search algorithms 56 devoted toward the efficient traversal of combinatorial space of residue alternatives.”
BackgroundAcoplamiento Molecular: Avances Recientes y Retos
Prieto-Martínez, F. D., Arciniega, M., & Medina‐Franco, J. L. (2018) · TIP Revista Especializada en Ciencias Químico-Biológicas · doi:10.22201/fesz.23958723e.2018.0.143 · Cited by 126 · see citing works
Lu, J., Hou, X., Wang, C., & Zhang, Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00645 · Cited by 116 · see citing works
Combined strategies in structure-based virtual screening
Wang, Z., Sun, H., Shen, C., Hu, X., Gao, J., Li, D., Cao, D., & Hou, T. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp06303j · Cited by 116 · see citing works
Wang, Z., Zheng, L., Liu, Y., Qu, Y., Li, Y., Zhao, M., Mu, Y., & Li, W. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.753002 · Cited by 118 · see citing works
Learning from the ligand: using ligand-based features to improve binding affinity prediction
Boyles, F., Deane, C. M., & Morris, G. M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz665 · Cited by 109 · see citing works
Deep Generative Models in De Novo Drug Molecule Generation
Pang, C., Qiao, J., Zeng, X., Zou, Q., & Wei, L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01496 · Cited by 106 · see citing works
Structure-Based Virtual Screening
Li, Q., & Shah, S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6783-4_5 · Cited by 105 · see citing works
LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance
Hauser, A. S., & Windshügel, B. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00234 · Cited by 104 · see citing works
Retrieving functional pathways of biomolecules from single-particle snapshots
Dashti, A., Mashayekhi, G., Shekhar, M., Ben-Hail, D., Salah, S., Schwander, P., Georges, A. D., Singharoy, A., Frank, J., & Ourmazd, A. (2020) · Nature Communications · doi:10.1038/s41467-020-18403-x · Cited by 104 · see citing works
Artificial Intelligence (AI) in Drugs and Pharmaceuticals
Sahu, A., Mishra, J., & Kushwaha, N. (2021) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207325666211207153943 · Cited by 110 · see citing works
Li, Y., & Yang, J. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00049 · Cited by 98 · see citing works
NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID
Gaudreault, F., Morency, L.-P., & Najmanovich, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv458 · Cited by 94 · see citing works
“The NRGsuite offers the users control over a large number of important parameters in docking simulations including the assignment of flexible side-chains and definition of geometric constraints.”
BackgroundStructure-based design for binding peptides in anti-cancer therapy
Wang, S., & Yu, J. (2017) · Biomaterials · doi:10.1016/j.biomaterials.2017.11.024 · Cited by 92 · see citing works
dMM-PBSA: A New HADDOCK Scoring Function for Protein-Peptide Docking
Spiliotopoulos, D., Kastritis, P. L., Melquiond, A. S. J., Bonvin, A. M. J. J., Musco, G., Rocchia, W., & Spitaleri, A. (2016) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2016.00046 · Cited by 91 · see citing works
“Over the years, since the pioneering work of Kuntz et al. (1982), several scoring functions have been developed (Gilson and Zhou, 2007; Huang et al., 2010; Sarti et al., 2013), based on several terms.”
BackgroundRNA –ligand molecular docking: Advances and challenges
Zhou, Y., Jiang, Y., & Chen, S. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1571 · Cited by 89 · see citing works
Protein-Protein and Protein-Ligand Docking
Hernndez-Santoyo, A., Tenorio-Barajas, A. Y., Altuzar, V., Vivanco-Cid, H., & Mendoza-Barrer, C. (2013) · InTech eBooks · doi:10.5772/56376 · Cited by 89 · see citing works
“Several reviews on scoring are available in the literature [10-12].”
