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REVIEW 3 major objections 5 minor 1 cited by

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

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MT-Mol claims a multi-agent LLM system with chemistry tools reaches state-of-the-art results on 17 of 23 molecular optimization tasks.

desk verdict A well-built multi-agent molecular optimization system, but its SOTA claim is invalid because the retrieval protocol appears to use the task oracle over the whole ZINC-250K database, blowing the 1K oracle budget. read the letter →

arxiv 2505.20820 v1 pith:2IZRMLLW submitted 2025-05-27 cs.AI

Hyomin Kim , Yunhui Jang , Sungsoo Ahn This is my paper · ORCID
classification cs.AI
keywords molecularoptimizationmulti-agentlanguagemodelstool-basedreasoningRDKitdescriptorsSMILESgenerationPMO-1KbenchmarkinterpretableLLMagents
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that a team of specialized large-language-model agents, equipped with off-the-shelf cheminformatics tools, can outperform dedicated molecular-optimization methods when the number of expensive property evaluations is limited to 1,000. The proposed system, MT-Mol, assigns five analyst agents to different families of RDKit descriptors, a scientist agent to propose molecules in SMILES notation, a verifier to check that each reasoning step actually matches the proposed structure, and a reviewer to critique the candidate with tool-computed evidence. On the 23-task PMO-1K benchmark the authors report the best top-10 AUC score on 17 tasks and a summed score of 15.42 against the previous best of 12.23. The case for caring is that the approach produces not only candidates but an interpretable, chemically grounded trail of why each candidate was made, which is rare among black-box optimization methods.

What carries the argument

The load-bearing object is the multi-agent feedback loop built on five analyst categories: structural descriptors, electronic and topological descriptors, fragment-based functional groups, chemical identifiers and representations, and miscellaneous descriptors. Each analyst agent selects task-relevant RDKit functions and returns their outputs; the scientist agent consumes those outputs together with the top-100 ranked reference molecules and a history of previous SMILES, and emits a new molecule with stepwise reasoning; the verifier enforces consistency between reasoning and structure; the reviewer supplies tool-grounded, stepwise critique. The loop iterates for a fixed number of rounds, and all agents communicate through JSON-formatted structured responses so that each step can be audited.

What would settle it

Inspect the retrieval step that produces the top-100 examples: if the PMO oracle is used to score the ZINC 250K database and the highest-scoring molecules are then inserted into the scientist prompt without charging those evaluations to the 1K budget, the benchmark comparison is no longer a fair 1K-call comparison. Re-running the benchmark with a retrieval set that is ranked by a function independent of the task oracle would settle the claim.

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Extended reading notes

Core claim

MT-Mol is presented as the first molecular-optimization framework that couples role-specialized LLM agents with a broad, categorized toolbox of 154 RDKit functions and a structured feedback loop. The scientist proposes a SMILES string and a stepwise rationale; the verifier parses each rationale step and rejects the candidate if the structure does not implement the stated feature; the reviewer then measures the candidate with the same tools and returns stepwise criticisms. The authors claim this loop is what lets the system reach state-of-the-art PMO-1K results, with particular gains on rediscovery and similarity tasks such as celecoxib_rediscovery (0.867) and mestranol_similarity (0.996), and they support the contribution with ablations showing that removing the analyst tools or the reviewer degrades performance.

Load-bearing premise

The argument depends on the assumption that the scores used to rank the reference molecules given to the scientist come from outside the 1,000-call oracle budget and do not leak the task objective.

Editorial extensions

If this is right

  • If the reported results hold, an LLM-only multi-agent system can beat reinforcement-learning and evolutionary baselines on most low-budget molecular optimization tasks.
  • Every generated molecule carries an auditable trail: the analyst tool outputs, the scientist's stepwise rationale, the verifier's consistency check, and the reviewer's critique.
  • The ablation results imply that the analyst agents' tool selection and the reviewer's feedback are the main drivers, not the backbone model alone.
  • Because agents read the natural-language objective, the method applies to tasks where the objective is partly qualitative, and the same pipeline could be re-targeted to new descriptions without retraining.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper does not pursue is to use the scientist-verifier-reviewer loop as mutation and crossover operators inside an evolutionary search, which could combine the interpretable feedback with the diversity of population-based methods.
  • The method spends many LLM calls per oracle call; if inference cost is far cheaper than measuring the property, the multi-agent overhead is a favorable trade, and its exact cost could be reported as a second budget axis.
  • The contribution of the top-100 reference set is not isolated in the ablations; substituting random or descriptor-only retrieval for score-ranked retrieval would quantify how much of the gain comes from the reference examples versus from the agent loop itself.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces MT-Mol, a multi-agent LLM framework for molecular optimization that combines role-specialized agents (analyst, scientist, verifier, reviewer) with 154 RDKit-based tools and stepwise, verifiable reasoning. The authors evaluate on the PMO-1K benchmark and claim state-of-the-art top-10 AUC on 17 of 23 tasks, with a summed score of 15.42 versus 12.23 for the previous best method, MOLLEO-D*. The central design is an iterative loop in which analyst agents select tools, the scientist proposes SMILES with structured reasoning, and verifier and reviewer agents provide corrective feedback. The manuscript also includes ablations showing performance drops when individual agents are removed, and it provides a link to anonymous source code.

