Artificial intelligence (AI) and machine learning (ML) techniques have been widely successful in a variety of structural biology applications, including protein structure prediction and acceleration of multiscale moleuclar simulations. In this talk, we highlight how we have been applying AI/ML methods to discover inhibitors against the novel coronavirus disease (COVID-19) causing agent, namely the severe acute respiratory coronavirus 2 (SARS-CoV-2). We describe how we have been using AI/ML methods: (1) in discovering novel inhibitors for the SARS-CoV-2 MPro, where our initial virtual screening, followed by AI-driven simulations provided insights into subsequent rounds of design, leading to improved affinity to the target protein, (2) in studying how two non-structural proteins (NSP-16 and NSP-10) associate with each other in the presence of various co-factors to provide novel insights into the drug-discovery process, and (3) modeling conformational transitions of the SARS-CoV-2 spike protein in the presence of the ACE2-receptor. We also discuss some potential pitfalls and challenges that we faced as part of our implementation and conclude with our perspective on how AI/ML methods could be truly beneficial for driving multiscale simulations with emerging supercomputing platforms.

Accelerating COVID-19 drug discovery with artificial intelligence (AI) techniques

ACS Spring Meeting 2021

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