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Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design.
Crystal structure of an hsp90–geldanamycin complex: targeting of a protein chaperone by an antitumor agent
Stebbins, C. E. et al · 1997
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J · 1997
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The protein data bank
Berman, H. M. et al · 2000
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Combined molecular mechanical and continuum solvent approach (mm-pbsa/gbsa) to predict ligand binding
Massova, I. & Kollman, P. A · 2000
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Are free energy calculations useful in practice? a comparison with rapid scoring functions for the p38 map kinase protein system
Pearlman, D. A. & Charifson, P. S · 2001
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Understanding molecular simulation: from algorithms to applications Second edition edn (Academic Press, San Diego, 2002)
Frenkel, D. & Smit, B · 2002
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Further development and validation of empirical scoring functions for structure-based binding affinity prediction
Wang, R. X., Lai, L. H. & Wang, S. M · 2002
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Molecular properties that influence the oral bioavailability of drug candidates
Veber, D. F. et al · 2002
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Improved protein–ligand docking using gold
Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W. & Taylor, R. D · 2003
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Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
Friesner, R. A. et al · 2004
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The pdbbind database: Collection of binding affinities for protein- ligand complexes with known three-dimensional structures
Wang, R., Fang, X., Lu, Y. & Wang, S · 2004
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A kinematic view of loop closure
Coutsias, E. A., Seok, C., Jacobson, M. P. & Dill, K. A · 2004
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General and targeted statistical potentials for protein–ligand interactions
Mooij, W. T. M. & Verdonk, M. L · 2005
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Rdkit: Open-source cheminformatics (2006)
Landrum, G · 2006
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Crystal structure of the t315i abl mutant in complex with the aurora kinases inhibitor pha-739358
Modugno, M. et al · 2007
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Structure-based optimization of protein tyrosine phosphatase 1b inhibitors: from the active site to the second phosphotyrosine binding site
Wilson, D. P. et al · 2007
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Discovery and design of novel hsp90 inhibitors using multiple fragment-based design strategies
Huth, J. R. et al · 2007
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Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science
Laio, A. & Gervasio, F. L · 2008
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Dihydroxylphenyl amides as inhibitors of the hsp90 molecular chaperone
Kung, P. P. et al · 2008
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Charmm-gui: a web-based graphical user interface for charmm
Jo, S., Kim, T., Iyer, V. G. & Im, W · 2008
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Empirical scoring functions for advanced protein-ligand docking with plants
Korb, O., Stutzle, T. & Exner, T. E · 2009
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, O. & Olson, A. J · 2010
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Ballester, P. J. & Mitchell, J. B. O · 2010
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Basic ingredients of free energy calculations: a review
Christ, C. D., Mark, A. E. & van Gunsteren, W. F · 2010
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
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Atpase inhibitors of heat-shock protein 90, second season
Janin, Y. L · 2010
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Fragment-based drug discovery applied to hsp90. discovery of two lead series with high ligand efficiency
Murray, C. W. et al · 2010
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How to obtain statistically converged mm/gbsa results
Genheden, S. & Ryde, U · 2010
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How were new medicines discovered?
Swinney, D. C. & Anthony, J · 2011
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Molecular parameter optimization gateway (paramchem) workflow management through teragrid asta
Ghosh, J. et al · 2011
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Mdanalysis: a toolkit for the analysis of molecular dynamics simulations
Michaud-Agrawal, N., Denning, E. J., Woolf, T. B. & Beckstein, O · 2011
Cited alongside, same era.
Discovery and optimization of a novel spiropyrrolidine inhibitor of β \beta -secretase (bace1) through fragment-based drug design
Efremov, I. V. et al · 2012
Cited alongside, same era.
Application of gaussian electrostatic model (gem) distributed multipoles in the amoeba force field
Cisneros, G. A · 2012
Cited alongside, same era.
Methods for the elucidation of protein-small molecule interactions
McFedries, A., Schwaid, A. & Saghatelian, A · 2013
Cited alongside, same era.
Autodockfr: advances in protein-ligand docking with explicitly specified binding site flexibility
Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J. & Sanner, M. F · 2015
Cited alongside, same era.
