SCOPUS 정보 검색 플랫폼

Volumn 34, Issue 2-3, 2015, Pages 115-126

Improving autodock vina using random forest: The growing accuracy of binding affinity prediction by the effective exploitation of larger data sets

(4) Li, Hongjian a Leung, Kwong Sak a Wong, Man Hon a Ballester, Pedro J b,c

a CHINESE UNIVERSITY OF HONG KONG (Hong Kong)

b WELLCOME TRUST SANGER INSTITUTE (United Kingdom)

c AIX MARSEILLE UNIVERSITY (France)

Author keywords

Docking; Drug lead optimization; Machine learning

Indexed keywords

ARTICLE; AUTODOCK VINA; BINDING AFFINITY; CLINICAL ASSESSMENT; COMPUTER PROGRAM; DATA ANALYSIS; LIGAND BINDING; MACHINE LEARNING; MEASUREMENT ACCURACY; MOLECULAR BIOLOGY; MOLECULAR DOCKING; PRIORITY JOURNAL; PROTEIN BINDING; RANDOM FOREST; STRUCTURAL BIOINFORMATICS; STRUCTURE ANALYSIS; TRAINING; SOFTWARE; THEORETICAL MODEL;

MACHINE LEARNING; MODELS, THEORETICAL; MOLECULAR DOCKING SIMULATION; SOFTWARE;

EID: 84923588607 PISSN: 18681743 EISSN: 18681751 Source Type: Journal
DOI: 10.1002/minf.201400132 Document Type: Article

Times cited : (209)

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