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REVIEW 3 major objections 4 minor 25 references

A generate–verify–refine loop lifts exact molecule matches above one-shot baselines.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-03 06:16 UTC pith:ITOQZC5T

load-bearing objection A solid generator-verifier-refiner integration with a modest, verifier-mediated exact-match gain; the main result is plausible but not independently checkable until the verifier and artifacts are released. the 3 major comments →

arxiv 2607.29479 v1 pith:ITOQZC5T submitted 2026-07-31 cs.LG

MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation

Qian Tan , Xuanyu Zhu , Lei Jiang , Zhonghang Yuan , Chen Zhang , Yuqiang Li This is my paper
classification cs.LG
keywords text-to-molecule generationverificationerror correctionstructural constraintsSMILESexact matchreinforcement learningLLM-based generation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper argues that text-to-molecule generation should not end with a single decoded SMILES. It proposes a three-stage pipeline: a generator trained with structural reasoning and reinforcement learning proposes a candidate; a verifier turns the description into explicit chemical constraints and checks the candidate; a refiner revises candidates that fail, using the failure reason as feedback. On ChEBI-20 and PCDes this raises exact-match accuracy from 0.530 to 0.582 and 0.455 to 0.500, and the ablation shows the gain comes from targeted correction, not extra sampling. A sympathetic reader would care because it suggests that explicit verification and repair, not just bigger models, is a practical route to chemically faithful generation.

Core claim

MolGVR's central claim is that coupling a generator with an executable verifier and a failure-driven refiner converts one-shot text-to-molecule prediction into a process that catches and corrects structural constraint violations. The verifier extracts only explicitly stated, checkable constraints from the description, normalizes them to a closed vocabulary, and runs deterministic molecular checks; it rejects only expert-validated hard rules, so correct molecules are never filtered out in the reported settings. The refiner, trained first on generator errors and then on verifier-feedback instances, repairs roughly a fifth of rejected candidates, whereas five additional generator samples recove

What carries the argument

The verification-guided refinement loop: an LLM-based rule extractor maps each description to a small set of normalized, executable structural constraints, a deterministic checker evaluates each candidate against them, and a refiner is trained to correct the rejected candidates using the specific unmet constraint as input. This loop carries the argument because it localizes the failure, supplies a reason, and turns that reason into a repair target.

Load-bearing premise

The verifier's pass/fail decisions are treated as ground truth, because the 'no false rejections' claim and the refiner's training signal depend on an expert-validated set of rules and a closed vocabulary that are not released; if those labels are wrong, the refiner learns from incorrect feedback and the reported gains would shrink.

What would settle it

If the verifier's rejection labels are wrong or circular, the loop fails. A concrete experiment: hold out a sample of generated candidates, ask independent chemists to label each candidate against the description, then compare those labels to the verifier's pass/fail decisions. Alternatively, replace the verifier's rejections with random rejections at the same rate and check whether match still improves; if it does, the specific feedback is not what causes the gain.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Exact-match performance improves by about 10% relative on both benchmarks, and the improvement is attributable to selective correction rather than additional sampling.
  • The verifier-refiner loop transfers to other generators: applying it to a different chemistry model raises its match score and cuts its violation rate by roughly half or more.
  • Verification-guided refinement and multi-sample generation are complementary; pass@5 plus refinement yields further gains beyond either alone.
  • The category-wise analysis identifies where repair works (functional groups, invalid SMILES, stereochemistry, group counts) and where it falls short (scaffolds, salt/charge states), pointing to the error types that still limit exact recovery.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The concrete ceiling of the approach is the expressiveness of the constraint vocabulary; adding checkable categories such as positional attachment, fused ring systems, or tautomers would likely raise both rejection recall and repair rates without changing the architecture.
  • Because the refiner is supervised on generator-induced errors, the same loop could be applied iteratively (verifier → refiner → verifier) or during generator decoding, where the extracted rules are fed back into the generator's prompt to prevent violations before they occur.
  • The 'no false rejections' result is measured on these two popular benchmarks under the authors' expert-validated rule set; on noisier or more ambiguous descriptions a more permissive verifier might trade false rejections for higher recall, and the current conservative design may leave some repairable errors undetected.
  • A natural stress test: use the verifier at training time to shape the generator's reward, rather than only correcting at inference, to see whether earlier exposure to constraint feedback compounds the gains.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes MolGVR, a three-stage Generator–Verifier–Refiner framework for text-to-molecule generation. The Generator is an Intern-S1-mini model fine-tuned with MSR-style structural reasoning and GRPO reinforcement learning; the Verifier uses GPT-5.2 to extract conservative structural constraints from descriptions and executes them with RDKit-based checkers; the Refiner is an SFT-trained model that corrects verifier-rejected candidates using failure feedback. On ChEBI-20 and PCDes the authors report pass@1 Match scores of 0.582 and 0.500, respectively, which they state are the best among listed baselines. Ablations show the full V&R pipeline adds roughly +0.01 Match over the already strong generator, and that this gain comes from targeted correction of verifier-identified failures rather than additional sampling. The paper also reports category-wise violation/repair rates and inference-time costs.

