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arXiv · 2608.11483

A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization

Kelvin P. Idanwekhai·Enes Kelestemur·Benjamin Strickland·Matthew Hart·Steini Davidsson·Angelos Angelopoulos·Ron Alterovitz·Marcello DeLuca·Alexander Tropsha
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Abstract

Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We present SABLE (Synthetically-accessible Agentic Bayesian Ligand Exploration), an open-source framework that employs natural-language orchestration to guide chemical structure optimization. SABLE uses an LLM to interpret user-defined goals and route tasks, while specialized tools perform reaction-templated analog enumeration, physicochemical and ADMET property prediction, structure-based affinity scoring, and Bayesian optimization. The resulting workflow is a computational twin of the analytical and prioritization stages of the design-make-test-analyze cycle, providing provenance of each numerical output. Across single, and multi-objective optimization studies, SABLE enriches candidate sets for user-defined computational objectives while evaluating only a subset of the enumerated search space. Its modular architecture allows tools and characterization backends to be replaced by editing a simple config file, without modifying operational logic. SABLE provides an extensible decision-support framework for prioritizing synthetically constrained analogs in early-stage drug discovery.

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BibTeXRIS

Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha. 2026-08-11. A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization. https://arxiv.org/abs/2608.11483

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