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Search and Scoring Strategies used by Docking Programs
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Search and Scoring Strategies used by Docking Programs
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Search and Scoring Strategies used by Docking Programs

Journal: Journal of chemical information and modeling

Article Title: Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening

doi: 10.1021/acs.jcim.7b00153

Figure Lengend Snippet: Search and Scoring Strategies used by Docking Programs

Article Snippet: The tools used in this work are all commonly available, even to academic groups, and are based on both different docking techniques and scoring function types. table ft1 table-wrap mode="anchored" t5 caption a7 docking program search algorithm scoring function AutoDock v4.2 Lamarkian genetic algorithm with simulated annealing force field DOCK v6.7 incremental construction (anchor-and- grow) force field FRED v3.0.1 exhaustive rigid docking search, discretized configuration space empirical HYBRID v3.0.1 exhaustive rigid docking search, discretized configuration space knowledge-based + empirical PLANTS v1.2 ant colony optimization empirical rDock v2013.1 genetic algorithm, Monte Carlo, minimization empirical Smina 1.1.2 iterated local search global optimzer knowledge-based Surflex-Dock v3.040 incremental construction with matching algorithm empirical Open in a separate window Search and Scoring Strategies used by Docking Programs To evaluate consensus scoring strategies, 21 targets were selected from the DUD-E benchmark data set, 23 each accompanied by sets of active and decoy compounds ( ).

Techniques: