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Feature-map vectors: a new class of informative descriptors for computational drug discovery.

Landrum, Gregory and Penzotti, Julie E and Putta, Santosh (2006) Feature-map vectors: a new class of informative descriptors for computational drug discovery. Journal of Computer-Aided Molecular Design, 20 (12). pp. 751-762. ISSN 0920-654X

Abstract

In order to develop robust machine-learning or statistical models for predicting biological activity, descriptors that capture the essence of the protein-ligand interaction are required. In the absence of structural information from X-ray or NMR experiments, deriving informative descriptors can be difficult. We have developed feature-map vectors (FMVs), a new class of descriptors based on chemical features, to address this challenge. FMVs, which are derived from the conformational models of a few actives, are low dimensional, problem specific, and highly interpretable. By using shape-based alignments and scoring with chemical features, FMVs can combine information about a molecule's shape and the pharmacophores it can match. In five validation studies, bag classifiers built using FMVs have shown high enrichments for identifying actives for five diverse targets: CDK2, 5-HT(3), DHFR, thrombin, and ACE. The interpretability of these descriptors has been demonstrated for CDK2 and 5-HT(3), where the method automatically discovers the standard literature pharmacophore.

Item Type: Article
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Additional Information: author can archive post-print (ie final draft post-refereeing); Publisher's version/PDF cannot be used
Keywords: Chemical features; Descriptor; Machine learning; Molecular shape; Pharmacophores
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Date Deposited: 14 Dec 2009 13:58
Last Modified: 14 Dec 2009 13:58
URI: https://oak.novartis.com/id/eprint/548

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