Artificial intelligence teaches drugs to target proteins by tackling the induced folding problem
Fernandez, A · 2020
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Deep learning unravels a dynamic hierarchy while empowering molecular dynamics simulations
Fernández, A · 2020
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Deep learning unravels a dynamic hierarchy while empowering molecular dynamics simulations
Fernández, A · 2020
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Heat shock protein 90 inhibitors: an update on achievements, challenges, and future directions
Li, L., Wang, L., You, Q. D. & Xu, X. L · 2020
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Scalable molecular dynamics on cpu and gpu architectures with namd
Phillips, J. C. et al · 2020
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Open graph benchmark: Datasets for machine learning on graphs
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Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W. & Jhoti, H · 2016
Cited alongside, same era.
Applying high-performance computing in drug discovery and molecular simulation
Liu, T. et al · 2016
Cited alongside, same era.
Protein ensembles: how does nature harness thermodynamic fluctuations for life? the diverse functional roles of conformational ensembles in the cell
Wei, G. H., Xi, W. H., Nussinov, R. & Ma, B. Y · 2016
Cited alongside, same era.
Insights into protein–ligand interactions: mechanisms, models, and methods
Du, X. et al · 2016
Cited alongside, same era.
Interaction entropy: A new paradigm for highly efficient and reliable computation of protein–ligand binding free energy
Duan, L. L., Liu, X. & Zhang, J. Z. H · 2016
Cited alongside, same era.
Charmm-gui input generator for namd, gromacs, amber, openmm, and charmm/openmm simulations using the charmm36 additive force field
Lee, J. et al · 2016
Cited alongside, same era.
Predicting binding free energies: frontiers and benchmarks
Mobley, D. L. & Gilson, M. K · 2017
Cited alongside, same era.
Hu, W. et al · 2020
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Decision making in structure-based drug discovery: visual inspection of docking results
Fischer, A., Smiesko, M., Sellner, M. & Lill, M. A · 2021
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gmx_mmpbsa: a new tool to perform end-state free energy calculations with gromacs
Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E · 2021
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Extended connectivity interaction features: improving binding affinity prediction through chemical description
Sanchez-Cruz, N., Medina-Franco, J. L., Mestres, J. & Barril, X · 2021
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Graphdta: predicting drug-target binding affinity with graph neural networks
Nguyen, T. et al · 2021
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Communicative representation learning on attributed molecular graphs
Song, Y. et al · 2021
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Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Li, S. et al · 2021
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Torchmd: A deep learning framework for molecular simulations
Doerr, S. et al · 2021
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Torchmd: A deep learning framework for molecular simulations
Doerr, S. et al · 2021
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Do transformers really perform badly for graph representation?
Ying, C. et al · 2021
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Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S · 2021
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A practical guide to large-scale docking
Bender, B. J. et al · 2021
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Thermodynamic dissection of potency and selectivity of cytosolic hsp90 inhibitors
Yoshimura, C. et al · 2021
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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
Rube, H. T. et al · 2022
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Assessment of the generalization abilities of machine-learning scoring functions for structure-based virtual screening
Zhu, H., Yang, J. C. & Huang, N · 2022
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Pre-training of equivariant graph matching networks with conformation flexibility for drug binding
Wu, F. et al · 2022
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Robust optimization as data augmentation for large-scale graphs
Kong, K. et al · 2022
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Binding affinity estimation from restrained umbrella sampling simulations
Kumar, V. G., Polasa, A., Agrawal, S., Kumar, T. K. S. & Moradi, M · 2023
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Misato-machine learning dataset of protein-ligand complexes for structure-based drug discovery
Siebenmorgen, T. et al · 2023
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Plas-20k: Extended dataset of protein-ligand affinities from md simulations for machine learning applications
Priyakumar, U. D. et al · 2023
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Gb-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features
Rayka, M. & Firouzi, R · 2023
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Multi-shelled ecif: improved extended connectivity interaction features for accurate binding affinity prediction
Shiota, K. & Akutsu, T · 2023
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The PyMOL molecular graphics system (2023)
Schrödinger, LLC · 2023
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Predicting equilibrium distributions for molecular systems with deep learning
Zheng, S. et al · 2024
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