Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density

Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874 · see citing works

A new mixing of Hartree–Fock and local density-functional theories

Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464304 · Cited by 16,515 · see citing works

Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

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

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

Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening

Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030644s · Cited by 5,280 · see citing works

Docking and scoring in virtual screening for drug discovery: methods and applications

Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,245 · see citing works

Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme.

Warshel, A., & Levitt, M. (1976) · Journal of Molecular Biology · doi:10.1016/0022-2836(76)90311-9 · Cited by 3,821 · see citing works

Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation

Jakalian, A., Jack, D. B., & Bayly, C. I. (2002) · Journal of Computational Chemistry · doi:10.1002/jcc.10128 · Cited by 3,428 · see citing works

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

Harder, E. D., Damm, W., Maple, J., Wu, C., Reboul, M., Xiang, J.-Y., Wang, L.-L., Lupyan, D., Dahlgren, M. K., Knight, J. L., et al. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00864 · Cited by 2,826 · see citing works

The performance of a family of density functional methods

Johnson, B. G., Gill, P. M. W., & Pople, J. A. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464906 · Cited by 1,926 · see citing works

Jaguar: A high‐performance quantum chemistry software program with strengths in life and materials sciences

Bochevarov, A. D., Harder, E. D., Hughes, T. F., Greenwood, J. R., Braden, D. A., Philipp, D. M., Rinaldo, D., Halls, M. D., Zhang, J., & Friesner, R. A. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24481 · Cited by 1,821 · see citing works

Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges

Vanommeslaeghe, K., Raman, E. P., & MacKerell, A. D. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3003649 · Cited by 1,651 · see citing works

Integrated Modeling Program, Applied Chemical Theory (IMPACT)

Banks, J. L., Beard, H. S., Cao, Y., Cho, A. E., Damm, W., Farid, R., Felts, A. K., Halgren, T. A., Mainz, D. T., Maple, J. R., et al. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20292 · Cited by 1,498 · see citing works

Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., et al. (2015) · Journal of the American Chemical Society · doi:10.1021/ja512751q · Cited by 1,313 · see citing works

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

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

Accurate First Principles Calculation of Molecular Charge Distributions and Solvation Energies from Ab Initio Quantum Mechanics and Continuum Dielectric Theory

Tannor, D. J., Marten, B., Murphy, R. B., Friesner, R. A., Sitkoff, D. F., Nicholls, A. O., Honig, B. H., Ringnalda, M. N., & Goddard, W. A. (1994) · Journal of the American Chemical Society · doi:10.1021/ja00105a030 · Cited by 1,044 · see citing works

Comparison of Shape-Matching and Docking as Virtual Screening Tools

Hawkins, P. C. D., Skillman, A. G., & Nicholls, A. O. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0603365 · Cited by 998 · see citing works

New Model for Calculation of Solvation Free Energies: Correction of Self-Consistent Reaction Field Continuum Dielectric Theory for Short-Range Hydrogen-Bonding Effects

Marten, B., Kim, K., Cortis, C. M., Friesner, R. A., Murphy, R. B., Ringnalda, M. N., Sitkoff, D. F., & Honig, B. H. (1996) · The Journal of Physical Chemistry · doi:10.1021/jp953087x · Cited by 952 · 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

Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding

Young, T. K., Abel, R., Kim, B., Berne, B. J., & Friesner, R. A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610202104 · Cited by 697 · see citing works

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 682 · see citing works

Self-consistent description of a metal–water interface by the Kohn–Sham density functional theory and the three-dimensional reference interaction site model

Kovalenko, A., & Hirata, F. (1999) · The Journal of Chemical Physics · doi:10.1063/1.478883 · Cited by 672 · see citing works

Alchemical free energy methods for drug discovery: Progress and challenges

Chodera, J., Mobley, D., Shirts, M., Dixon, R. W., Branson, K., & Pande, V. (2011) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2011.01.011 · Cited by 545 · see citing works

Virtual screening and fast automated docking methods

Schneider, G., & Böhm, H. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(01)02091-8 · Cited by 404 · see citing works

