Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence

18 May 2020, Version 3
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro . Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2.

Keywords

COVID-19SARS-COV2, 3CL Protease, Structure-based optimization policy, Deep learning, Artificial intelligence, Reinforcement learning.

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