Significance. If the reported results hold, MolGVR provides a useful demonstration that explicit, executable verification plus feedback-guided refinement can improve exact molecular match over one-shot generation, and that targeted correction is more effective per candidate than continued sampling. The work's strengths include a clean separation of generator improvements (MSR/RL) from verification/refinement gains, a carefully designed conservative rule-extraction prompt, error-category breakdowns, and an honest discussion of inference overhead. However, the central V&R gain rests on an unreleased expert-validated verifier that also generates the Refiner's training labels, and the pass@5 results use gold labels for candidate selection. These issues materially limit independent verification of the paper's main contribution as currently presented.

major comments (3)
  1. [Method (Verifier); Appendix B, D; Appendix C.2] The V&R gain is mediated entirely by the Verifier's pass/fail decisions: the 'empty feedback' Refiner collapses Match to 0.218 on ChEBI-20 (Table 3), while the full pipeline gains +0.011/+0.010 over the generator. Yet the Verifier is not released: the rule JSON, SMARTS patterns, checker code, and the 'expert validation' procedure are described but no artifact is provided, and Appendix C.2 shows the same Verifier labels the Refiner's second-stage training data. Thus the verifier is simultaneously the generator of training labels and the selector of test-time corrections, and the reported repair rates and gains cannot be independently confirmed. Please release the full verifier rule set and checker implementation, or clearly frame the V&R results as conditional on an unreleased component.
  2. [Appendix C.3, Eq. (22); Table 3 pass@5 rows] The pass@5 candidate selection priority begins with Match, i.e., the gold InChI-equality label. Table 3's pass@5 improvements are therefore oracle-selected and do not support the claim that refinement 'can be combined with multi-sample to further improve performance' in a deployable setting. The paper discloses the priority list, but the table and abstract/contributions present pass@5 as a pipeline feature rather than an upper-bound analysis. Please either remove gold-based selection from the main claims or rerun pass@5 with a selection rule that does not use test labels.
  3. [Table 2; Appendix C.1] Most baseline numbers in Table 2 appear to be taken from prior papers without re-runs, and the PCDes evaluation uses the LDMol-preprocessed split. Because Match, FCD, and fingerprint scores are sensitive to canonicalization, output-length handling, and test-split construction, the 'best Match' claim requires that all compared methods are evaluated under identical preprocessing. Please state the source of each baseline row (original paper, re-run, or reproduced) and confirm that the same split and metric pipeline were used for every entry; if available, include a small re-run of the strongest baselines under the MolGVR evaluation harness.
minor comments (4)
  1. [Appendix A.1, Eq. (11)] The RL accuracy reward is raw-string exact match against the gold answer, while the evaluation metric Match uses InChI equality. This is a conservative proxy, but the mismatch should be discussed explicitly, including whether any RL checkpoints were selected on the string reward rather than on InChI Match.
  2. [Figure 6 and Section 'Verification-Guided Refinement'] The denominators 1414 and 1526 are not the test-set sizes (3,300 and 2,998). Please clarify whether these are test samples with generated candidates that passed parsing, or some other subset.
  3. [Table 6 caption and text] The sentence 'combined setting also achieves the best results on most string-similarity and fingerprint-similarity metrics, string-similarity, and fingerprint-similarity metrics' contains a duplicated phrase.
  4. [General] The Refiner's stage-2 training data size (5,570 ChEBI-20 / 1,155 PCDes) is reported only in Appendix C.2; consider stating this in the main method section since it indicates the scale of verifier feedback available for training.

