In:
Chinese Journal of Chemistry, Wiley, Vol. 28, No. 9 ( 2010-09), p. 1587-1592
Abstract:
The multi‐target quantitative structure‐activity relationship (QSAR) study of human immunodeficiency virus (HIV‐1) inhibitors was addressed by applying a simple, yet effective linear regression model based on the multi‐task learning paradigm. QSAR studies were performed on three datasets of HIV‐1 inhibitors targeted on protease, integrase and reverse transcriptase, respectively. By using the multi‐task learning method, the synergy among different set of inhibitors was exploited and an efficient multi‐target QSAR modeling for HIV‐1 inhibitors was obtained. The general descriptor features and drug‐like features for compound description were ranked according to their jointly importance in multi‐target QSAR modeling, respectively. A SAReport for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is expected to provide useful clues for the design of novel multi‐target HIV‐1 inhibitors with increasing likelihood of successful therapies.
Type of Medium:
Online Resource
ISSN:
1001-604X
,
1614-7065
DOI:
10.1002/cjoc.201090269
Language:
English
Publisher:
Wiley
Publication Date:
2010
detail.hit.zdb_id:
2144352-X
SSG:
6,25
