A review of protein-small molecule docking methods

By Richard D. Taylor, P. Jewsbury, J. Essex. J. Comput. Aided Mol. Des., 2002

622 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9662e5b9d84bb048556a40315954de7a40ff85e0

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In Computational Theory and Mathematics

In Organic Chemistry

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, methodology): “Generally, in these methods, the binding site and the ligand are approximated by “hot” points and the superposition of matching point is evaluated (Taylor et al., 2002).”
ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility (2006, positive): “A large number of docking programs have been developed in the last 20 years based on a variety of search algorithms.(1,2) The use of such programs in conjunction with one or more scoring functions to evaluate and rank potential ligands from chemical collections is a standard”
Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “Most ligand docking applications struggle to correctly predict conformational selection or induced-fit effects ( 58 ) resulting from ligand and protein flexibility.”
Improved protein–ligand docking using GOLD (2003, background): “DOCK, 1 FlexX, 2 PRO_LEADS, 3 and GOLD 4,5 are examples of docking programs, but many more are reported in the literature (for an overview of docking strategies see Taylor et al. 6 ).”
Virtual Screening with AutoDock: Theory and Practice (2010, background): “Building on 2 decades of research, many successful methods for docking of ligands to macromolecular targets have been developed [6-13].”
Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go (2008, methodology): “…drug design field shows that significant improvements must still be achieved in order to develop highly accurate molecular docking and VS methods (Taylor et al ., 2002; Krumrine et al ., 2003; Brooijmans and Kuntz, 2003; Mohan et al ., 2005; Ferrara et al ., 2006; Klebe, 2006; Rester, 2006;…”
Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get” (2009, neutral): “…Can Lose Internal Freedom and Entropy Some simple models assume that the ligand entropy lost on binding correlates with the number of rotatable bonds in the ligand (Böhm, 1993; Gilson and Zhou, 2007; Huey et al., 2007; Laederach and Reilly, 2003; Taylor et al., 2002; Gohlke and Klebe, 2002).”
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”
Protein-Protein and Protein-Ligand Docking (2013, background): “There are a number of excellent reviews of molecular docking methods and a large number of publications comparing the performance of a variety of molecular docking tools [1-3], [13].”
Protein–ligand docking: Current status and future challenges (2006, methodology): “energy function that does not require any sort of derivative information.(21) In addition, MC methods are more efficient”
A Structure-Based Drug Discovery Paradigm (2019, neutral): “These modifications are accepted or rejected depending upon probability functions such as genetic algorithm methods [52,54] and the Monte Carlo (MC) method.”