# Synthesizability-constrained expansion and multi-objective evolution of antitubercular compounds

> **NIH NIH R21** · RBHS-NEW JERSEY MEDICAL SCHOOL · 2022 · $197,349

## Abstract

PROJECT SUMMARY
New approaches are urgently needed to advance new chemical entities as antitubercular agents of clinical
relevance. A key hurdle is the multiple criteria process that constitutes hit-to-lead optimization of small molecules
with demonstrated in vitro potency versus drug-sensitive and drug-resistant strains of the causative bacterium
Mycobacterium tuberculosis (Mtb). This project will address the design, construction, and validation of novel
machine learning approaches to predict two of these critical molecular properties: in vitro Mtb growth inhibition
and mouse pharmacokinetic exposure in the plasma. The resulting models, validated through external test
statistics, will be complemented by computational approaches, relying on expert-encoded reaction templates
and/or learned reaction prediction models, to predict optimal synthetic routes to two promising antitubercular
small molecules: JSF-3005 and CD117. These data will inform the enumeration of synthesizable analogs for hit
expansion to be followed by the selection of candidate analogs scored with a multi-objective Pareto optimization
criterion combining multiple surrogate QSAR models with or without docking scores. Optimality scores will guide
a genetic algorithm for synthesizability-constrained molecular optimization as a replacement for exhaustive
forward enumeration. The top-scoring candidate lead compounds in each series will be then assayed for critical
molecular properties to validate this novel approach and supply novel antitubercular agents for further study
outside the scope of this proposal.

## Key facts

- **NIH application ID:** 10430402
- **Project number:** 1R21AI169342-01
- **Recipient organization:** RBHS-NEW JERSEY MEDICAL SCHOOL
- **Principal Investigator:** Connor Wilson Coley
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $197,349
- **Award type:** 1
- **Project period:** 2022-03-18 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430402

## Citation

> US National Institutes of Health, RePORTER application 10430402, Synthesizability-constrained expansion and multi-objective evolution of antitubercular compounds (1R21AI169342-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10430402. Licensed CC0.

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