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Virtual screening system for finding structurally diverse hits by active learning.

Authors :
Fujiwara Y
Yamashita Y
Osoda T
Asogawa M
Fukushima C
Asao M
Shimadzu H
Nakao K
Shimizu R
Source :
Journal of chemical information and modeling [J Chem Inf Model] 2008 Apr; Vol. 48 (4), pp. 930-40. Date of Electronic Publication: 2008 Mar 20.
Publication Year :
2008

Abstract

Two virtual screening strategies, "query by bagging" (QBag) and "query by bagging with descriptor-sampling" (QBagDS), based on active learning were devised. The QBag strategy generates multiple structure-activity relationship rules by bagging and selects compounds to improve the rules. To find many structurally diverse hits, the QBagDS strategy generates rules by bagging with descriptor sampling. They can also use prior knowledge about hits to improve the efficiency at the beginning of screening. We performed simulation experiments and clustering analysis for several G-protein coupled receptors and showed that the QBag and QBagDS strategies outperform the conventional similarity-based strategy and that using both descriptor sampling and prior knowledge are effective for finding many hits. We applied the bagging with descriptor sampling strategy to novel hit finding, and 4 of the 10 selected compounds showed high inhibition.

Details

Language :
English
ISSN :
1549-9596
Volume :
48
Issue :
4
Database :
MEDLINE
Journal :
Journal of chemical information and modeling
Publication Type :
Academic Journal
Accession number :
18351729
Full Text :
https://doi.org/10.1021/ci700085q