The York Lab project was among an elite set of 49 chosen to be awarded allocations on the new NSF-funded Leadership-class computer system designed to be used by the most experienced academic computational scientists in the nation. This recent allocation award provides the York Lab access to the world's most powerful academic supercomputer, Frontera (https://www.tacc.utexas.edu/-/frontera-sets-sights-on-transformative-research). As many experimental labs have been heavily constrained, if not shut down by the COVID-19 pandemic, there is urgent need to leverage computational methods for prediction and bring them to bear in these design efforts of potent and selective inhibitors for COVID-19. The access to supercomputer resource definitely accelerates the research for COVID-19 protease inhibitor design and tools to inform precision medicine therapies.

Besides, the York Lab at the Laboratory for Biomolecular Simulation Research at Rutgers, along with the RCSB Protein Data Bank, are part of the Rutgers COVID Research Alliance, an integral component of the Rutgers Center for COVID-19 Response and Pandemic Preparedness (CCRP2) and the RBHS Institute for Infectious and Inflammatory Diseases (I3D).  The York Lab specializes in the development of high-performance computational tools for drug discovery, and in particular, GPU-accelerated free energy methods in the AMBER molecular simulation software suite [J. Chem. Inf. Model. 58, 2043 (2018); 59, 3128 (2019)] used worldwide for prediction of ligand-protein binding affinities for lead optimization. We have recently made several breakthroughs that have enabled robust, accurate prediction of the relative ligand binding affinities of libraries of compounds to their protein targets.  Together with the rapidly growing body of structural and fragment screening data, we are engaged in lead refinement efforts of M^pro inhibitors, and the development of new stand-alone high-performance tools to inform precision medicine therapies.  We will fast-track these tools into the AMBER software package and make immediately available to a broad scientific community.

In very early stages, these tools and supercomputer resources will facilitate lead optimization of compounds that target M^pro inhibitors for COVID-19.  As new drugs enter into clinical trials, and as FDA approved drugs emerge that have activity against M^pro, the web service using new GPU-accelerated methods will enable rapid predictions to be made about the most effective combination therapies based on a particular virus strain (i.e., specific sequence of M^pro).