Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

BMC Bioinformatics

Volumn 16, Issue 6, 2015, Pages

  Ashtawy, Hossam M ^a   Mahapatra, Nihar R ^a  

^a Michigan State University   (United States)

Author keywords

Docking power; Ligand pose identification; Machine learning; Molecular docking; Scoring functions

Indexed keywords

ARTIFICIAL INTELLIGENCE; BINS; COMPLEXATION; COST EFFECTIVENESS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MOLECULAR MODELING; PROTEINS; STATISTICAL TESTS;

BENCHMARK DATASETS; GEOMETRICAL FEATURES; MOLECULAR DOCKING; PROTEIN-LIGAND COMPLEXES; ROOT MEAN SQUARE DEVIATIONS; SCORING FUNCTIONS; STRUCTURE BASED DRUG DESIGNS; SYSTEMATIC ANALYSIS;

LIGANDS;

LIGAND; PROTEIN; PROTEIN BINDING;

ARTIFICIAL INTELLIGENCE; BINDING SITE; CHEMISTRY; COMPUTER PROGRAM; DRUG DESIGN; DRUG DEVELOPMENT; HUMAN; METABOLISM;

ARTIFICIAL INTELLIGENCE; BINDING SITES; DRUG DESIGN; DRUG DISCOVERY; HUMANS; LIGANDS; PROTEIN BINDING; PROTEINS; SOFTWARE;

EID: 84964698564     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-16-S6-S3     Document Type: Article

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