A D3R prospective evaluation of machine learning for protein-ligand scoring

Journal of Computer-Aided Molecular Design

Volumn 30, Issue 9, 2016, Pages 761-771

  Sunseri, Jocelyn ^a   Ragoza, Matthew ^a   Collins, Jasmine ^a   Koes, David Ryan ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

D3R; Machine learning; Protein ligand scoring; Virtual screening

Indexed keywords

ARTIFICIAL INTELLIGENCE; BINDING ENERGY; CLASSIFICATION (OF INFORMATION); E-LEARNING; LEARNING SYSTEMS; LIGANDS; PROTEINS;

BINDING AFFINITIES; D3R; MACHINE-LEARNING; PERFORMANCE; POSE PREDICTIONS; PROSPECTIVE EVALUATION; PROTEIN LIGANDS; PROTEIN-LIGAND SCORING; SCORING METHODS; VIRTUAL SCREENING;

BINDERS;

HEAT SHOCK PROTEIN 90; MITOGEN ACTIVATED PROTEIN KINASE KINASE KINASE 4; LIGAND; PROTEIN; PROTEIN BINDING;

ANALYTIC METHOD; AREA UNDER THE CURVE; ARTICLE; BINDING AFFINITY; CRYSTAL STRUCTURE; DRUG DESIGN DATA RESOURCE; HIGH THROUGHPUT SCREENING; LIGAND BINDING; MACHINE LEARNING; MOLECULAR DYNAMICS; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN BINDING; PROTEIN LIGAND SCORING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; VIRTUAL REALITY; ALGORITHM; BINDING SITE; BIOLOGY; CHEMISTRY; HUMAN; MOLECULAR DOCKING; PROCEDURES; PROSPECTIVE STUDY;

ALGORITHMS; BINDING SITES; COMPUTATIONAL BIOLOGY; HSP90 HEAT-SHOCK PROTEINS; HUMANS; LIGANDS; MACHINE LEARNING; MOLECULAR DOCKING SIMULATION; PROSPECTIVE STUDIES; PROTEIN BINDING; PROTEINS;

EID: 84984910946     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-016-9960-x     Document Type: Article

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