Flexible docking using tabu search and an empirical estimate of binding affinity

By C. A. Baxter, Christopher W. Murray, D. Clark, David R. Westhead, Matthew Eldridge. 1998

Subfields: Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “Random/Stochastic Search eHiTS [28] AutoDock [34] FRED [30] Gold [35] Surflex-Dock [31] PRO_LEADS [44] DOCK [32] EADock [45] GLIDE [37] ICM [46] EUDOC [38] LigandFit [47] FlexX [39] Molegro Virtual Docker [48] Hammerhead [40] CDocker [49] Flog [41] GlamDock [50] SLIDE [42] PLANTS [51] ADAM [43]…”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “PRO_LEADS (Baxter et al., 1998) and PSI-DOCK (Pei et al., 2006) are two examples of this category. c. Evolutionary Algorithms (EA) - these algorithms are based on the idea of biological evolution, with the most famous Genetic Algorithms (GAs).”
Advances and Challenges in Protein-Ligand Docking (2010, methodology): “Example docking programs are PRO LEADS [73] and PSI-DOCK [70].”
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, unknown): “Three scoring functions in the GOLD software version 3.2 (Jones et al., 1995, 1997): GoldScore, ChemScore (Baxter, 1998; Eldridge, 1997) and ASP (Mooij and Verdonk, 2005).”
Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. (2013, background): “Historically, most of these comparisons have been made in terms of their ability to reproduce the X-ray pose of selected ligands [8, 18, 21, 32-50], their capability to predict binding free energies from the best-scored pose [16, 21, 24, 27, 28, 35, 51-56], or their ability to identify known binders”
Database Screening for HIV Protease Ligands: The Influence of Binding-Site Conformation and Representation on Ligand Selectivity (1999, methodology): “New docking tools are typically verified on a large variety of test cases, with up to 200 protein-ligand complexes redocked to test the robustness of the method (Jones et al. 1997; Baxter et al. 1998; Rarey, Kramer, & Lengauer 1999).”
Structure-Based Virtual Screening: From Classical to Artificial Intelligence (2020, background): “, 2009); • Tabu search (Baxter et al., 1998); • Particle swarm optimization (Gowthaman et al.”
Performance of machine-learning scoring functions in structure-based virtual screening (2017, methodology): “RF-Score-VS v3 however performed significantly better than PMF, twofold better compared to the second best ChemScore function (11.4 vs. 4.9 respectively) and 3 to 4-times better than the remaining three functions (D-Score, G_Score and Dock 6.6 built-in function).”
Comparative evaluation of eight docking tools for docking and virtual screening accuracy (2004, background): “Indeed, for DOCK and FLEXX, docking errors are generally related to ligand ﬂexibility (number of rotatable bonds (cid:1) 10) and the resulting numerous possibilities of ligand fragmentation.”