A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
Summary
This paper presents SABLE, an open-source agentic framework that uses natural-language orchestration with LLMs to guide synthetically constrained multi-objective hit-to-lead optimization in drug discovery, integrating reaction-templated enumeration, property prediction, and Bayesian optimization.
View Cached Full Text
Cached at: 08/13/26, 03:26 PM
# A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization Source: [https://arxiv.org/abs/2608.11483](https://arxiv.org/abs/2608.11483) [View PDF](https://arxiv.org/pdf/2608.11483) > 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\. ## Submission history From: Kelvin Idanwekhai \[[view email](https://arxiv.org/show-email/a0b9dd36/2608.11483)\] **\[v1\]**Tue, 11 Aug 2026 22:50:56 UTC \(6,580 KB\)
Similar Articles
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
This paper introduces agentic Bayesian optimization, where an LLM agent acts as the central decision-maker in the BO loop with a Bayesian backend, enabling online strategy revision and problem reframing. The authors instantiate this in Sara and lenz, demonstrating reliability and performance gains over standard BO and LLM-based baselines.
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
The paper extends the LLaMEA framework to automatically design multi-objective Bayesian optimization algorithms using large language models as mutation and crossover operators within evolutionary strategies, achieving state-of-the-art accuracy with significantly lower computational cost on synthetic and real-world problems.
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol is an evolutionary agentic framework that combines a multi-objective genetic algorithm with an LLM-based operator to design small-molecule drugs, achieving state-of-the-art binding affinity and drug-likeness on multiple protein targets.
Molecular Lead Optimization via Agentic Tool Planning
TRACE is a trajectory-aware LLM agent for molecular lead optimization that uses sequential decision-making over molecular optimization tools, achieving improved ADMET properties while preserving molecular similarity.
EASy: Towards Efficient LLM-Based Agentic System
The paper proposes EASy, a trainable agentic framework that uses reinforcement learning to jointly optimize task performance and computational efficiency, introducing a milestone-plan-act workflow, dependency-aware execution graphs, and tree-structured rollout for training.