QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines.
The least squares support vector machine (LSSVM), as a novel machine learning algorithm, was used to develop quantitative and classification models as a potential screening mechanism for a novel series of 1,4-dihydropyridine calcium channel antagonists for the first time. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modeling results in a nonlinear, seven-descriptor model based on LSSVM with mean-square errors 0.2593, a predicted correlation coefficient (R(2)) 0.8696, and a cross-validated correlation coefficient (R(cv)(2)) 0.8167. The best classification results are found using LSSVM: the percentage (%) of correct prediction based on leave one out cross-validation was 91.1%. This paper provides a new and effective method for drug design and screening.
Yao
Xiaojun
X
Department of Chemistry, Lanzhou University, Lanzhou 730000, China. xiaojunyao@yahoo.com
Liu
Huanxiang
H
Zhang
Ruisheng
R
Liu
Mancang
M
Hu
Zhide
Z
Panaye
A
A
Doucet
J P
JP
Fan
Botao
B
D000465:Algorithms; D002121:Calcium Channel Blockers / Q000737:chemistry* / Q000145:classification* / Q000494:pharmacology; D015220:Calcium Channels / Q000378:metabolism*; D004095:Dihydropyridines / Q000737:chemistry* / Q000145:classification* / Q000494:pharmacology; D015195:Drug Design; D004353:Drug Evaluation, Preclinical; D020128:Inhibitory Concentration 50; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
D002121:Calcium Channel Blockers; D015220:Calcium Channels; D004095:Dihydropyridines; C038806:1,4-dihydropyridine