An Auditable Agent Platform For Automated Molecular Optimisation

• URL: http://arxiv.org/abs/2508.03444v1
• Date: Tue, 05 Aug 2025 13:41:32 GMT
• Title: An Auditable Agent Platform For Automated Molecular Optimisation
• Authors: Atabey Ünlü, Phil Rohr, Ahmet Celebi,
• Abstract summary: Drug discovery frequently loses momentum when data, expertise, and tools are scattered.<n>To shorten this loop we built a hierarchical, tool using agent framework that automates molecular optimisation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Drug discovery frequently loses momentum when data, expertise, and tools are scattered, slowing design cycles. To shorten this loop we built a hierarchical, tool using agent framework that automates molecular optimisation. A Principal Researcher defines each objective, a Database agent retrieves target information, an AI Expert generates de novo scaffolds with a sequence to molecule deep learning model, a Medicinal Chemist edits them while invoking a docking tool, a Ranking agent scores the candidates, and a Scientific Critic polices the logic. Each tool call is summarised and stored causing the full reasoning path to remain inspectable. The agents communicate through concise provenance records that capture molecular lineage, to build auditable, molecule centered reasoning trajectories and reuse successful transformations via in context learning. Three cycle research loops were run against AKT1 protein using five large language models. After ranking the models by mean docking score, we ran 20 independent scale ups on the two top performers. We then compared the leading LLMs' binding affinity results across three configurations, LLM only, single agent, and multi agent. Our results reveal an architectural trade off, the multi agent setting excelled at focused binding optimization, improving average predicted binding affinity by 31%. In contrast, single agent runs generated molecules with superior drug like properties at the cost of less potent binding scores. Unguided LLM runs finished fastest, yet their lack of transparent tool signals left the validity of their reasoning paths unverified. These results show that test time scaling, focused feedback loops and provenance convert general purpose LLMs into auditable systems for molecular design, and suggest that extending the toolset to ADMET and selectivity predictors could push research workflows further along the discovery pipeline.

Related papers

• An LLM Driven Agent Framework for Automated Infrared Spectral Multi Task Reasoning [4.934622388454071]
  Large language models (LLMs) offer promising potential for complex scientific reasoning.<n>This study addresses the challenge of achieving accurate, automated infrared spectral interpretation under low-data conditions.<n>We introduce an end-to-end, large language model driven agent framework that integrates a structured literature knowledge base, automated spectral preprocessing, and multi task reasoning.
  arXiv  Detail & Related papers  (2025-07-29T03:20:51Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Large Language Model Agent for Modular Task Execution in Drug Discovery [7.1616715247845955]
  We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline.<n>By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation.
  arXiv  Detail & Related papers  (2025-06-26T00:19:01Z)
• Distilling LLM Agent into Small Models with Retrieval and Code Tools [57.61747522001781]
  Agent Distillation is a framework for transferring reasoning capability and task-solving behavior from large language models into small language models.<n>Our results show that sLMs as small as 0.5B, 1.5B, 3B parameters can achieve performance competitive with next-tier larger 1.5B, 3B, 7B models.
  arXiv  Detail & Related papers  (2025-05-23T08:20:15Z)
• DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery [54.79763887844838]
  Large language models (LLMs) integrated with autonomous agents hold significant potential for advancing scientific discovery through automated reasoning and task execution.<n>We introduce DrugPilot, a LLM-based agent system with a parameterized reasoning architecture designed for end-to-end scientific in drug discovery.<n>DrugPilot significantly outperforms state-of-the-art agents such as ReAct and LoT, achieving task completion rates of 98.0%, 93.5%, and 64.0% for simple, multi-tool, and multi-turn scenarios, respectively.
  arXiv  Detail & Related papers  (2025-05-20T05:18:15Z)
• MALT: Improving Reasoning with Multi-Agent LLM Training [66.9481561915524]
  MALT (Multi-Agent LLM Training) is a novel post-training strategy that divides the reasoning process into generation, verification, and refinement steps.<n>On MATH, GSM8K, and CSQA, MALT surpasses the same baseline LLM with a relative improvement of 15.66%, 7.42%, and 9.40% respectively.
  arXiv  Detail & Related papers  (2024-12-02T19:30:36Z)
• Fleet of Agents: Coordinated Problem Solving with Large Language Models [10.167121757937062]
  Fleet of Agents (FoA) is a principled framework utilizing large language models as agents to navigate through dynamic tree searches.<n>FoA spawns a multitude of agents, each exploring the search space autonomously, followed by a selection phase.<n>FoA achieves the best cost-quality trade-off among all benchmarked methods and FoA + LMA3.2-11B surpasses the Llama3.2-90B model.
  arXiv  Detail & Related papers  (2024-05-07T09:36:23Z)
• Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models [56.00992369295851]
  Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. This paper delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. We propose Agent-FLAN to effectively Fine-tune LANguage models for Agents.
  arXiv  Detail & Related papers  (2024-03-19T16:26:10Z)
• Learning to Use Tools via Cooperative and Interactive Agents [58.77710337157665]
  Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. We propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. Our experiments on three datasets show that the LLMs, when equipped with ConAgents, outperform baselines with substantial improvement.
  arXiv  Detail & Related papers  (2024-03-05T15:08:16Z)
• MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization [86.61052121715689]
  MatPlotAgent is a model-agnostic framework designed to automate scientific data visualization tasks. MatPlotBench is a high-quality benchmark consisting of 100 human-verified test cases.
  arXiv  Detail & Related papers  (2024-02-18T04:28:28Z)
• DrugAssist: A Large Language Model for Molecule Optimization [29.95488215594247]
  DrugAssist is an interactive molecule optimization model that performs optimization through human-machine dialogue. DrugAssist has achieved leading results in both single and multiple property optimization. We publicly release a large instruction-based dataset called MolOpt-Instructions for fine-tuning language models on molecule optimization tasks.
  arXiv  Detail & Related papers  (2023-12-28T10:46:56Z)

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.