High-Dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues Based on Support Vector Regression (SVR).

Abstract

To design ARC-111 analogues with improved efficiency, we constructed the QSAR of 22 ARC-111 analogues with RPMI8402 tumor cells. First, the optimized support vector regression (SVR) model based on the literature descriptors and the worst descriptor elimination multi-roundly (WDEM) method had similar generalization as the artificial neural network (ANN) model for the test set. Secondly, seven and 11 more effective descriptors out of 2,923 features were selected by the high-dimensional descriptor selection nonlinearly (HDSN) and WDEM method, and the SVR models (SVR3 and SVR4) with these selected descriptors resulted in better evaluation measures and a more precise predictive power for the test set. The interpretability system of better SVR models was further established. Our analysis offers some useful parameters for designing ARC-111 analogues with enhanced antitumor activity.

• Authors: Wei Zhou, Zhijun Dai, Yuan Chen, Haiyan Wang, Zheming Yuan
• Journal: International journal of molecular sciences (Int J Mol Sci) Vol. 13 Issue 1 Pg. 1161-1172 ( 2012) ISSN: 1422-0067 [Electronic] Switzerland
• PMID: 22312310 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Antineoplastic Agents
• Naphthyridines
• topovale

Topics

• Antineoplastic Agents (chemistry)
• Naphthyridines (chemistry)
• Neural Networks, Computer
• Quantitative Structure-Activity Relationship
• Support Vector Machine