Circularity Check

1 steps flagged

Pass@5 'multi-sample further improvement' is oracle-selected by gold Match (Eq. 22); central pass@1 gains remain externally benchmarked.

specific steps
  1. self definitional [Appendix C.3, Eq. (22); Table 3 pass@5 rows]
    "From the resulting five final candidates, we select a single prediction for each input using a fixed priority order—Match, validity, Morgan FTS, RDK FTS, MACCS FTS, BLEU, and Levenshtein distance—and then compute all evaluation metrics based on these selected predictions. ... Match>Validity>Morgan>RDK>MACCS>BLEU>−Levenshtein."

    The pass@5 'prediction' is not produced from the input description; it is chosen as the candidate with the highest gold Match. Hence the Table 3 pass@5 Match is, by construction, the maximum Match over the candidate set (an oracle upper bound), not an independent prediction. Any pass@5-over-pass@1 gain is therefore guaranteed by the selection rule rather than by the Verifier–Refiner mechanism, undermining the claim that 'refinement and multi-sample generation are complementary' as a deployable performance statement. The central pass@1 comparison is unaffected.

full rationale

The central pass@1 results in Tables 2 and 3 are computed against external gold molecules via InChI equality (Eq. 20) after RDKit canonicalization; they do not reduce to the Verifier's own rule set, and the headline benchmark gains over ChemDFM-v1.5-8B and bioT5+ are not definitional. The Verifier's expert-validated, unreleased rules are used both to construct the Refiner's second-stage training data (Appendix C.2) and to decide test-time refinements; this makes the +0.011/+0.010 V&R gain dependent on an unverified label source and limits independent falsifiability, but it is an internal-consistency concern rather than a circular derivation because the final Match metric is external. The one concrete circular step is the multi-sample evaluation: Eq. (22) selects the final candidate using the gold Match label before Match is computed, so the pass@5 improvements in Table 3 are oracle-selected upper bounds and cannot support the stated claim that refinement and multi-sample generation are complementary in a deployable sense. Since this affects only the secondary pass@5 claim while the central pass@1 comparison remains externally benchmarked, the overall circularity is partial rather than total.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

The framework rests on a chain of unverified domain assumptions: RDKit's checks are faithful, the closed vocabulary covers the constraints, GPT-5.2 extracts them correctly, expert validation is sound, and the gold molecule is always the right refinement target. No independent evidence is provided for any of these beyond the benchmark numbers.

