oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning

Carl Edwards; Heng Ji; Qingyun Wang; Ruiling Xu; Yifan Zhang

arxiv: 2510.07731 · v3 · submitted 2025-10-09 · 💻 cs.AI · cs.CL

oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning

Ruiling Xu , Yifan Zhang , Qingyun Wang , Carl Edwards , Heng Ji This is my paper

Pith reviewed 2026-05-18 09:40 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords organic mechanism reasoningLLM benchmarkingchemical reasoningmulti-step reasoningfine-tuningorganic chemistryAI for chemistryreaction intermediates

0 comments

The pith

Current LLMs display chemical intuition yet struggle with consistent multi-step reasoning on organic reaction mechanisms.

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

Organic reaction mechanisms describe the elementary steps by which reactants pass through intermediates to form products, a process central to chemical reactivity and molecule design. The paper presents oMeBench as the first large-scale expert-curated benchmark containing over 10,000 annotated mechanistic steps complete with intermediates, type labels, and difficulty ratings. It also introduces the oMeS evaluation framework that scores both logical coherence at each step and chemical similarity between proposed and reference intermediates. Experiments show that leading models perform better on isolated steps than on full pathways and that prompting plus fine-tuning a specialist model on the dataset raises scores by 50 percent over the best closed-source system. A sympathetic reader would care because reliable mechanism reasoning is a prerequisite for trustworthy AI assistance in synthesis planning and reaction discovery.

Core claim

The paper establishes that large language models possess some chemical intuition for organic reactions but lack the ability to generate valid intermediates, preserve chemical consistency, and follow logically coherent multi-step pathways. This limitation is quantified on oMeBench through the oMeS framework, which combines step-level logic checks with chemical similarity metrics. The authors further demonstrate that targeted prompting strategies combined with fine-tuning a domain-specific model on the new dataset produce a 50 percent performance increase relative to the strongest closed-source baseline.

What carries the argument

oMeBench, the expert-curated collection of more than 10,000 mechanistic steps with annotated intermediates, reaction types, and difficulty levels, together with the oMeS dynamic scoring system that evaluates both logical validity of each step and chemical similarity to reference structures.

If this is right

Models that succeed on oMeBench should produce fewer chemically impossible intermediates when asked to propose full reaction pathways.
Specialist fine-tuning on mechanism data yields larger gains than general prompting alone.
Difficulty ratings in the benchmark enable graded evaluation that separates basic intuition from advanced multi-step planning.
The oMeS scoring method supplies a finer-grained alternative to simple exact-match or BLEU-style metrics for scientific reasoning tasks.

Where Pith is reading between the lines

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

Extending oMeBench-style curation to inorganic or biochemical mechanisms could test whether the same multi-step reasoning gaps appear in adjacent domains.
Models improved via this benchmark may accelerate computer-aided synthesis planning by reducing the rate of proposed pathways that fail experimental validation.
The 50 percent gain from fine-tuning suggests that domain-specific annotated traces are a high-leverage training resource for other scientific reasoning problems.

Load-bearing premise

The expert-curated annotations correctly identify valid chemical intermediates and maintain logical consistency without systematic labeling errors or coverage gaps.

What would settle it

Independent re-annotation of a random subset of oMeBench entries by multiple practicing organic chemists revealing that more than 15 percent of the recorded intermediates are chemically invalid or that step sequences violate conservation rules would falsify the benchmark's reliability and the reported performance gains.

Figures

Figures reproduced from arXiv: 2510.07731 by Carl Edwards, Heng Ji, Qingyun Wang, Ruiling Xu, Yifan Zhang.

**Figure 2.** Figure 2: Overview of dataset construction and examples. A named reaction refers to a class of reactions that share [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the dataset metadata and format. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 5.** Figure 5: Performance of LLMs on oMeBench across difficulty levels. Frontier models outperform others but all [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 7.** Figure 7: Performance vs Reaction Complexity. Per [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 6.** Figure 6: Distribution of mechanistic reasoning errors [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 8.** Figure 8: Model accuracy by type. While addition and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 9.** Figure 9: Top 19 R-groups most frequently suggested by Gemini-Pro-2.5 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

read the original abstract

Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to understanding chemical reactivity and designing new molecules and reactions. Although large language models (LLMs) have shown promise in understanding chemical tasks such as synthesis design, it is unclear to what extent this reflects genuine chemical reasoning capabilities, i.e., the ability to generate valid intermediates, maintain chemical consistency, and follow logically coherent multi-step pathways. We address this by introducing oMeBench, the first large-scale, expert-curated benchmark for organic mechanism reasoning in organic chemistry. It comprises over 10,000 annotated mechanistic steps with intermediates, type labels, and difficulty ratings. Furthermore, to evaluate LLM capability more precisely and enable fine-grained scoring, we propose oMeS, a dynamic evaluation framework that combines step-level logic and chemical similarity. We analyze the performance of state-of-the-art LLMs, and our results show that although current models display promising chemical intuition, they struggle with correct and consistent multi-step reasoning. Notably, we find that using prompting strategy and fine-tuning a specialist model on our proposed dataset increases performance by 50% over the leading closed-source model. We hope that oMeBench will serve as a rigorous foundation for advancing AI systems toward genuine chemical reasoning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

oMeBench adds a new large-scale dataset for testing LLM chemical reasoning but the 50% gain and multi-step failure claims rest on unvalidated annotations.

read the letter

The key takeaway is that this paper introduces oMeBench, the first large-scale expert-curated benchmark for organic mechanism elucidation with over 10,000 annotated steps, along with the oMeS dynamic evaluation framework. It shows that LLMs have some chemical intuition but struggle with consistent multi-step reasoning, and that fine-tuning a model on this data with prompting can yield a 50% performance increase over leading closed-source models. The work does a good job of focusing on the reasoning aspect rather than just outcome prediction, which is a common limitation in chemistry AI tasks. The dataset includes intermediates, type labels, and difficulty ratings, which allows for more granular analysis. The results highlight specific weaknesses in current models, which could guide future improvements in training for synthetic chemistry applications. Where it falls short is in the details of the annotation process. There is no mention of inter-annotator agreement, validation against known mechanisms, or how potential labeling errors were minimized. This is important because the entire evaluation and the claimed gains depend on these annotations being accurate representations of chemical reality. Without that, the conclusions about model struggles and the magnitude of improvement are less convincing. The paper engages directly with the literature on LLM capabilities in chemistry and tries to move beyond superficial task performance. This paper is aimed at researchers in AI applied to chemistry and drug discovery who need better ways to test reasoning abilities. A reader interested in benchmark design or fine-tuning for domain-specific tasks would find value here, particularly if they can use the dataset. It deserves serious peer review to verify the data quality and evaluation method. I recommend sending it to referees with instructions to examine the curation and validation sections closely.

Referee Report

2 major / 2 minor

Summary. The paper introduces oMeBench, the first large-scale expert-curated benchmark for organic mechanism reasoning consisting of over 10,000 annotated mechanistic steps with intermediates, type labels, and difficulty ratings. It proposes oMeS, a dynamic evaluation framework combining step-level logic and chemical similarity for fine-grained LLM assessment. The authors evaluate state-of-the-art LLMs and conclude that current models show promising chemical intuition but struggle with correct and consistent multi-step reasoning; they further report that prompting strategies plus fine-tuning a specialist model on the dataset yields a 50% performance increase over the leading closed-source model.

Significance. If the expert annotations can be validated as reliable, oMeBench would represent a meaningful contribution by filling a gap in step-level benchmarks for genuine chemical reasoning rather than surface intuition. The reported gains from fine-tuning are potentially actionable for model specialization in chemistry. The work's impact hinges on demonstrating that the benchmark's ground truth is robust, as this underpins both the performance claims and the diagnosis of multi-step failures.

major comments (2)

[§3] §3 (Dataset Curation): The expert curation process for oMeBench does not report inter-annotator agreement metrics, the number of experts involved, or any external validation against literature or reaction databases. This is load-bearing for the central claims, as the 50% performance lift from fine-tuning and the conclusion that models 'struggle with correct and consistent multi-step reasoning' both rest on the annotations accurately capturing valid intermediates and logical coherence.
[Results] Results section: The headline empirical result of a 50% performance increase is presented without statistical significance testing, variance estimates across multiple runs, or controls for dataset-specific effects, making it difficult to assess whether the observed delta reliably supports the superiority of the prompting-plus-fine-tuning approach over closed-source baselines.

minor comments (2)

[Abstract] Abstract: The acronym 'oMeS' is introduced without expansion on first use.
[§4] The manuscript would benefit from a clearer description of how chemical similarity is operationalized within oMeS (e.g., specific metric or embedding method) to allow reproducibility of the fine-grained scores.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and describe the revisions we will make to strengthen the manuscript's claims on annotation reliability and empirical rigor.

read point-by-point responses

Referee: [§3] §3 (Dataset Curation): The expert curation process for oMeBench does not report inter-annotator agreement metrics, the number of experts involved, or any external validation against literature or reaction databases. This is load-bearing for the central claims, as the 50% performance lift from fine-tuning and the conclusion that models 'struggle with correct and consistent multi-step reasoning' both rest on the annotations accurately capturing valid intermediates and logical coherence.

Authors: We agree that explicit reporting of the curation process is essential to substantiate the benchmark's validity. The original manuscript summarized the expert annotation workflow but did not include quantitative agreement metrics or validation details. In the revised version we will expand §3 to specify the number of expert annotators, report inter-annotator agreement (including the metric and value obtained), and describe the external validation steps performed against literature sources and reaction databases. These additions will directly support the reliability of the ground-truth annotations underlying both the performance claims and the diagnosis of multi-step reasoning failures. revision: yes
Referee: [Results] Results section: The headline empirical result of a 50% performance increase is presented without statistical significance testing, variance estimates across multiple runs, or controls for dataset-specific effects, making it difficult to assess whether the observed delta reliably supports the superiority of the prompting-plus-fine-tuning approach over closed-source baselines.

Authors: We concur that additional statistical controls are needed to make the reported performance gains more robust. We will revise the Results section to include formal statistical significance tests comparing the fine-tuned model against the closed-source baseline, report variance or standard deviation across multiple independent runs with different random seeds, and incorporate controls or ablation analyses that address potential dataset-specific effects. These changes will provide clearer evidence for the reliability of the 50% improvement. revision: yes

Circularity Check

0 steps flagged

No circularity: new benchmark and empirical evaluation are self-contained

full rationale

The paper introduces oMeBench, a new expert-curated dataset of over 10,000 mechanistic steps, along with the oMeS dynamic evaluation framework. All reported results, including the 50% performance gain from prompting and fine-tuning, are direct empirical measurements obtained by running models on this newly created benchmark. No mathematical derivations, equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central claims rest on the external validity of the annotations and evaluation protocol rather than any reduction to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that expert annotations constitute reliable ground truth for chemical validity and logical coherence, plus the assumption that oMeS similarity metrics meaningfully capture reasoning quality.

axioms (1)

domain assumption Expert annotations provide accurate ground truth for valid intermediates, reaction types, and logical consistency.
The benchmark and all reported performance numbers depend on the correctness of these human labels.

pith-pipeline@v0.9.0 · 5766 in / 1145 out tokens · 27846 ms · 2026-05-18T09:40:04.998189+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce oMeS, a dynamic evaluation framework that combines step-level logic and chemical similarity... oMeS-total (Stot) will assign credit only for exact matches... Spart grants partial credit when the generated intermediate has meaningful chemical similarity... measured by molecular fingerprints

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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Forward citations

Cited by 2 Pith papers

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

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cs.AI 2026-04 unverdicted novelty 6.0

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages · cited by 2 Pith papers · 4 internal anchors

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SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

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work page internal anchor Pith review Pith/arXiv arXiv 2023
[10]

level:Difficulty level:easy,medium, orhard, based on mechanistic complexity and reasoning depth

work page
[11]

Nazarov Cyclization Reac- tion

name:The formal or commonly accepted name of the reaction (e.g., “Nazarov Cyclization Reac- tion”). 13

work page
[12]

reactants_smiles / products_smiles:Chemically valid SMILES strings representing all reactants and products

work page
[13]

