QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.
The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.
Liu
H X
HX
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
Zhang
R S
RS
Yao
X J
XJ
Liu
M C
MC
Hu
Z D
ZD
Fan
B T
BT
D000465:Algorithms; D001185:Artificial Intelligence; D002460:Cell Line; D003198:Computer Simulation; D051546:Cyclooxygenase 2; D052246:Cyclooxygenase 2 Inhibitors; D016861:Cyclooxygenase Inhibitors / Q000737:chemistry* / Q000145:classification / Q000494:pharmacology*; D015195:Drug Design; D006801:Humans; D008565:Membrane Proteins; D017711:Nonlinear Dynamics; D011451:Prostaglandin-Endoperoxide Synthases / Q000187:drug effects*; D021281:Quantitative Structure-Activity Relationship
D052246:Cyclooxygenase 2 Inhibitors; D016861:Cyclooxygenase Inhibitors; D008565:Membrane Proteins; D051546:Cyclooxygenase 2; C496540:PTGS2 protein, human; D011451:Prostaglandin-Endoperoxide Synthases