Significance. If the evaluation were valid, the paper would make a useful contribution to LLM-based molecular optimization: the multi-agent decomposition with explicit reasoning verification and tool-grounded feedback is a sensible design, the use of 154 RDKit functions is concrete and reproducible, and the ablation study is internally consistent. However, the central SOTA claim depends critically on the top-100 retrieval step not using the task oracle. The manuscript provides strong evidence that the retrieval does use the oracle to pre-score ZINC 250K, which would violate the 1K oracle-call budget of the PMO-1K protocol and invalidate the comparison with all baselines. Because the headline result is the main contribution, the paper cannot be accepted in its current form.

major comments (3)
  1. [Section 3.1, Figure 1, Appendix B.2, Appendix E, Appendix G] The top-100 retrieval step is described only as 'sort by score,' while the scientist prompt in Appendix B.2 contains 'Top-100 Relevant SMILES Examples (SMILES, score)' and instructs the model to 'FAITHFULLY REFER TO THESE EXAMPLES.' Appendix E reports per-oracle min/max/mean/std statistics computed over ZINC 250K, and Appendix G states that 'ZINC250K incorporates molecule with property scores for molecular optimization task.' The only way to produce those statistics is to evaluate each task oracle on the entire ZINC 250K database before generation. This means the pipeline performs on the order of 250K oracle evaluations per task outside the reported 1K-call budget and hands the scientist the top-scoring reference molecules together with their oracle scores. The resulting top-10 AUC values in Table 1 are therefore not comparable to the baselines under the PMO-1K protocol, and the claimed state-of-the-art result is unsupported.
  2. [Section 4.1 and Appendix B.3] The manuscript never defines what 'score' means in the retrieval step. If the score is the task oracle, then the method consumes far more than 1K oracle calls per task. If the score is a non-oracle similarity, then the per-oracle statistics in Appendix E and the '(SMILES, score)' entries in the prompts still require oracle evaluations that the paper does not account for. The authors must provide a precise accounting of every oracle evaluation used to construct the retrieval set, and must show that the total number of oracle calls per task respects the PMO-1K budget, before any claim about sample efficiency can be evaluated.
  3. [Table 2 and Section 4.2] The ablations remove the analyst, verifier, and reviewer agents but always keep the oracle-scored top-100 examples in the scientist prompt. Therefore the observed performance drops measure only the marginal effect of those agents conditional on the leaked retrieval, not the contribution of tool-based reasoning under a fair benchmark. The ablation results cannot establish that the multi-agent tool loop is responsible for the reported performance unless the retrieval set is constructed without oracle information.
minor comments (5)
  1. [Abstract and Section 1] The sentence 'we show that our framework shows the state-of-the-art performance' is redundant and should be rephrased.
  2. [Section 4.1] The text refers to 'Mol-Agent' when describing the proposed method; this should be 'MT-Mol' for consistency.
  3. [Section 4.2 and Table 10] There are several typos: 'reviwer' should be 'reviewer,' 'aent' should be 'agent,' 'troglitazon_rediscovery' in Table 11 should match 'troglitazone_rediscovery' elsewhere, and the drd2 standard deviation '0.756±0410' appears to be missing a decimal point.
  4. [Appendix B.2] The placeholder '[top100 SMLIES]' should read '[top100 SMILES].'
  5. [Appendix A] The ordering of the tool tables is confusing: the text lists Table 9 before Table 8. Please renumber or reorder for readability.

Circularity Check

1 steps flagged · score 8.0 of 10

Oracle-ranked ZINC retrieval feeds the evaluation metric into generation, invalidating the PMO-1K SOTA claim.

  1. self definitional [Section 3.1 (Figure 1), Section 4 (Datasets), Appendix B.2 (scientist prompt), Appendix E (Table 11)]
    "Figure 1: 'Top-k data retrieving ... sort by score'; Section 4: 'We use the ZINC 250K (Sterling and Irwin, 2015) dataset to retrieve the top-100 reference molecules for the scientist agent’s prompt.'; Appendix B.2: 'Top-100 Relevant SMILES Examples (SMILES, score)' and 'YOU MUST FAITHFULLY REFER TO THESE EXAMPLES WHEN DESIGNING YOUR MOLECULE.'; Appendix E: 'Data statistics of ZINC 250k that we retrieved for each oracle.'"

