Problem
Research questions and friction points this paper is trying to address.
hit-to-lead optimization
multi-objective optimization
synthetic accessibility
drug discovery
computational chemistry
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
🤖 AI Summary
This work addresses the intricate trade-offs in lead compound optimization among multiple objectives—including potency, selectivity, physicochemical properties, pharmacokinetics, and safety—while ensuring synthetic feasibility. To this end, we introduce SABLE, the first open-source, configurable multi-objective molecular optimization framework that supports natural language instructions and provides full traceability. SABLE leverages a large language model for task routing and integrates modules for reaction template enumeration, structural affinity scoring, ADMET prediction, and Bayesian optimization to emulate the design–make–test–analyze cycle. Experimental results demonstrate that SABLE efficiently enriches candidate molecules satisfying user-defined multi-objective constraints by evaluating only a fraction of the chemical space, thereby substantially accelerating early-stage drug discovery.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Modular Agentic Framework
LLM-guided Optimization
Reaction-templated Enumeration
Bayesian Multi-objective Optimization
Synthetically Constrained Drug Design
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