Fragment-to-lead and target validation

The discovery of an antiviral therapeutic requires a target that is robust to mutations, and suitable chemical matter that modulates the target. The Fragment-to-Lead and Target Validation project sits at the crucial interface between target validation and chemical lead generation. We aim to validate biological hypotheses behind target selection and produce lead molecules for downstream therapeutic development, producing leads against 9 antiviral targets. By tightly integrating the unique capabilities of extremely high-throughput X-ray crystallography at Diamond Light Source, this project leverages recent advances in artificial intelligence and machine learning (Al/ML) and exascale computing free energy calculations to rapidly generate novel potent lead compounds able to overcome resistance from initial X-ray fragment screens. This project builds on the successful COVID Moonshot initiative, which executed a rapid fragment-to-lead campaign against SARS-CoV-2 main protease - starting from fragment screen, a lead compound with IC50 = 140 nM was discovered in <6 months and <400 compounds made. In the first stage of a hit-to-lead campaign, we will use machine learning to learn pharmacophore features from high throughput fragment screen readout, and use these patterns to search for potent hits from virtual, synthetically accessible chemical space. The goal is to arrive at chemical matter which engages the viral protein with antiviral activity, which in turn enables experiments that validate the target. Working with Project 1 (Antiviral Targeting to Suppress Resistance), potent hits will be used to validate biological hypotheses of target engagement, and Deep Mutational Scanning and serial passaging will be used to evaluate the barrier to resistance and the mutations that give rise to resistance. These insights will be used in the iterative medicinal chemistry design process, in selecting chemical series with less resistance potential and focusing on expanding into vectors that target mutationally robust residues. In the second stage of the hit-to-lead campaign, we will build on the wealth of structural and bioactivity data generated in the first stage and use machine learning, alchemical free energy calculations, and high throughput nanomole chemistry to rapidly evaluate and synthesize analogues which expand into promising vectors. The goal of this phase is to arrive at: (i) potent inhibitor in biochemical assays with IC50<500 nM; (ii) inhibition in cellular antiviral assays with EC50<3μM and cytotoxicity CC50>50 μM; and (iii) developable Tier 1 ADME and physicochemical properties: clog P<4, kinetic solubility > 50 μM, rat and human microsomal stability Clint<50, MDCK-LE permeability Papp>1x108cm/s. These leads are inputs to Lead Optimization (Project 5), which will focus on further improvements in potency, ADMET and in vivo pharmacokinetics.