Feature-map vectors: a new class of informative descriptors for computational drug discovery.
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.
Landrum
Gregory A
GA
Novartis Institutes For BioMedical Research, CH-4002, Basel, Switzerland. gregory.landrum@novartis.com
Penzotti
Julie E
JE
Putta
Santosh
S
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017076:Computer-Aided Design; D051357:Cyclin-Dependent Kinase 2 / Q000737:chemistry / Q000187:drug effects; D015195:Drug Design; D006801:Humans; D066298:In Vitro Techniques; D008024:Ligands; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry; D044406:Receptors, Serotonin, 5-HT3 / Q000737:chemistry / Q000187:drug effects
D008024:Ligands; D011506:Proteins; D044406:Receptors, Serotonin, 5-HT3; C495894:CDK2 protein, human; D051357:Cyclin-Dependent Kinase 2