MT -Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025)

Abstract: Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refine molecular candidates. However, this potential remains underexplored, particularly in the context of structured reasoning, interpretability, and comprehensive tool-grounded molecular optimization. To address this gap, we introduce MT-Mol, a multi-agent framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. Our system incorporates comprehensive RDKit tools, categorized into five distinct domains: structural descriptors, electronic and topological features, fragment-based functional groups, molecular representations, and miscellaneous chemical properties. Each category is managed by an expert analyst agent, responsible for extracting task-relevant tools and enabling interpretable, chemically grounded feedback. MT-Mol produces molecules with tool-aligned and stepwise reasoning through the interaction between the analyst agents, a molecule-generating scientist, a reasoning-output verifier, and a reviewer agent. As a result, we show that our framework shows the state-of-the-art performance of the PMO-1K benchmark on 15 out of 23 tasks and outperforms LLM baselines on ChemCoTBench benchmark.

## introduction large language models (llms) have demonstrated remarkable capabilities in a wide range of problems such as question answering @xcite , summarization @xcite , translation @xcite , and code generation @xcite using large-scale pretraining and incontext learning @xcite @xcite . motivated by the success, researchers are investigating the potential of llms for scientific discovery in the chemical domain @xcite @xcite @xcite . in particular, employing llms to design new molecules (e.g., drug candidates), is promising due to several advantages: (1) llms exhibit general understanding and reasoning capabilities obtained from large-scale pretraining, (2) they can use the off-the-shelf tools for analyzing molecules, and (3) they are capable of interact with other agents to further improve the design candidate. recent studies have explored the application of llms in molecular optimization. for example, @xcite extends llms with embedding layers and in-context examples to build a surrogate modeling framework for molecular optimization. molleo @xcite leverages llms as mutation and crossover operators within an evolutionary algorithm. chem-crow @xcite integrates llms with chemical tools for to faciliate synthesis planning and molecular analysis. while these approaches demonstrate encouraging results, we argue that they do not fully exploit the broader capabilities of modern llms such as multi-agent collaboration, tool integration, and iterative reasoning, which are essential for high-quality molecular optimization. contribution. in this work, we propose mt-mol, a multi-agent framework for molecular optimization. our key idea is to decompose the optimization process into four distinct roles (analyst, scientist, verifier, and reviewer) and employ specialized agent for each role. unlike previous approaches, mt-mol generates molecules with explicit stepwise reasoning, consistency checks, and tool-informed feedback. furthermore, we collect a set of 154 chemistry-related functions, which serve as applicable tools for agents during molecular generation process. to be specific, we introduce four agents: (1) analyst, (2) scientist, (3) verifier, and (4) reviewer. in detail, five analyst agents proposes the taskspecific relevant tools using different types of chemical functions: structure, electronic properties, functional groups, identifiers, and miscellaneous descriptors. then a scientist agent proposes new molecules in smiles format @xcite and explains each design step through structured reasoning. next, a verifier agent evaluates whether the reasoning of the scientist is consistent with the proposed molecule. finally, a reviewer agent assesses both the molecule and the reasoning process using the outputs from the tools and provides detailed feedback. each agent plays a collaborative role that enables interpretable, tool-aware, and iterative molecular design. by incorporating domainspecific tools such as rdkit @xcite , mt-mol supports chemically informed generation and transparent decision-making. in summary, we propose a multi-agent framework for molecular optimization, where each agent is assigned a specific role such as tool selection, molecule generation, consistency validation, and reasoning critique. our system integrates 154 rd-kit functions, organized into five specialized analyst agents covering structural descriptors, electronic and topological descriptors, structural descriptors, fragment-based analysis, and identifiers or representations. we achieve state-of-the-art performance on 15 out of 23 tasks from the pmo-1k benchmark, outperforming recent strong baselines including lico and molleo in terms of top-10 auc scores. additionally, our framework offers an interpretable reasoning pipeline in which each generated molecule is equipped with stepwise rationale, double-check verification, and tool-informed reviewer feedback. llms for molecular optimization. llms have recently been applied to molecular optimization tasks. lico (nguyen and grover, 2025) extends a pretrained llm with structured embeddings to model property functions without relying on natural language prompts. molleo @xcite uses llms as evolutionary