Novel Potential Inhibitors Against SARS-CoV-2 Using Artificial Intelligence
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Abstract Since known approved drugs like liponavir and ritonavir failed to cure SARS-CoV-2 infected patients, it is high time to generate new chemical entities against this virus. 3CL main protease acts as key enzyme for the growth of a virus which acts as biocatalyst and helps to break protein and ultimately in the growth of coronavirus. 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.
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