A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions

Journal of Computational Chemistry

Volumn 35, Issue 12, 2014, Pages 932-943

  Grinter, Sam Z ^a,b   Zou, Xiaoqin ^a,b,c  

^a UNIVERSITY OF MISSOURI   (United States)

^b UNIVERSITY OF MISSOURI   (United States)

^c University of Missouri   (United States)

Author keywords

knowledge based scoring function; ligand interactions; molecular docking; protein; sparse data

Indexed keywords

BINDING ENERGY; PROTEINS;

BAYESIAN STATISTICAL APPROACH; LIGAND INTERACTIONS; MOLECULAR DOCKING; PROTEIN STRUCTURE PREDICTION; PROTEIN-LIGAND INTERACTIONS; PROTEIN-PROTEIN DOCKING; SCORING FUNCTIONS; SPARSE DATA;

KNOWLEDGE BASED SYSTEMS;

LIGAND; PROTEIN;

ARTICLE; BAYES THEOREM; CHEMICAL STRUCTURE; CHEMISTRY; KNOWLEDGE-BASED SCORING FUNCTION; LIGAND INTERACTIONS; MOLECULAR DOCKING; SPARSE DATA;

KNOWLEDGE-BASED SCORING FUNCTION; LIGAND INTERACTIONS; MOLECULAR DOCKING; PROTEIN; SPARSE DATA;

BAYES THEOREM; LIGANDS; MODELS, MOLECULAR; PROTEINS;

EID: 84898003872     PISSN: 01928651     EISSN: 1096987X     Source Type: Journal    
DOI: 10.1002/jcc.23579     Document Type: Article

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