Project titles for:
Project titles for: 2007-09
- Comparison of software for Analysis of Molecular Marker Data
- In silico Prediction of miRNA in Rice Genome
- Web Tools for Bioinformatics
- In silico Structure Prediction of enzyme Arsenite methyltransferase
- Comparison of various software for QTL Analysis
- QSAR studies of Tuberculosis Inhibitors
- Determination of common transcription factor binding site in promoter region of abiotic stress resistance gene in rice
- SNP Mining in Rice Genome
- Protein Modelling of Betaine Aldehyde Dehydrogenase-2 in Rice
- Data Mining of Chemical Substructures for Biological Efficacy
- Plant Disease Database mined from PubMed
- Comparison of Protein Structure Prediction Methods
- Virtual High Throughput Screening for Influenza Virus Inhibitors
- Combinatorial Libraries for Screening against Tuberculosis Inhibitors
Tuberculosis has become one of the most fatal diseases for which no completely successful chemotherapy has been developed so far. The emergence of drug resistance remains one of the most challenging issues in the treatment of Mycobacterium tuberculosis infection. In current scientific research, using computational methods to solve chemical and biological problems has been one of important area. Selection and validation of novel molecular targets have become of paramount importance in light of the plethora of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. With the help of variety of computational methods, traditional drug development has benefited a lot from the computational research. QSAR is increasingly gaining importance in the pharmaceutical industry as a cost-effective and timely strategy for introducing novel drug targets. This technique provides which ligand will be the potential inhibitor for a given receptor. In the present work, we combine ligand-based approach with receptor-based approach in building QSAR models by applying the conformation and alignment from a flexible docking simulation into PLS methods. This approach benefits in activity calculation of a large set of ligands to a known structure receptor in computer-aided rational drug design. The results demonstrated that the software Dock and MOE are the excellent tools for conformation generation and model development. PLS show the good statistical results (R2 = 0.91483) both in fitting and prediction process. The results demonstrate that combination of ligand-based and receptor-based modeling is a powerful approach to build 3D-QSAR models.