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Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease.

Klon, Anthony and Glick, Meir and Davies, John (2004) Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease. Journal of Chemical Information and Computer Sciences, 44 (6). pp. 2216-2224. ISSN 0095-2338

Abstract

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

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Date Deposited: 14 Dec 2009 13:55
Last Modified: 31 Jan 2013 01:09
URI: https://oak.novartis.com/id/eprint/714

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