Prediction of P-glycoprotein substrates by a support vector machine approach

Journal of Chemical Information and Computer Sciences

Volumn 44, Issue 4, 2004, Pages 1497-1505

  Xue, Y ^a,b,c   Yap, C W ^a   Sun, L Z ^a   Cao, Z W ^a   Wang, J F ^a   Chen, Y Z ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b SINGAPORE MIT ALLIANCE   (Singapore)

^c SICHUAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MULTIDRUG-RESISTANT (MDR) AGENTS; PROBABILISTIC NEURAL NETWORKS (PNN); RECURSIVE FEATURE ELIMINATION (RFE) METHOD; SUPPORT VECTOR MACHINE (SVM);

APPROXIMATION THEORY; CHEMICAL COMPOUNDS; COMPUTATIONAL METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS; PROTEINS; STATISTICAL METHODS;

MOLECULAR DYNAMICS;

DRUG; GLYCOPROTEIN P;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; DRUG DESIGN; METABOLISM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DRUG DESIGN; MODELS, CHEMICAL; MOLECULAR STRUCTURE; P-GLYCOPROTEIN; PHARMACEUTICAL PREPARATIONS;

EID: 4043091303     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci049971e     Document Type: Article

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