AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Tang, B.; He, F.; Liu, D.; Fang, M.; Wu, Z.; Xu, D.

doi:10.1101/2020.03.03.972133

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bioRxiv · 10.1101/2020.03.03.972133

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

Tang, B.·He, F.·Liu, D.·Fang, M.·Wu, Z.·Xu, D.

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Abstract

The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive for antiviral drug design. 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) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.

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Tang, B., He, F., Liu, D., Fang, M., Wu, Z., Xu, D.. 2020-03-08. AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. https://doi.org/10.1101/2020.03.03.972133

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