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 pharmaco­dynamics properties of molecules.

Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes.

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