Feature-map vectors: a new class of informative descriptors for computational drug discovery
Authors: Landrum, Gregory; Penzotti, Julie; Putta, Santosh
Source: Journal of Computer-Aided Molecular Design, Volume 20, Number 12, December 2006 , pp. 751-762(12)
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-HT3, DHFR, thrombin, and ACE. The interpretability of these descriptors has been demonstrated for CDK2 and 5-HT3, where the method automatically discovers the standard literature pharmacophore.
Document Type: Research Article
Affiliations: Email: email@example.com
Publication date: December 2006