Problem Lists With Toolkit Videos for Drug Discovery Hackathon

Sometimes it is very difficult to find resources in one place when a high level of competition is there. Drug Discovery Hackathon from the Government of India is providing multiple toolkits to better understand the problem statements and their application.

Here you can find the list of Problem Statements with video toolkit:

T1 – COVID Specific Drug Discovery:

• Developing a regression-based model for screening compounds with 3CLpro inhibitory activity

• Fragment-based de novo design of exemplar inhibitor against SARS-CoV-2 spike glycoprotein

• Screening of medicinal plants compound datasets against multiple targets of SARS-COV-2

• Designing newer quinoline analogues by performing molecular docking analysis of hydroxychloroquine and other quinolone agents against Spike- ACE2 complex, Transmembrane protease serine 2 and Spike protein of SARS-CoV-2

• Development of nucleotide analogs library by performing virtual screening using molecular docking methodologies at the active site of RNA dependent RNA polymerase enzyme of SAS-COV-2

• Identifying covalent spike protein inhibitors to control the infection of SARS-CoV-2 from small molecule databases

• Computational screening to identify lead drug candidates binding to NSP12 [RNA dependent RNA polymerase (RdRp)] from SARS CoV-2

• Computational screening of synthetic/natural compound databases to identify Furin enzyme inhibitors and in silico prediction of their toxicity potential in host

• Targeting the N- and C-terminal of nucleocapsid protein of SARS-CoV-2 for identification of small molecules through virtual screening

• To design small molecules binding to glycan groups on the Spike protein on the surface of SARS-Cov2

• Construction of homology models of wild and mutant D614G spike protein

• Identification and characterization of potent inhibitors by exploring the interaction interfaces between Envelope protein of SARS-CoV-2 and host protein –H2A and BRD2/4 (Dr.Shailendra Asthana)

• Probing helminth proteome for anti-hypertensive small natural peptides (small peptide of <2000 kDa) inhibitor for ACE2/ Cathepsin L to inhibit SARS-CoV-2 entry

• Building a complete structural model for SARS-CoV-2

T2 – General Drug Discovery, Including COVID

• Develop a reinforcement learning-based algorithm to identify lead molecules by emulating ligand-protein interactions

• Machine learning models to prioritize optimal parameters of predicted ADME and Toxicity data