A comparative study on the molecular descriptors for predicting drug-likeness of small molecules.

ABSTRACT: Screening of " drug-like" molecule from the molecular database produced through high throughput techniques and their large repositories requires robust classification. In our work, a set of heuristically chosen nine molecular descriptors including four from Lipinski's rule, were used as classification parameter for screening "drug-like" molecules. The robustness of classification was compared with four fundamental descriptors of Lipinski. Back propagation neural network based classifier was applied on a database of 60000 molecules for classification of, " drug-like" and "non drug-like" molecules. Classification result using nine descriptors showed high classification accuracy of 96.1% in comparison to that using four Lipinski's descriptors which yielded an accuracy of 82.48%. Also a signif

SUBMITTER: Mishra H 

PROVIDER: S-EPMC2728118 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature