A Web Service for Fragment-based Selectivity Analysis of Drug Leads

Abstract Significance: To date, no specific therapeutic drug or vaccine has been approved for the treatment of human coronavirus. Better, direct-acting anti-viral drugs and accelerated methods for identifying them are desperately needed. Having a large body of diverse fragment binding simulation data for each SARS-CoV-2 drug target represents a unique opportunity to accelerate preclinical drug discovery for SARS-CoV-2 protein inhibitors. In contrast to testing-based approaches, understanding fragment interaction patterns provides chemists specific mechanistic information to guide lead optimization. We propose to (1) create comprehensive fragment maps for the full suite of SARS-CoV-2 proteins; (2) build automated tools for enumeration and evaluation of compounds that address protease selectivity and inhibition at Spike protein ppi and allosteric sites; and (3) make these available worldwide through the BMaps Web application. As such, all anti-viral researchers can benefit. Innovation: Generating thousands of fragment binding patterns for each of the known SARS-CoV-2 protein structures is a novel scientific approach to the rational design of SARS-CoV-2 antivirals. This would be the largest data source of fragment data on SARS-CoV-2 drug targets available and the resource would be accessible by all scientists working to address the COVID-19 pandemic. The innovation proposed is to enable a new scientific approach to rational design for SARS-CoV-2 antivirals based on the analysis of fragment binding patterns using novel compound enumeration and evaluation methods. Aim 1: Generate fragment and water maps for the full suite of proteins involved in the coronavirus life cycle. Using hot spots for location bias, run ~1,000 fragment simulations on each consensus of 6 structures from molecular dynamics. Aim 2: Develop automated tools to accelerate the enumeration and evaluation of candidate inhibitor molecules. Two approaches are proposed: (1) adapt our test software to enumerate all available modifications with all fragments for a given starting point and (2) use a Conditional GAN (Generative Adversarial Network) deep learning network to enumerate inhibitors from fragments, using discriminator networks to bias towards synthesizable molecules with good properties. Aim 3. Build a repository of candidate inhibitors targeting coronavirus proteins through a variety of different mechanisms. Overall Impact: The SARS-CoV-2 protein-fragment maps lead chemists to often non-obvious ideas to progress their compounds toward clinical trials. The ability to automatically enumerate and evaluate compounds from a large fragment map repository enables broad access to target-relevant chemical diversity, without tedious manual searching. A repository of candidate inhibitors targeting coronavirus proteins enables drug researchers to get started quickly.Project Narrative Accessing diverse chemical fragment binding data (1,000s for each SARS-CoV-2 drug target) presents a unique opportunity to accelerate much needed preclinical drug discovery for SARS-CoV-2 inhibitors. The fragment-based drug design platform (BMaps, www.boltzmannmaps.com), built under NIH Phase I and II grants, will be extended with fragment maps for SARS-CoV-2 proteins and further automation of compound enumeration through fragment growing. This will empower antiviral drug researchers in attacking the difficult problems of SARS-CoV-2 protease selectivity and inhibitor binding at allosteric or protein-protein interaction sites on the SARS-CoV-2 S protein.