Submission Details
Molecule(s):
CC(C)(C)NC(=O)C1CC2CCCCC2CN1CC(OCC12CCCC1CS(=O)(=O)N2)c1cccc(C(F)(F)F)c1
COc1ccc2c(OC3CC4C(=O)Nc5cc(C(F)(F)F)ccc5C(=O)C4C3)cc(-n3cccn3)nc2c1
Cc1cccc(C)c1C(=O)NC(Cc1ccccc1)C(O)C(=O)N1CC2CC(C1)n1c2cccc1=O
CC(C)(C)NC(=O)C1CC2CCCCCC2CN1CC(O)C(Cc1ccccc1)NC(=O)c1ccccc1
Design Rationale:
The molecules were generated using AI-based deep neural network and further optimised using transfer and reinforcement learning using protease specific datasets. The generated molecules were filtered using various physicochemical filters and docking calculation (De Novo Design of New Chemical Entities (NCEs) for SARS-CoV-2 Using Artificial Intelligence, ChemRxiv, 2020). Based on our work another group has also performed in silico free energy calculation to further prioritise the molecules (Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods, ChemRxiv, 2020).
Other Notes:
The view fragments is not functional