Feature-map vectors: a new class of informative descriptors for computational drug discovery
| Autor: | Gregory A. Landrum, Santosh Putta, Julie E. Penzotti |
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| Rok vydání: | 2007 |
| Předmět: |
Models
Molecular In Vitro Techniques Ligands Machine learning computer.software_genre Artificial Intelligence Drug Discovery Feature (machine learning) Humans Computer Simulation Physical and Theoretical Chemistry Interpretability business.industry Drug discovery Chemistry Cyclin-Dependent Kinase 2 Proteins Statistical model Computer Science Applications Drug Design Computer-Aided Design Artificial intelligence Receptors Serotonin 5-HT3 Pharmacophore business computer Algorithms |
| Zdroj: | Journal of Computer-Aided Molecular Design. 20:751-762 |
| ISSN: | 1573-4951 0920-654X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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