Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions

Journal of Chemical Information and Modeling

Volumn 57, Issue 4, 2017, Pages 1007-1012

  Li, Yang ^a   Yang, Jianyi ^a  

^a NANKAI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; BINDING ENERGY; CORRELATION METHODS; LIGANDS; PROTEINS;

ATOMIC DISTANCES; PEARSON CORRELATION COEFFICIENTS; PROTEIN-LIGAND BINDING AFFINITIES; PROTEIN-LIGAND INTERACTIONS; SCORING FUNCTIONS; SEQUENCE ALIGNMENTS; SEQUENCE SIMILARITY; STRUCTURE ALIGNMENT;

MACHINE LEARNING;

LIGAND; PROTEIN; PROTEIN BINDING;

AMINO ACID SEQUENCE; BIOLOGY; CHEMISTRY; MACHINE LEARNING; METABOLISM; PROCEDURES;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; LIGANDS; MACHINE LEARNING; PROTEIN BINDING; PROTEINS;

EID: 85018596195     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.7b00049     Document Type: Article

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