  In this work, we propose MEDICO, a Multi-viEw Deep generative model for
molecule generation, structural optimization, and the SARS-CoV-2 Inhibitor
disCOvery. To the best of our knowledge, MEDICO is the first-of-this-kind graph
generative model that can generate molecular graphs similar to the structure of
targeted molecules, with a multi-view representation learning framework to
sufficiently and adaptively learn comprehensive structural semantics from
targeted molecular topology and geometry. We show that our MEDICO significantly
outperforms the state-of-the-art methods in generating valid, unique, and novel
molecules under benchmarking comparisons. In particular, we showcase the
multi-view deep learning model enables us to generate not only the molecules
structurally similar to the targeted molecules but also the molecules with
desired chemical properties, demonstrating the strong capability of our model
in exploring the chemical space deeply. Moreover, case study results on
targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that
by integrating molecule docking into our model as chemical priori, we
successfully generate new small molecules with desired drug-like properties for
the Mpro, potentially accelerating the de novo design of Covid-19 drugs.
Further, we apply MEDICO to the structural optimization of three well-known
Mpro inhibitors (N3, 11a, and GC376) and achieve ~88% improvement in their
binding affinity to Mpro, demonstrating the application value of our model for
the development of therapeutics for SARS-CoV-2 infection.
