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Identifying new anti-cancer drugs by computational multi-target approaches targeting the G-quadruplex DNA
Aim: 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. 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. 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. The software 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 various in-silico and in-vitro assays.
Project tasks:
⦁ Chemical and biological data curation of the collected experimental data. A Comprehensive literature survey will be performed for identifying various ligand molecules along with their activity against various G4 motifs. Extensive data curation will be performed which will include checking & rectifying the errors in the chemical structure, exclusive handling of inorganic/organometallic/salts, normalization of the chemical structures, duplicate analysis, activity-cliff analysis etc
⦁ Development of multi-target QSAR models against different types of G4s. Depending on the type of collected response data, i.e. continuous and categorical, regression and classification-based QSAR models will be developed, respectively. Several descriptors will be computed using available in-house python script and other freely available software. The computed descriptors will include several classes such as constitutional, atom centered, connectivity indices, edge adjacency, electro-topological state, walk path counts, functional group, etc. As applicable, several linear and non-linear chemometric techniques will be employed to develop the models. The QSAR models will be evaluated using the standard protocol recommended by the OECD Guidelines.
⦁ Development of Artificial Intelligence (AI) based software tool, KNIME nodes and KNIME workflow schemes. We will build a user-friendly, platform-independent software tool which will utilize the knowledge gained from the modeling study as well as the developed QSAR models to screen, optimize and/or design MTDLs against G4.
⦁ Virtual screening using desirability-based multi-objective optimization, in silico and experimental evaluation of screened MTDLs. We will perform the virtual screening of big chemical space (databases such as, ZINC, Maybridge, DrugBank, InterBioScreen natural and Super Natural II, etc.), while employing desirability-based MOO approach. Screened ligands will be evaluated using molecular docking, MD simulations and key biophysical assays.
⦁ Communication, dissemination and IPR. Events will be organized to communicate main issues of the investigation for the general public and contacts with industrial associations and companies interested. Patents will be requested before dissemination of the results. Dissemination of the results to the scientist community will be carried out by means of the publication of papers and the participation in conferences and trade fairs related with the field of research.




Financing agency:

H2020-MSCA-IF-2020 (Marie Skłodowska-Curie Individual Fellowships)

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101029275