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Journal of Computational Chemistry

Volumn 38, Issue 3, 2017, Pages 169-177

Improving scoring-docking-screening powers of protein–ligand scoring functions using random forest

(2) Wang, Cheng a Zhang, Yingkai a,b

a NEW YORK UNIVERSITY (United States)

b NEW YORK UNIVERSITY SHANGHAI (China)

Author keywords

docking; machine learning; protein ligand binding affinity; random forest; scoring function

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; BINDING ENERGY; BINS; DECISION TREES; DOCKING; HTTP; LEARNING ALGORITHMS; LEARNING SYSTEMS; LIGANDS; PROTEINS;

AUTODOCK VINAS; LIGAND BINDING; LIGAND BINDING AFFINITY; LIGAND DOCKING; RANDOM FORESTS; SCORING FUNCTIONS; SCORING POWER; SELECTION FRAMEWORK;

DELTA FUNCTIONS;

LIGAND; PROTEIN;

ALGORITHM; CHEMISTRY; MOLECULAR DOCKING;

ALGORITHMS; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEINS;

EID: 85000454204 PISSN: 01928651 EISSN: 1096987X Source Type: Journal
DOI: 10.1002/jcc.24667 Document Type: Article

Times cited : (231)

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