title: 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 significant decrease of false positives resulted while using nine descriptors causing a sharp 18% increase of specificity of classification. From this study it appeared that Lipinski's descriptors which mainly deal with pharmacokinetic properties of molecules form the basis for identification of "drug-like" molecules that can be substantially improved by adding more descriptors representing pharmaco-dynamics properties of molecules.
authors.affiliation: Bioinformatics Division, Indian Institute of Information Technology, Allahabad, India. hrishikesh@iiita.ac.in
authors.forename: Hrishikesh
authors.initials: H
authors.lastname: Mishra
authors.forename: Nitya
authors.initials: N
authors.lastname: Singh
authors.forename: Tapobrata
authors.initials: T
authors.lastname: Lahiri
authors.forename: Krishna
authors.initials: K
authors.lastname: Misra
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