期刊
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
卷 20, 期 12, 页码 751-762出版社
SPRINGER
DOI: 10.1007/s10822-006-9085-8
关键词
chemical features; descriptor; machine learning; molecular shape; pharmacophores
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.
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