    Ranking the ZINC 250K retrieval pool 'by score' requires evaluating the task oracle on all ~250K database molecules; Appendix E's per-oracle min/max/mean/std over ZINC 250K is only producible by such full-database oracle evaluation. Those oracle scores are then inserted into the scientist prompt as '(SMILES, score)' and the model is told to 'faithfully refer' to the examples. The final top-10 AUC in Table 1 is computed with the same task oracle. The evaluation metric is therefore also the retrieval signal: the reported PMO-1K result is not an independent measure of generation under the stated 1K oracle-call budget but an artifact of oracle-labeled lookup.

full rationale

MT-MOL's agent architecture, RDKit tool selection, verifier, and reviewer are methodologically independent, and I found no load-bearing self-citation or imported uniqueness claims. The central circularity is in the benchmark setup. Figure 1 sorts the retrieval pool 'by score'; Appendix B.2 places the resulting '(SMILES, score)' examples in the scientist prompt and instructs the model to 'faithfully refer' to them; Appendix E reports per-oracle min/max/mean/std over ZINC 250K, which can only be obtained by evaluating each task oracle on all ~250K database molecules; Appendix G confirms that 'ZINC250K incorporates molecule with property scores for molecular optimization task.' The top-10 AUC in Table 1 is then computed with the same oracle. Thus the evaluation function's values are an input to the generator and its output is evaluated by the same function, making the reported 'state-of-the-art under 1K oracle calls' score a product of oracle-labeled retrieval rather than an independent test of the multi-agent method. This is a self-referential use of the target function, and the central claim reduces to this lookup step. No other circular steps were identified.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim is empirical. The main ledger item is the opaque retrieval score; if it is the task oracle, the method effectively uses precomputed oracle values as input, which would be a free parameter of the evaluation. The framework also has unreported loop counts that affect results.

free parameters (3)
  • outer_iterations_N
    The generation-review loop repeats until a maximum number of iterations N is reached; N is not reported, and results depend on it.
  • top_k_retrieval = 100
    The scientist prompt is built from the top-100 ZINC molecules sorted by an unreported score; this choice strongly affects which molecules are proposed.
  • num_reasoning_steps_k = 3
    The scientist and verifier use three fixed reasoning steps; an architecture choice that shapes the prompts and feedback.
assumptions (3)
  • domain assumption The PMO-1K benchmark oracles are valid proxies for the stated drug-discovery objectives.
    The paper takes AUC top-10 under 1K oracle calls as the evaluation protocol from Gao et al. (2022) and Nguyen and Grover (2025).
  • domain assumption ZINC 250K is a suitable starting library and the top-100 retrieval does not leak optimization signal.
    The paper retrieves top-100 references from ZINC but never specifies the scoring function for retrieval; this is the load-bearing assumption behind the SOTA claim.
  • domain assumption RDKit descriptors provide accurate and task-relevant chemical features.
    All analyst and reviewer feedback is computed from RDKit tool outputs, so the quality of these descriptors underlies the interpretability and guidance.

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Cite this review

Pith. "Pith review of MT-Mol:Multi Agent System with Tool-based Reasoning for Molecular Optimization." pith.science (2026). https://pith.science/paper/2IZRMLLW

@misc{pith2026250520820,
  author       = {Pith},
  title        = {Pith review of: MT-Mol:Multi Agent System with Tool-based Reasoning for Molecular Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2IZRMLLW}},
  note         = {Machine review of arXiv:2505.20820}
}
read the original 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 17 out of 23 tasks.

Figures

Figures reproduced from arXiv: 2505.20820 by the authors.

Figure 1
Figure 1. Overview of our method. Given a molecular optimization task, analyst agents analyze the prompt and outputs list of relevant RDKit functions from five categories. Top-k molecules are retrieved as reference molecules for the scientist agent. Then, the scientist agent proposes a SMILES with stepwise reasoning, which the double checker validates for consistency. The reviewer finally assesses the reasoning using tool-inf… view at source ↗
Figure 2
Figure 2. Example of analyst agents. Example case of five analyst agents analyzing the SMILES proposed by the scientist agent for the fexofenadine_mpo task. Each analyst agent chooses task-relevant tools: electronical and topological descriptors, miscellaneous descriptors, identifiers and representations, structural descriptors, and functional groups. The molecules at the bottom visualizes how analyst agents analyze the scien… view at source ↗
Figure 3
Figure 3. Examples of structured and stepwise response. The figures illustrate examples of structured feedback mechanisms employed by our agent system for the mestranol_similarity task. (a) The verifier flags a mismatch between reasoning and SMILES and the scientist revises both for consistency. (b) The reviewer suggests reducing rotatable bonds and the scientist reflects the design, improving the score. SMILES based on the f… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Top-10 AUC curves. Top-10 average AUC curves on the PMO benchmark, averaged over three random seeds. Our method consistently surpasses MOLLEO by achieving higher and faster-rising AUC curves, highlighting the effectiveness of tool-guided reasoning and multi-agent feedb…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Modularity of Agentic Systems for Drug Discovery

