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In this work, we propose MEDICO, a Multi-viEw Deep generative model formolecule generation, structural optimization, and the SARS-CoV-2 InhibitordisCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graphgenerative model that can generate molecular graphs similar to the structure oftargeted molecules, with a multi-view representation learning framework tosufficiently and adaptively learn comprehensive structural semantics fromtargeted molecular topology and geometry. We show that our MEDICO significantlyoutperforms the state-of-the-art methods in generating valid, unique, and novelmolecules under benchmarking comparisons. In particular, we showcase themulti-view deep learning model enables us to generate not only the moleculesstructurally similar to the targeted molecules but also the molecules withdesired chemical properties, demonstrating the strong capability of our modelin exploring the chemical space deeply. Moreover, case study results ontargeted molecule generation for the SARS-CoV-2 main protease (Mpro) show thatby integrating molecule docking into our model as chemical priori, wesuccessfully generate new small molecules with desired drug-like properties forthe Mpro, potentially accelerating the de novo design of Covid-19 drugs.Further, we apply MEDICO to the structural optimization of three well-knownMpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in theirbinding affinity to Mpro, demonstrating the application value of our model forthe development of therapeutics for SARS-CoV-2 infection.

Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery