# Fragment-to-lead and target validation

> **NIH NIH U19** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $8,477,219

## Abstract

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.

## Key facts

- **NIH application ID:** 10513873
- **Project number:** 1U19AI171399-01
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** John Damon Chodera
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $8,477,219
- **Award type:** 1
- **Project period:** 2022-05-16 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10513873

## Citation

> US National Institutes of Health, RePORTER application 10513873, Fragment-to-lead and target validation (1U19AI171399-01). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10513873. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*