Recent Advances in Scaffold Hopping

Hu, Y., Stumpfe, D., & Bajorath, J. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01437 · Cited by 364 · see citing works

The molecular modeling toolkit: A new approach to molecular simulations

Hinsen, K. (2000) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(20000130)21:2 · Cited by 347 · see citing works

Structural Basis for Bile Acid Binding and Activation of the Nuclear Receptor FXR

Mi, L.-Z., Devarakonda, S., Harp, J. M., Han, Q., Pellicciari, R., Willson, T. M., Khorasanizadeh, S., & Rastinejad, F. (2003) · Molecular Cell · doi:10.1016/s1097-2765(03)00112-6 · Cited by 315 · see citing works

Classification of Current Scoring Functions

Liu, J., & Wang, R. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500731a · Cited by 302 · see citing works

FLOG: A system to select ?quasi-flexible? ligands complementary to a receptor of known three-dimensional structure

Miller, M. D., Kearsley, S. K., Underwood, D. J., & Sheridan, R. P. (1994) · Journal of Computer-Aided Molecular Design · doi:10.1007/bf00119865 · Cited by 281 · see citing works

pmx: Automated protein structure and topology generation for alchemical perturbations

Gapsys, V., Michielssens, S., Seeliger, D., & de Groot, B. L. (2014) · Journal of Computational Chemistry · doi:10.1002/jcc.23804 · Cited by 269 · see citing works

Three-Dimensional Molecular Theory of Solvation Coupled with Molecular Dynamics in Amber

Luchko, T., Gusarov, S., Roe, D. R., Simmerling, C. L., Case, D. A., Tuszyński, J. A., & Kovalenko, A. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct900460m · Cited by 263 · see citing works

Combining quantum mechanics and interatomic potential functions inab initio studies of extended systems

Sauer, J., & Sierka, M. (2000) · Journal of Computational Chemistry · doi:10.1002/1096-987x(200012)21:16 · Cited by 234 · see citing works

Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set

Li, Y., Liu, Z., Li, J., Han, L., Liu, J., Zhao, Z., & Wang, R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500080q · Cited by 214 · see citing works

On Scaffolds and Hopping in Medicinal Chemistry

Brown, N., & Jacoby, E. (2006) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/138955706778742768 · Cited by 159 · see citing works

Structural plasticity in the oestrogen receptor ligand‐binding domain

Nettles, K. W., Bruning, J. B., Gil, G. A., O'Neill, E., Nowak, J., Hughs, A., Kim, Y., DeSombre, E. R., Dilis, R., Hanson, R. N., et al. (2007) · EMBO Reports · doi:10.1038/sj.embor.7400963 · Cited by 138 · see citing works

Rapid, Precise, and Reproducible Prediction of Peptide–MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment

Wan, S., Knapp, B., Wright, D. W., et al. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00179 · Cited by 134 · see citing works

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

Lee, T., Hu, Y., Sherborne, B. S., Guo, Z., & York, D. M. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00102 · Cited by 132 · see citing works

A DFT-Based QM-MM Approach Designed for the Treatment of Large Molecular Systems: Application to Chorismate Mutase

Crespo, A., Scherlis, D. A., Marti, M. A., Ordejón, P., Roitberg, A. E., & Estrin, D. A. (2003) · The Journal of Physical Chemistry B · doi:10.1021/jp036236h · Cited by 128 · see citing works

Small Molecule Modulation of Nuclear Receptor Conformational Dynamics: Implications for Function and Drug Discovery

Kojetin, D. J., & Burris, T. P. (2012) · Molecular Pharmacology · doi:10.1124/mol.112.079285 · Cited by 122 · see citing works

Accurate Modeling of Scaffold Hopping Transformations in Drug Discovery.