free parameters (3)
  • reward mixing weight λ = 0.1
    Eq. (9) combines format and accuracy rewards; chosen by hand, no sensitivity analysis reported.
  • format reward values r_fmt = 1 / 0.2 / 0
    Eq. (10) shapes the format reward; values are hand-set.
  • priority-order selection rule for pass@5 = Match > Validity > Morgan > RDK > MACCS > BLEU > -Levenshtein
    Eq. (22) defines how one candidate is picked from the five; using Match (gold label) first makes the pass@5 result oracle-selected.
axioms (5)
  • domain assumption RDKit's SMILES parsing, SMARTS matching, stereochemistry assignment, and formal-charge inspection correctly implement the chemical checks in the Verifier.
    The entire Verifier (Sec. 'MolGVR Verifier') is built on these RDKit functions being faithful to chemistry; no independent validation is provided.
  • domain assumption The six structural evidence types (formula, chain length, rings, functional groups, chirality) are sufficient intermediate targets for the Generator.
    Generator SFT (Eq. 6-8) uses these as ground-truth evidence; if they mislead the model, the generator's reasoning is unfounded.
  • ad hoc to paper The expert-validated closed set of rule categories and normalized labels covers the constraints that matter in ChEBI-20 and PCDes.
    The rule extraction prompt restricts to a fixed vocabulary (Appendix B.1); constraints outside it are silently omitted, and the expert validation is not documented or released.
  • ad hoc to paper GPT-5.2 correctly performs conservative rule extraction and generates correct RDKit checker code.
    The Verifier depends on an undocumented proprietary model; no evaluation of extraction accuracy is provided other than the downstream result.
  • domain assumption Gold molecules are always the correct target for refinement of rejected candidates.
    Refiner SFT pairs every non-matching/rejected candidate with the gold SMILES as the target; this assumes no labeling errors and that the gold is reachable from the failed candidate.

pith-pipeline@v1.3.0-daily-deepseek · 25778 in / 12205 out tokens · 118006 ms · 2026-08-03T06:16:26.488399+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation." pith.science (2026). https://pith.science/paper/ITOQZC5T

@misc{pith2026260729479,
  author       = {Pith},
  title        = {Pith review of: MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ITOQZC5T}},
  note         = {Machine review of arXiv:2607.29479}
}
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read the original abstract

Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecular representations. However, molecular descriptions often contain informative structural constraints, and violating such constraints can change the molecular identity. This makes chemical verification and error correction important but underexplored. To fill this gap, we propose MolGVR, a chemistry-grounded Generator--Verifier--Refiner framework. The Generator infers structural evidence and generates candidate molecules. The Verifier addresses the lack of chemical validation by converting descriptions into chemical constraints and checking candidates against them. The Refiner addresses generation failures by revising candidates rejected by the Verifier. Experiments on ChEBI-20 and PCDes show that MolGVR improves exact-match performance. These results suggest that coupling generation with executable verification and feedback-guided refinement is an effective way to improve text-to-molecule generation.

Figures

Figures reproduced from arXiv: 2607.29479 by Chen Zhang, Lei Jiang, Qian Tan, Xuanyu Zhu, Yuqiang Li, Zhonghang Yuan.

Figure 1
Figure 1. Figure 1: Motivation of MolGVR. Existing general-purpose, chemistry, and scientific LLMs can generate molecules that appear [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the MolGVR workflow. The MSR- and RL-trained Generator produces one or multiple candidate [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative examples on the ChEBI-20 and PCDes [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correction rates of verification-guided refinement [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Data synthesis process. (a) Generator SFT data are constructed by using RDKit to extract six types of structural evidence [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Confusion matrices of the rule-based MolGVR Verifier on ChEBI-20 and PCDes. Under the expert-validated hard [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

25 extracted references · 1 linked inside Pith

  1. [1]

    functional_groups Allowed normalized values: ester, amide, carboxylic_acid, carboxylate, ketone, aldehyde, alcohol, phenol, amine, sulfonamide, carbamate, phosphate, phosphonate, sulfate, nitrile, ether, thiol, sulfone, thioester, hydroxamate, imide, amidine, amidinium, carboxamidinium, sulfonic_acid, sulfonate, phosphodiester, phosphocholine, phenoxide, ...

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    Li, J.; Liu, Y.; Liu, W.; Lei, J.; Zhang, D.; Fan, W.; Zhou, D.; Li, Y.; and Li, Q

    Empoweringmoleculediscoveryformolecule-caption translation with large language models: A chatgpt perspec- tive.IEEEtransactionsonknowledgeanddataengineering, 36(11): 6071–6083. Li, J.; Liu, Y.; Liu, W.; Lei, J.; Zhang, D.; Fan, W.; Zhou, D.; Li, Y.; and Li, Q. 2026. Molreflect: Towards in-context fine-grained alignments between molecules and texts.IEEE Tr...

