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

By Douglas B. Kitchen, H. Decornez, John R. Furr, Jürgen Bajorath. 2004

3,738 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Infectious Diseases, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “Furthermore, the solvent is not explicitly considered, hindering the estimation of desolvation energies [58].”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, background): “The ability to accurately predict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic molecules early in development, and guide medicinal chemistry efforts [1, 2].”
Virtual ligand screening: strategies, perspectives and limitations (2006, background): “Despite the fact that VS is still a young discipline, it has been reviewed frequently [22] [23] [24] [25] [26] [27] [28] [29] [30] , most recently in a comprehensive overview by Kubinyi [31] .”
Binding Affinity via Docking: Fact and Fiction (2018, background): “There are three main types of scoring functions: force-field based, knowledge-based statistical functions, and empirical scoring functions [23].”
Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, background): “11 are not derived directly from the entire molecular structure, but rather they typically depend on the chemical moieties, composed of a few functional groups, and their 3D orientations.(42,43) Therefore, we chose datasets from the protein-ligand binding category for investigating whether our model”
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, neutral): “Force field scoring functions parameterize the potential energy of a complex as a sum of energy terms arising from bonded and non-bonded interactions (Huang et al., 2006).”
Computational Methods in Drug Discovery (2014, methodology): “There are three basic methods to represent target and ligand structures in silico: atomic, surface, and grid representations (Halperin et al., 2002; Kitchen et al., 2004).”
Molecular docking: a powerful approach for structure-based drug discovery. (2011, background): “These advances allow the computational strategies to permeate all aspects of drug discovery today [1-5], such as the virtual screening (VS) techniques [6] for hit identification and methods for lead optimization.”
Molecular dynamics simulations and novel drug discovery (2018, methodology): “Structure-based virtual screening based on molecular docking methods has become an indispensable tool in drug discovery [13,15,103].”