A machine learning-based method to improve docking scoring functions and its application to drug repurposing

Journal of Chemical Information and Modeling

Volumn 51, Issue 2, 2011, Pages 408-419

  Kinnings, Sarah L ^a   Liu, Nina ^b   Tonge, Peter J ^b   Jackson, Richard M ^a   Xie, Lei ^c,d   Bourne, Philip E ^c  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b STONY BROOK UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^d City University of New York   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); GENES; IONS; MOLECULAR MODELING; PREDICTIVE ANALYTICS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

CLASSIFICATION MODELS; DOCKING SCORING FUNCTION; HIGH THROUGHPUT SCREENING; INDIVIDUAL COMPONENTS; MYCOBACTERIUM TUBERCULOSIS; NON-COVALENT INTERACTION; PHOSPHODIESTERASE INHIBITORS; SUPPORT VECTOR MACHINE (SVMS);

BINDING ENERGY;

ENOYL ACYL CARRIER PROTEIN REDUCTASE (NADH); LIGAND; PHOSPHODIESTERASE; PHOSPHODIESTERASE INHIBITOR;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BINDING SITE; CHEMICAL STRUCTURE; COMPUTER PROGRAM; DRUG ANTAGONISM; DRUG DEVELOPMENT; DRUG REPOSITIONING; HUMAN; METABOLISM; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BINDING SITES; DRUG DISCOVERY; DRUG REPOSITIONING; ENOYL-(ACYL-CARRIER-PROTEIN) REDUCTASE (NADH); HUMANS; LIGANDS; MODELS, MOLECULAR; PHOSPHODIESTERASE INHIBITORS; PHOSPHORIC DIESTER HYDROLASES; SOFTWARE;

EID: 79952178127     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci100369f     Document Type: Article

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