    Pith/arXiv arXiv 2026

  3. [3]

    elements_or_atom_types Allowed normalized values: fluorine, chlorine, bromine, iodine, phosphorus, sulfur, selenium, sodium, potassium, calcium, iron, cobalt, zinc, platinum, arsenic, nitrogen, oxygen

  4. [4]

    element_count_requirements Extract only explicit counts of elements or atom types. Allowed normalized values: fluorine, chlorine, bromine, iodine, phosphorus, sulfur, selenium, sodium, potassium, calcium, iron, cobalt, zinc, platinum, arsenic, nitrogen, oxygen Examples: one phosphorus atom, two nitrogen atoms, three chlorine atoms, dichloro

  5. [5]

    core_scaffolds_or_ring_systems Allowed normalized values: benzene, pyridine, quinoline, isoquinoline, triazole, tetrazole, piperidine, benzofuran, chromene, pyrimidine, oxazolidinone, phenothiazine, indole, naphthalene, purine, uracil, benzoxazole, benzothiazole, oxazole, thiazole, thiazolium, imidazole, steroid, pregnane

  6. [6]

    S at position 1

    stereochemistry_requirements Extract ONLY stereochemical information that can be checked without parent-structure numbering. Allowed normalized values: r_center, s_center, e_double_bond, z_double_bond, cis_double_bond, trans_double_bond Allowed examples: one R center, two S centers, one E double bond Do NOT extract: - position-dependent stereochemistry, s...

  7. [8]

    group_count_requirements Extract only explicit counts of functional groups or directly checkable subunits. Allowed normalized values: oxo_group, hydroxy_group, amino_group, carboxylic_acid, carboxylate, amide, ester, thioester, phosphate, phosphonate, sulfate, sulfonamide, nitrile, ether, double_bond, c_c_double_bond Examples: two oxo groups, one double b...

  8. [10]

    explicitly stated about the target molecule, and

  9. [11]

    derived from alanine

    directly verifiable from the molecule structure / charge / disconnected components. - If a fact would likely be discarded later because it is too ambiguous, too ontology-like, too position-dependent, too context-dependent, or too hard to verify reliably, DO NOT extract it. - When uncertain, omit rather than guess. Target molecule only: - The description m...

  10. [12]

    functional_groups Allowed normalized values: carboxylic_acid, carboxylate, amide, ester, carbamate, ketone, aldehyde, alcohol, phenol, amine, ether, nitrile, nitroso, sulfonic_acid, sulfonamide, phosphate, phosphodiester, isourea Extract only if the functional group is directly and reliably checkable as a concrete substructure

  11. [13]

    Do NOT map an unknown class name to the closest allowed label

    core_scaffolds_or_ring_systems Allowed normalized values: benzene, pyridine, pyrimidine, imidazole, triazole, quinoline, benzodiazepinone, naphthalene, steroid Extract only if the scaffold/ring system is explicitly stated for the target molecule. Do NOT map an unknown class name to the closest allowed label

  12. [14]

    elements_or_atom_types Allowed normalized values: fluorine, chlorine, iodine, phosphorus, sulfur, sodium, calcium, strontium, mercury, molybdenum, nitrogen, oxygen Extract only if the target molecule is explicitly described as containing that element or atom type

  13. [15]

    Examples: - one phosphorus atom - two chlorine atoms - 1:1 ratio of calcium and oxygen If the count is not explicit, do not extract it

    element_count_requirements Allowed normalized values: fluorine, chlorine, iodine, phosphorus, sulfur, sodium, calcium, strontium, mercury, molybdenum, nitrogen, oxygen Extract only if an explicit count is directly stated and directly usable. Examples: - one phosphorus atom - two chlorine atoms - 1:1 ratio of calcium and oxygen If the count is not explicit...

