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

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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.

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Acknowledgements

The authors would like to thank Erin Bradley (Sunesis Inc.) for providing the aligned CDK2 crystal structures and Christian Lemmen (BioSolveIT Gmbh) for providing the ACE and thrombin datasets.

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Authors and Affiliations

  1. Novartis Institutes For BioMedical Research, CH-4002, Basel, Switzerland

    Gregory A. Landrum

  2. 5731, 16th Ave NE, Seattle, WA, 98105, USA

    Julie E. Penzotti

  3. Telik Inc., 3165 Porter Drive, Palo Alto, CA, 94304, USA

    Santosh Putta

Authors
  1. Gregory A. Landrum
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  2. Julie E. Penzotti
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  3. Santosh Putta
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Correspondence to Gregory A. Landrum.

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Landrum, G.A., Penzotti, J.E. & Putta, S. Feature-map vectors: a new class of informative descriptors for computational drug discovery. J Comput Aided Mol Des 20, 751–762 (2006). https://doi.org/10.1007/s10822-006-9085-8

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  • DOI: https://doi.org/10.1007/s10822-006-9085-8

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