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

• URL: http://arxiv.org/abs/2608.11483v1
• Date: Tue, 11 Aug 2026 22:50:56 GMT
• Title: A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
• Abstract summary: We present SABLE, an open-source framework to guide chemical structure optimization.<n>SABLE uses an LLM to interpret user-defined goals and route tasks.<n> specialized tools perform reaction-templated analog enumeration, physicochemical and ADMET property prediction, structure-based affinity scoring, and Bayesian optimization.
• Score: 32.444035880212404
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents [90.3279207163486]
  EvoSOP is a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset.<n>We show that EvoSOP significantly boosts task success rates while substantially reducing the number of interaction rounds.
  arXiv  Detail & Related papers  (2026-07-08T12:09:49Z)
• Design Structure Matrix Modularization with Large Language Models [5.897012511455557]
  Design Structure Matrix (DSM) modularization is a fundamental challenge in engineering design.<n>Traditional methods treat modularization as a pure graph optimization, without access to the engineering context embedded in the system.<n>This paper builds on prior work on LLM-based optimization for DSM sequencing.<n>Our method achieves near-reference quality within 30 iterations without requiring specialized optimization code.
  arXiv  Detail & Related papers  (2026-04-30T15:38:38Z)
• Execution-Verified Reinforcement Learning for Optimization Modeling [49.171122807323634]
  Execution-verified learning framework treats a mathematical programming solver as a deterministic, interactive verifier.<n>Experiments on NL4OPT, MAMO, IndustryOR, and OptiBench across Gurobi, OR-Tools, and COPT show that EVOM matches or outperforms process-supervised SFT.
  arXiv  Detail & Related papers  (2026-04-01T03:39:11Z)
• A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting [0.0]
  We introduce LLM-AutoOpt, a hybrid HPO framework that combines BO with LLM-based contextual reasoning.<n>We show that LLM-AutoOpt achieves improved predictive performance and more interpretable optimization behavior compared to BO and LLM baselines without meta-knowledge.
  arXiv  Detail & Related papers  (2026-02-01T21:26:57Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• Leveraging Discrete Function Decomposability for Scientific Design [48.365465744654365]
  In the era of AI-driven science and engineering, we often want to design objects in silico according to user-specified properties.<n>For example, we may wish to design a protein to bind its target, arrange components within a circuit to minimize latency, or find materials with certain properties.<n>We propose and demonstrate use of a new distributional optimization algorithm, De-Aware Distributional Optimization (DADO), that can leverage any decomposability defined by a junction tree on the design variables.
  arXiv  Detail & Related papers  (2025-11-04T21:57:51Z)
• SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations [25.18297372152296]
  SOCRATES is a novel two-stage procedure that automates the design of tailored SO algorithms.<n>An ensemble of digital replicas of the real system is used as a testbed to evaluate a set of baseline SO algorithms.<n>An LLM acts as a meta-optimizer, analyzing the performance trajectories of these algorithms to iteratively revise and compose a final, hybrid optimization schedule.
  arXiv  Detail & Related papers  (2025-11-01T19:57:38Z)
• REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving [6.19179006129561]
  We introduce a novel compilation framework (dubbed Reasoning) that formulates optimization as a sequential, context-aware decision process.<n>Our approach demonstrates the potential of LLM-guided reasoning to transform the landscape of compiler optimization.
  arXiv  Detail & Related papers  (2025-06-02T07:02:46Z)
• Efficient Model-Free Exploration in Low-Rank MDPs [76.87340323826945]
  Low-Rank Markov Decision Processes offer a simple, yet expressive framework for RL with function approximation. Existing algorithms are either (1) computationally intractable, or (2) reliant upon restrictive statistical assumptions. We propose the first provably sample-efficient algorithm for exploration in Low-Rank MDPs.
  arXiv  Detail & Related papers  (2023-07-08T15:41:48Z)
• Optimization for truss design using Bayesian optimization [1.5070398746522742]
  The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced stress. We rely on finite element analysis, which is a computationally costly design analysis tool for design evaluation.
  arXiv  Detail & Related papers  (2023-05-27T10:28:27Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.