  14. [16]

    group_count_requirements Allowed normalized values: carboxylic_acid, carboxylate, hydroxy_group Extract only if the count is explicit and directly usable. Examples: - monocarboxylic acid -> carboxylic_acid count = 1 - dicarboxylic acid -> carboxylic_acid count = 2 - tricarboxylic acid -> carboxylic_acid count = 3 - dihydroxy -> hydroxy_group count = 2 - m...

  15. [17]

    functional_groups

    salt_or_charge_state Allowed normalized values: hydrochloride_salt, mesylate_salt, sodium_salt, zwitterion, monoanion, dianion, trianion Extract only if the target molecule is explicitly described in that form. Examples: - hydrochloride salt form - mesylate salt form - sodium salt - zwitterionic form - monocarboxylic acid anion - dicarboxylate anion - tri...

    2022

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    aza-groups at the 1-, 5-, 9- and 13-positions

    Error category: element_count_requirements Unmet text requirement: "aza-groups at the 1-, 5-, 9- and 13-positions"

  17. [19]

    tetramine

    Error category: group_count_requirements Unmet text requirement: "tetramine" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{C(CN)CNCCCNCCCN} Case 2 Generator input: <tbdd>\nThe molecule is a hydroxy fatty acid anion that...

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    derived from dodecanoic acid

    Similar improvements are observed on PCDes, where Matchincreasesfrom0.385to0.490andfrom0.450to0.503, respectively. The combined setting also achieves the best results on most string-similarity and fingerprint-similarity metrics, string-similarity, and fingerprint-similarity metrics, demonstrating that MSR supervision and RL provide com- plementary benefit...

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    omega-hydroxy fatty acid anion

    Error category: functional_groups Unmet text requirement: "omega-hydroxy fatty acid anion" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{CC(CCCCCCCCCCCCCC(=O)[O-])CO} Case 3 Generator input: <tbdd>\nThe molecule is a 2,...

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    both stereocentres have S-configuration

    Error category: stereochemistry_requirements Unmet text requirement: "both stereocentres have S-configuration" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{C1=C[C@@H]([C@H](C(=C1)C(=O)O)N)O} F.2 PCDes Case 1 Generator ...

  21. [23]

    primary amine

    Error category: functional_groups Unmet text requirement: "primary amine" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{C1=CC=NC(=C1)CCN} Case 2 Generator input: <tbdd>\nit is the prodrug of fluphenazine, an antipsychot...

  22. [24]

    a decanoate ester

    Error category: functional_groups Unmet text requirement: "a decanoate ester" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{CCCCCCCCCC(=O)OCCN1CCN(CC1)CCCN2C 3=CC=CC=C3SC4=C2C=C(C=C4)C(F)(F)F} Case 3 Generator input: <t...

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    the hydrochloride salt of mechlorethamine

    Error category: salt_or_charge_state Unmet text requirement: "the hydrochloride salt of mechlorethamine" Correct all listed violations and return the target molecule. Output format: The refined SMILES is \\boxed{ correct smiles } Refiner output: </think> \n\nThe refined SMILES is \\boxed{CN(CCCl)CCCl.Cl}

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    It has a role as an antineoplastic agent

    it is the hydrochloride salt of mechlorethamine. It has a role as an antineoplastic agent. It contains a mechlorethamine. Mechlorethamine Hydrochloride is the hydrochloride salt of mechlorethamine, a nitrogen mustard and an analogue of sulfur mustard, with antineoplastic and immunosuppressive activities. Mechlorethamine is metabolized to an unstable, high...

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    InFindings of the Association for Computa- tional Linguistics: ACL 2024, 9551–9565

    Enhancing Cross Text-Molecule Learning by Self- Augmentation. InFindings of the Association for Computa- tional Linguistics: ACL 2024, 9551–9565. Kim, H.; Jang, Y.; and Ahn, S. 2025. MT-Mol: Multi Agent SystemwithTool-basedReasoningforMolecularOptimiza- tion. In Christodoulopoulos, C.; Chakraborty, T.; Rose, C.; and Peng, V., eds.,Findings of the Associat...

    2024