QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

Abstract:

: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.

journal_name

J Comput Aided Mol Des

authors

Liu HX,Zhang RS,Yao XJ,Liu MC,Hu ZD,Fan BT

doi

10.1007/s10822-004-2722-1

subject

Has Abstract

pub_date

2004-06-01 00:00:00

pages

389-99

issue

6

eissn

0920-654X

issn

1573-4951

journal_volume

18

pub_type

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