Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: 1,000-fold improvement in IC50 (1.41 μM to 1 nM for NCATS-SM0707)

• Primary Outcome: Biochemical potency improvement of ALDH3A1 inhibitors

• Nnt Or Sensitivity: Cellular activity: 4 nM for NCATS-SM0708

• Confidence Interval: Not reported in abstract

Clinical Application

Early-stage research requiring further development including safety, selectivity, and pharmacokinetic studies Methodology could be applied by Australian pharmaceutical companies and research institutions. Future clinical development would require TGA approval pathway for novel therapeutics targeting ALDH3A1 Patients with cancers or neurodegenerative diseases where ALDH3A1 is implicated

Abstract

Developing potent, selective small-molecule inhibitors remains a major challenge in drug discovery. ALDH3A1, a detoxifying aldehyde dehydrogenase isoform implicated in cancer and neurodegeneration, is a promising yet underexplored therapeutic target. To accelerate inhibitor optimization, we developed an AI-guided, reaction-based hit-to-lead workflow combining sequential reaction enumeration, pharmacophore-informed docking, and predictive modeling to support scalable SAR expansion. Applied to ALDH3A1, two rounds of enumeration using Enamine building blocks generated about 250,000 virtual analogues. Combined deep learning and docking-based triage prioritized 150 compounds for synthesis, leading to a roughly 1,000-fold improvement in biochemical potency from 1.41 μM to 1 nM for NCATS-SM0707, together with 4 nM cellular activity for NCATS-SM0708. Crucially, this methodology can be expanded by applying various chemical reactions at different positions. This study highlights CSAR as a scalable, generalizable complementary strategy to accelerate hit optimization through reaction-based enumeration and AI-guided prioritization of larger synthetically accessible chemical space.

References

1. 1.Jain, S., Yasgar, A., Dalal, A., Nilova, A., Davies, M., Baljinnyam, B., Qu, Y., Denson, J. P., Esposito, D., Tao, D., Yang, S. M., Talley, D. C., Simeonov, A., Martinez, N. J., Rai, G., & Zakharov, A. V. (2026). Integrating AI in medicinal chemistry for accelerated drug discovery: A comprehensive SAR (CSAR) optimization strategy and discovery of potent ALDH3A1 inhibitors. Journal of Medicinal Chemistry. https://doi.org/10.1021/acs.jmedchem.6c00537