Wang, L.-L., Deng, Y., Wu, Y., Kim, B., LeBard, D. N., Wandschneider, D., Beachy, M., Friesner, R., & Abel, R. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00991 · Cited by 119 · see citing works

FESetup: Automating Setup for Alchemical Free Energy Simulations

Loeffler, H. H., Michel, J., & Woods, C. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00368 · Cited by 110 · see citing works

FEW: A workflow tool for free energy calculations of ligand binding

Homeyer, N., & Gohlke, H. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23218 · Cited by 98 · see citing works

Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties

Richter, H. G., Benson, G. M., Bleicher, K., Blum, D., Chaput, E., Clemann, N., Feng, S., Gardès, C., Grether, U., HARTMAN, P. G., et al. (2011) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2010.12.123 · Cited by 79 · see citing works

Optimization of Gaussian surface calculations and extension to solvent-accessible surface areas

Weiser, J., Shenkin, P. S., & Still, W. C. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199905)20:7 · Cited by 76 · see citing works

Design and x-ray crystal structures of high-potency nonsteroidal glucocorticoid agonists exploiting a novel binding site on the receptor

Biggadike, K., Bledsoe, R. K., Coe, D., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909125106 · Cited by 72 · see citing works

Discovery of novel and orally active FXR agonists for the potential treatment of dyslipidemia & diabetes

Richter, H. G., Benson, G. M., Blum, D., Chaput, E., Feng, S., Gardès, C., Grether, U., HARTMAN, P. G., Kuhn, B., Martin, R. E., et al. (2010) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2010.11.039 · Cited by 60 · see citing works

Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist

Feng, S., Yang, M., Zhang, Z., Wang, Z., Hong, D., Richter, H. G., Benson, G. M., Bleicher, K., Grether, U., Martin, R. E., et al. (2009) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2009.03.008 · Cited by 55 · see citing works

Collaborating to improve the use of free-energy and other quantitative methods in drug discovery

Sherborne, B. S., Shanmugasundaram, V., Cheng, A. C., et al. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9996-y · Cited by 53 · see citing works

Recent Advances toward a General Purpose Linear-Scaling Quantum Force Field

Giese, T. J., Huang, M. S., Chen, H., et al. (2014) · Accounts of Chemical Research · doi:10.1021/ar500103g · Cited by 43 · see citing works

Quantum-mechanics methodologies in drug discovery: applications of docking and scoring in lead optimization.

Crespo, A., Rodriguez-Granillo, A., & Lim, V. T. (2017) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026617666170707120609 · Cited by 34 · see citing works

Consensus bond-charge increments fitted to electrostatic potential or field of many compounds: Application to MMFF94 training set

Bush, B. L., Bayly, C. I., & Halgren, T. A. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(19991115)20:14 · Cited by 33 · see citing works

The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP

Hu, Y., Sherborne, B. S., Lee, T., Case, D. A., York, D. M., & Guo, Z. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9920-5 · Cited by 32 · see citing works

Assessing the Growth of Bioactive Compounds and Scaffolds over Time: Implications for Lead Discovery and Scaffold Hopping

Jasial, S., Hu, Y., & Bajorath, J. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00713 · Cited by 17 · see citing works

Nucleic acid reactivity: Challenges for next-generation semiempirical quantum models

Huang, M. S., Giese, T. J., & York, D. M. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23933 · Cited by 15 · see citing works

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

Fradera, X., Verras, A., Hu, Y., et al. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0053-2 · Cited by 10 · see citing works

Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges

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

Virtual screening : Computer aided drug design

pandey, R., Yadav, M. K., Yadav, P., et al. (2021) · Journal of Emerging Technologies and Innovative Research

Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users

Garijo, D., Corcho, Ó., Gil, Y., et al. (2014) · IEEE International Conference on eScience · doi:10.1109/escience.2014.33 · Cited by 14 · see citing works

Workflow Analysis using Graph Kernels

Friesen, N., & Stefan Rüping, A. (2010) · Cited by 10 · see citing works

Approaches to Virtual Screening and Screening Library Selection

Wildman, S. A. (2013) · Current Pharmaceutical Design · doi:10.2174/1381612811319260009 · Cited by 8 · see citing works

Discovery of Potential Antitubercular Activity in on-AntibioticsThrough Virtual Screening

Nagasree, K. P. (2015)