The goal of the proposed research is to develop computational methodology which can screen out small ligand molecules having potential to selectively target G-quadruplex (G4) DNA which are associated with cancer pathology. Multiple oncogenes and telomerase activity are deregulated in various types of cancers. Ligand induced stabilization of G4s associated with the oncogenes (c-myc, c-kit, k-ras, etc.) and stabilization of telomeric G4s are efficient ways in targeted cancer therapy. Here, we intend to develop multi-target QSAR models that can aid in finding potential multi-target directed ligands (MTDLs) that can stabilize multiple G4s from different oncogenes simultaneously. It is the first computational study for identifying MTDLs against multiple G4s in Cancer treatment. Different multi-target QSAR approaches will be explored including ‘multiple regression and classification’ QSAR models, multi-target QSAR using Box-Jenkins moving average approach and multi-target QSAR using Perturbation approach. Several advanced machine learning techniques will be employed. A state-of-the-art software tool will be developed where all the successful multi-target QSAR models and in-house ADMET models will be incorporated as a knowledgebase. Notably, desirability-based multi-objective optimization approach will be used for identifying drug-like molecules. The software along with in-house KNIME workflows will then be used to screen potential MTDLs against multiple G4s. The selectivity and binding characteristics of the screened MTDLs towards G4s over duplex DNA will be analysed by performing molecular docking and molecular dynamics studies. The binding capacity of the screened MTDLs with intended G4s over duplex DNAs will then be confirmed using experiments such as isothermal fluorescence, UV-Vis, CD spectroscopies and FRET melting assay. The findings will aid in identifying new leads and reducing false positive outcomes in the crucial early stages of drug discovery and development.
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