    cs.LG 2025-06 conditional novelty 4.0 of 10

    On 26 chemistry questions, swapping the LLM, agent type, or prompt in an LLM agent changes its scores so much that the system cannot be treated as modular.

Reference graph

Works this paper leans on

29 extracted references · 26 canonical work pages · cited by 1 Pith paper

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    Previous SMILES string

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    amlodipine_mpo Generate molecules similar to amlodipine with good drug-like properties (e.g., 3-ring topology)

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    ‘json {{

    Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. Yixin Liu, AR Fabbri, P Liu, D Radev, and A Cohan. On learning to summarize with large language models as references (2024). North American Chapter of the Association for Computational Linguistics. Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A Smith. 2021. Ex...

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    Avoid forbidden substructures and stay below similarity cap

    deco_hop Modify the decorations of a molecule while preserving a fixed scaffold. Avoid forbidden substructures and stay below similarity cap

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    ‘json {{

    Detected functional groups in your previous molecule — MOLECULE SMILES TO IMPROVE — MOLECULE SMILES: [previous smiles] - Task score: [score] (0–1) - Functional groups detected: [functional groups] — YOUR PREVIOUS THOUGHT AND REVIEWER’S FEEDBACK — Step1: List Key Features Your previous thought process: [scientist step1 reasoning] Accordingly, reviewer’s fe...

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    albuterol_similarity Design a molecule similar to albuterol while preserving key functional groups

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    gsk3b Design molecules predicted to have high binding affinity for the GSK3β protein

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    celecoxib_rediscovery Recreate the anti-inflammatory drug celecoxib

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    isomers_c9h10n2o2pf2cl Generate an exact isomer of C9H10N2O2PF2Cl

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    drd2 Generate molecules predicted to strongly bind to the dopamine D2 receptor using a predictive model

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    fexofenadine_mpo Create molecules structurally similar to fexofe- nadine with TPSA ≈ 90 and logP ≈ 4

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    median2 Design a molecule similar to both tadalafil and sildenafil

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    Must match the molecular formula exactly

    isomers_c7h8n2o2 Generate any molecule that is an exact isomer of C7H8N2O2. Must match the molecular formula exactly

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    osimertinib_mpo Create osimertinib-like molecules with low logP (≈1) and TPSA ≈ 100

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    jnk3 Design molecules with high predicted inhibitory activity against the JNK3 protein

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    median1 Find a molecule similar to both camphor and menthol

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    ranolazine_mpo Create ranolazine-like molecules with TPSA ≈ 95 and logP ≈ 7

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    mestranol_similarity Generate molecules similar to the hormone mestranol, preserving the core scaffold

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    sitagliptin_mpo Design sitagliptin-like molecules matching formula C16H15F6N5O

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    perindopril_mpo Design perindopril-like molecules

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    qed Maximize a quantitative estimate of drug- likeness (QED) score

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    valsartan_smarts Generate molecules containing the substructure SMARTS with logP ≈ 2.0 and TPSA ≈ 95

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    scaffold_hop Replace the molecular scaffold while keeping key functional groups unchanged

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    thiothixene_rediscovery Reproduce the structure of thiothixene

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    troglitazone_rediscovery Reconstruct the diabetes drug troglitazone

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    26 D PMO-1K experiment result We provide the full PMO-1K experiment result in Table 10

    zaleplon_mpo Design zaleplon-like molecules with formula C19H17N3O2. 26 D PMO-1K experiment result We provide the full PMO-1K experiment result in Table 10. E ZINC 250K statistics We provide the data statistics of ZINC250K (Ster- ling and Irwin, 2015) that we used in our setti...

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    Also, RDKit tools are used to an- alyze the chemical properties of the given molecule which is used in our study

    incorporates molecule with property scores for molecular optimization task which aligns with goal of our study. Also, RDKit tools are used to an- alyze the chemical properties of the given molecule which is used in our study. 27 Task GP BO REINVENT LICO -L Genetic GFN Graph GA...

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Reviewed August 7, 2026 · model on record in the stance chip above.