BackgroundBioinformatics and variability in drug response: a protein structural perspective
Lahti, J. L., Tang, G. W., Capriotti, E., Liu, T., & Altman, R. B. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0843 · Cited by 87 · see citing works
Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
Svensson, F., Karlén, A., & Sköld, C. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2004835 · Cited by 86 · see citing works
Antony, J., Grimme, S., Liakos, D. G., & Neese, F. (2011) · The Journal of Physical Chemistry A · doi:10.1021/jp203963f · Cited by 85 · see citing works
Could MM-GBSA be accurate enough for calculation of absolute protein/ligand binding free energies?
Mulakala, C., & Viswanadhan, V. N. (2013) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2013.09.005 · Cited by 84 · see citing works
Assessment of ligand binding residue predictions in CASP9
Schmidt, T., Haas, J., Cassarino, T. G., & Schwede, T. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23174 · Cited by 83 · see citing works
Nagasundaram, N., Yapp, E. K. Y., Le, N. Q. K., Kamaraj, B., Al‐Subaie, A. M., & Yeh, H. (2019) · BioMed Research International · doi:10.1155/2019/8427042 · Cited by 82 · see citing works
Poli, G., Martinelli, A., & Tuccinardi, T. (2016) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.1080/14756366.2016.1193736 · Cited by 81 · see citing works
“Several attempts to increase docking accuracy and reliability, thus improving its VS performance, reported in literature concerned the development of optimized SFs, rescoring procedures and pose filtering methods 13,25–31 .”
MethodIn Silico Strategies in Tuberculosis Drug Discovery
Macalino, S. J. Y., Billones, J. B., Organo, V. G., & Carrillo, M. C. O. (2020) · Molecules · doi:10.3390/molecules25030665 · Cited by 79 · see citing works
Su, M., Feng, G., Liu, Z., Li, Y., & Wang, R. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00714 · Cited by 78 · see citing works
Multipose Binding in Molecular Docking
Atkovska, K., Samsonov, S. A., Paszkowski‐Rogacz, M., & Pisabarro, M. T. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15022622 · Cited by 79 · see citing works
“One of the most commonly used computational screening methods is molecular docking, which aims to predict the structure and binding affinity of a complex between a receptor and a ligand [3–5].”
MethodMachine‐Learning Techniques Applied to Antibacterial Drug Discovery
Durrant, J. D., & Amaro, R. E. (2014) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12423 · Cited by 78 · see citing works
“Ligand- and receptor-based methods must find a balance between speed and accuracy (57).”
BackgroundCharting a Path to Success in Virtual Screening
Forli, S. (2015) · Molecules · doi:10.3390/molecules201018732 · Cited by 77 · see citing works
One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery
Ross, G. A., Morris, G. M., & Biggin, P. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct4004228 · Cited by 77 · see citing works
Virtual Screening in Drug Design
Lill, M. A. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-342-8_1 · Cited by 77 · see citing works
Khoury, L. E., Santos‐Martins, D., Sasmal, S., Eberhardt, J., Bianco, G., Ambrosio, F. A., Solis-Vasquez, L., Koch, A., Forli, S., & Mobley, D. L. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00240-w · Cited by 75 · see citing works
Wang, D. D., Zhu, M., & Yan, H. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa107 · Cited by 74 · see citing works
Score_set: A CAPRI benchmark for scoring protein complexes
Lensink, M. F., & Wodak, S. J. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24678 · Cited by 71 · see citing works
“9 The energy terms may represent physics-based residue or atom pair potentials,10,11 or a statistical simplification thereof.”
BackgroundComprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges
Huang, S. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx030 · Cited by 74 · see citing works
Practical Considerations in Virtual Screening and Molecular Docking
Berry, M. J., Fielding, B. C., & Gamieldien, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802508-6.00027-2 · Cited by 74 · see citing works
“The GB model is also more efficient and faster at ranking binding affinities of ligands, making it more suitable in a virtual screening context (Hou et al. , 2011a, 2011b; Huang et al. , 2010; Li et al. , 2010).”
