Using molecular docking, 3D-QSAR, and cluster analysis for screening structurally diverse data sets of pharmacological interest.
In this study, we propose a drug design approach which includes docking, molecular fingerprints based cluster analysis, and 'induced' descriptors based receptor-dependent 3D-QSAR. The method was shown to be very useful for screening and modeling structurally diverse data sets of pharmacological interest. Different from other receptor-dependent 3D-QSAR, no ambiguous alignments are required for the construction of the models, and the computational cost is relatively lower. Moreover, 'induced' descriptors were shown to be very powerful in "capturing" ligand-receptor intermolecular interactions. The methodology was validated for eight data sets sampled from the literature and from public databases: human sex hormone-binding globulin, human corticosteroid-binding globulin, anthrax lethal factor, HIV-1 reverse transcriptase, neuraminidase A, thrombin, trypsin, and Pneumocystis carinii dihydrofolate reductase data sets. The resulting models were interpretable; the constructed QSAR equations have high statistical significance and predictive strength; and the drug design solutions were shown to be useful for guiding ligand modification for the development of new inhibitors for a broad range of molecular targets.
Santos-Filho
Osvaldo A
OA
Jack Bell Research Centre at Vancouver General Hospital, Faculty of Medicine, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada. o_santosfilho@yahoo.com
Cherkasov
Artem
A
D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D005823:Genetics / Q000706:statistics & numerical data; D008024:Ligands; D008958:Models, Molecular; D010449:Peptide Mapping; D011485:Protein Binding; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D011955:Receptors, Drug / Q000737:chemistry
D008024:Ligands; D011955:Receptors, Drug