Virtual screening and repositioning of inconclusive molecules of beta-lactamase Bioassays-A data mining approach

Gad, Akshata and Manuel, Andrew Titus and Jinuraj, K R and John, Lijo and Sajeev, R and Priya, Shanmuga V G and Jaleel, Abdul U C (2017) Virtual screening and repositioning of inconclusive molecules of beta-lactamase Bioassays-A data mining approach. In: COMPUTATIONAL BIOLOGY AND CHEMISTRY, 70 . pp. 65-88.

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Abstract

This study focuses on the best possible way forward in utilizing inconclusive molecules of PubChem bioassays AID 1332, AID 434987 and AID 434955, which are related to beta-lactamase inhibitors of Mycobacterium tuberculosis (Mtb). The inadequacy in the experimental methods that were observed during the invitro screening resulted in an inconclusive dataset. This could be due to certain moieties present within the molecules. In order to reconsider such molecules, insilico methods can be suggested in place of invitro methods For instance, datamining and medicinal chemistry methods: have been adopted to prioritise the inconclusive dataset into active or inactive molecules. These include the Random Forest algorithm for dataminning, Lilly MedChem rules for virtually screening out the promiscuity, and Self Organizing Maps (SOM) for clustering the active molecules and enlisting them for repositioning through the use of artificial neural networks. These repositioned molecules could then be prioritized for downstream drug discovery analysis. (C) 2017 Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	COMPUTATIONAL BIOLOGY AND CHEMISTRY
Additional Information:	Copy right for this article belongs to the ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Department/Centre:	Division of Physical & Mathematical Sciences > Physics
Date Deposited:	03 Nov 2017 10:45
Last Modified:	04 Feb 2019 05:20
URI:	http://eprints.iisc.ac.in/id/eprint/58161

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