operators, enabling coherent molecule generation across singleand multi-objective settings. prompt-molopt @xcite introduces prompt-based editing to optimize multiple properties in low-data regimes while preserving pharmacophores. drugassist @xcite fine-tunes an instruction-based llm on a curated chemistry dataset to support interactive, feedback-driven molecule design. @xcite combines general-purpose llms with chemistry tools and a react-based reasoning loop to automate generation, retrosynthesis, and property prediction. chemcotbench @xcite , a benchmark that evaluates step-by-step chemical reasoning in tasks like molecule optimization, reveals that current llms still have significant room for improvement in complex chemical reasoning. despite these advances, existing approaches often lack interpretability, structured collaboration among specialized agents, and a systematic feedback loop that enhances accurate molecule design. to address these limitations, our method introduces five expert analyst agents powered by rdkit (landrum, 2013) and a multi-agent feedback loop that ensures both accurate and interpretable molecular optimization. multi-agent llms. multi-agent llms have shown promise in collaborative reasoning and decomposed problem-solving. agentverse @xcite assigns agents to roles like recruitment and evaluation, leveraging specialization for better coordination. proagent @xcite enables agents to infer and adapt to teammates' strategies through communication history. self-adaptive multi-agent systems (nascimento et al., 2023) use a self-control loop to make agents responsive to dynamic environments. theory of mind for multi-agent collaboration @xcite enhances coordination by giving agents shared belief states and goal-tracking abilities. @xcite improves communication scalability via a shared message pool that standardizes agent interactions. while these frameworks contribute to multi-agent architectural design, they have overlooked domain-specific tool integration and have less focused on molecule optimization. our framework addresses this gap by tightly coupling expert analyst agents with reasoning roles to enable targeted, tool-informed molecular design. ## method in this section, we introduce our multi-agent framework for molecular optimization, coined mt-mol. in section 3.1, we first describe the overview of our system, which consists of four primary agent types: 1) analyst, 2) scientist, 3) verifier, and 4) reviewer agents. we describe details of the analysts in section 3.2, and stepwise reasoning and feedback process in section 3.3. ## experiments in this section, we evaluate the effectiveness of our multi-agent llm system for molecular optimization in low-budget settings. we conduct experiments on the practical molecular optimization (pmo)-1k benchmark, which contains 23 chemically diverse optimization tasks, ranging from rediscovery and scaffold hopping to multi-property objectives. our framework consists of expert analyst agents-each specialized in task decomposition, smiles generation, verification, and toolinformed feedback-that collaborate to produce interpretable and high-quality molecular optimization. we compare our results against existing llmdriven and evolutionary baselines, including lico and molleo, using various backbone models. we describe the dataset and baselines below, followed by the experimental setting described in section 4.1. we then present the main benchmark results in baselines. in pmo-1k benchmark, we compare mt-molagainst six baselines: gp bo @xcite , @xcite , lico (nguyen and grover, 2025), and two variants of molleo @xcite (molleo-b, and molleo-d). molleo operates through llm-guided mutation and crossover, using different base models (biot5 @xcite and @xcite . we evaluated two versions of our framework (ours-d) using deepseek-v3 as a backbone for all the agent roles. in chemcotbench benchmark, we compare our framework against 22 baselines: (1) reasoning llms, including @xcite , o1-mini (openai team, 2024), o3-mini (openai team, 2025), gemini-2.5-pro (deepmind team, 2025), claude-3.7-sonnet-thinking (anthropic team, 2025), qwen-3-thinking (qwen team, 2025), and llama-nemotron-thinking @xcite , ( 2 ## conclusion in this paper, we introduced mt-mol, a multiagent framework for molecular optimization and generation that combines tool-guided reasoning with structured collaboration among specialized llm agents. our system integrates five expert analyst agents, each equipped with domain-specific chemistry functions, to guide and critique molecule design. through systematic interaction among scientist, verifier, and reviewer agents, mt-mol achieves interpretable, chemically valid, and taskaligned molecular optimization. our experiments on the pmo-1k benchmark demonstrate that mt-mol outperforms strong baselines, including lico and molleo, achieving state-of-theart performance on 15 out of 23 tasks. furthermore, evaluations on the chemcotbench benchmark confirm our framework's superiority, where it significantly outperforms 22 strong large language model baselines. the results highlight the effectiveness of structured reasoning, tool-based validation, and multi-agent feedback in navigating the complex chemical space. this work provides the multiagent system with comprehensive and systematic tool-augmented responses, accelerating molecular optimization and enabling transparent scientific discovery.