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

Document text

Principal Investigator: John Laurence Kulp III
Organization: CONIFER POINT PHARMACEUTICALS, LLC
Fiscal Year: 2020
Award: $225,000
Funding agency: National Institute of General Medical Sciences

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.

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