MethodYang, C., & Zhang, Y. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00485 · Cited by 74 · see citing works
Integrated Molecular Modeling and Machine Learning for Drug Design
Xia, S., Chen, E., & Zhang, Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00814 · Cited by 73 · see citing works
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 72 · see citing works
Converging a Knowledge-Based Scoring Function: DrugScore2018
Dittrich, J., Schmidt, D., Pfleger, C., & Gohlke, H. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00582 · Cited by 69 · see citing works
Molecular Docking: A Structure-Based Approach for Drug Repurposing
Kumar, S., & Kumar, S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00006-7 · Cited by 76 · see citing works
Automated docking for novel drug discovery
Bello, M., Martínez‐Archundia, M., & Correa‐Basurto, J. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.794780 · Cited by 72 · see citing works
“Despite its simple physical meaning, a major challenge for force field scoring functions is how to treat the solvent in ligand binding [114] .”
BackgroundVirtual Screening Strategies in Medicinal Chemistry: The State of the Art and Current Challenges
Braga, R. C., Alves, V. M., Silva, A. C., do Nascimento, M. N., Silva, F. M., Lião, L. M., & Andrade, C. H. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026614666140929120749 · Cited by 71 · see citing works
Sarkar, M., Paul, S. S., & Mukherjea, K. K. (2013) · Journal of Luminescence · doi:10.1016/j.jlumin.2013.03.026 · Cited by 70 · see citing works
Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides
Zhou, P., Li, B., Yan, Y., Jin, B., Wang, L., & Huang, S. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00142 · Cited by 69 · see citing works
Efficient conformational ensemble generation of protein-bound peptides
Yan, Y., Zhang, D., & Huang, S. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0246-7 · Cited by 68 · see citing works
“Because of its high computational efficiency, ensemble docking has been widely used to consider molecular flexibility in both protein– protein and protein–ligand docking [10, 26, 27].”
The latest automated docking technologies for novel drug discovery
Caballero, J. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1858793 · Cited by 68 · see citing works
“Despite the great amount of innovative ideas reflected in too many different scoring functions, it is common to classify them into empirical, force field-based, and knowledge-based functions [15].”
BackgroundVirtual Screening with Gnina 1.0
Sunseri, J., & Koes, D. (2021) · Molecules · doi:10.3390/molecules26237369 · Cited by 65 · see citing works
“A “scoring function” maps the input structure representing the relative location and orientation of the pair of molecules to a score representing the strength of their interaction[1].”
BackgroundIn silico methods for design of biological therapeutics
Roy, A., Nair, S. A., Sen, N., Soni, N., & Madhusudhan, M. S. (2017) · Methods · doi:10.1016/j.ymeth.2017.09.008 · Cited by 62 · see citing works
Paul, B. K., Ray, D., & Guchhait, N. (2012) · Physical Chemistry Chemical Physics · doi:10.1039/c2cp23496c · Cited by 62 · see citing works
Moon, S., Hwang, S.-Y., Lim, J., & Kim, W. Y. (2023) · Digital Discovery · doi:10.1039/d3dd00149k · Cited by 61 · see citing works
- Authors
- Huang, S.-Y., Grinter, S. Z., & Zou, X.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Zou, Xiaoqin
- Venue
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2010
- Volume
- 12
- Issue
- 40
- Pages
- 12899
- ISSN
- 1463-9076 (ISSN-L)
- 1463-9084
- PMC
- PMC11103779
- OpenAlex
- W2038542598
- Cited by
- 508
- References
- 133
- Citations by year
- 2026: 27 · 2025: 34 · 2024: 33 · 2023: 34 · 2022: 45 · 2021: 44 · 2020: 31 · 2019: 34 · 2018: 30 · 2017: 22 · 2016: 37 · 2015: 34 · 2014: 42 · 2013: 27 · 2012: 20
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Physical Chemistry Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R21GM088517)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-ligand docking, scoring functions, knowledge-based scoring, consensus scoring, structure-based drug design, protein-ligand binding affinity prediction, benchmarking database
- MeSH terms
- Algorithms, Computer Simulation, Ligands, Protein Binding, Proteins, Drug Design
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