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
Discovery of important substructures from chemical compounds is very important as the substructure of a chemical compound has a key role in its pharmacological activity. By finding common and important substructures across a group of chemical compounds, relationship between chemical structure and biological function can be understood well. In the case of drug discovery, the structures of the chemical compounds tested and their effectiveness would be the raw data, while molecular structures that appear commonly in effective compounds would be patterns. Patterns (substructures) can be used to make informed guesses about which compounds should be effective and which probably will not be. DrugBank database is a unique resource that combines detailed drug i.e. chemical, pharmacological and pharmaceutical data. So, Data was downloaded from DrugBank. Three programs developed in perl were used to mine and cluster useful information. 2D representations of structures (SMILES) were also extracted with disease information of drugs. SMILES were then converted to Structures using Chemfinder for excel. From clustered drugs, relation between various types of functional group to disease was studied. After analyses of results, it could be concluded that some frequent substructures or functional groups can be active for biological efficacy and others may not. Aniline group was found to be inactive type because they were not efficient for any particular kind of disease. Other functional groups like piperazine, antibody, nitrile, amide, amine, alcohol can be active type as they were found to be present in specific common disease’s drugs & responsible for biological activity of those drugs. After knowing that one particular substructure is responsible for ability of a drug in inhibition of a specific disease and another drug for the same disease is to be made then we can go to trial of chemical compounds having that particular substructure responsible for biological efficacy.