H+ OSO2Me

conditions:Concise expression of reaction conditions, including catalysts, reagents, or solvents (e.g., “H+ OSO2Me”). 6.description:A short natural-language summary of the overall mechanism and rationale. 7.mechanism:A list of ordered elementary steps. Each step must contain: •step: Step number (integer, starting from 1). •type: The high-level mechanistic...

work page
[14]

Each mechanism must align with those recordings in literature, textbooks or databases

work page
[15]

All SMILES strings, including reactants, products and intermediates, must satisfy chemical validity and are compilable by RDKit

work page
[16]

Assign step types and subtypes according to the standardized taxonomy provided in Tab. 5

work page
[17]

reaction_id

Annotators should provide rationales and descriptions inconcise academic language, emphasizing reactivity, resonance, or stability reasoning rather than descriptive repetition. Example Annotation. Mechanism JSON 1{ 2"reaction_id": "NR-201", 3"level": "medium", 4"name": "Nazarov Cyclization Reaction", 5"reactants_smiles": ["C(C)=CC(=O)C=C(C)", "CS(=O)(=O)O...

work page 2017
[18]

reaction_id

focuses on radical reactions, aggregating over 5,000 manually curated steps linked to specific transition states. Its counterpart, PMechDB, specializes in polar reactions (Tavakoli et al., 2024), providing a complementary collection of elementary steps. While valuable, both databases focus narrowly on single mechanistic families, and do not integrate radi...

work page 2024
[19]

” concatenates multiple molecules and “≫

are redundant but not erroneous, yielding an accurate evaluation of mechanistic reasoning. By contrast, conventional DP misaligns these intermediate steps, lowering bothS total andS partial despite chemically valid logic. Edge Case 2: Incomplete but near-correct predictions.Here, the model predicts a shorter sequence that captures most intermediates corre...

work page 2025
[20]

Database preprocessing: We pre-compute difference fingerprints for all reactions in the external database and cache them as{(d i,reaction i)}|D| i=1

work page
[21]

Query fingerprint generation: For a query reaction, we compute its difference fingerprint dq using the procedure described above

work page
[22]

Similarity scoring: We compute Tanimoto similarity between dq and each cached fingerprint di, filtering out identical reactions (by reaction ID or exact SMILES match)

work page
[23]

G.4 Implementation Details We implement this retrieval system using RDKit version 2023.09 with Python 3.9

Top-k selection: We rank candidates by decreasing similarity and retrieve the top k (typically k= 3 ) most similar reactions. G.4 Implementation Details We implement this retrieval system using RDKit version 2023.09 with Python 3.9. The external database consists of Next standardized reactions from diverse sources, stored in JSONL format with fields for r...

work page 2023
[24]

Provide valid SMILES strings (no placeholder notation)

work page
[25]

Include diverse functional groups: alkyl, aryl, heteroatoms, etc

work page
[26]

Do not include any H (H, CH3, CH2CH3, etc.) in the suggestions, only include C instead (C, CC)

work page
[27]

Ensure chemical stability and synthetic accessibility

work page
[28]

Include both electron-donating and electron-withdrawing groups

work page
[29]

[*:1]": [

If there are already ring systems in the template, do not include any ring systems in the suggestions Format your response as JSON, below is an example { "[*:1]": ["C", "CC", "C(C)(C)C", "c2ccccc2", "CCCC(C)C", "CC(C)(C)OC1=CC=C(C=C1)C2=CC=C(OC(C)(C)C)C=C2", "C(F)(F)F"], "[*:2]": ["C", "c2ccccc2", "CCCCCCCCCC4=CC2=CC=CC=C2C3=CC=CC=C34", "COC1=CC(=CC=C1)C2...

work page 2023

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Grzybowski, Ying Diao, Jiawei Han, Ge Liu, Hao Peng, Martin D

mclm: A function-infused and synthesis- friendly modular chemical language model.ArXiv preprint, abs/2505.12565. Robert B. Grossman. 2003.The Art of Writing Rea- sonable Organic Reaction Mechanisms, 2nd edition. Springer Science & Business Media, New York, NY . Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong M...

work page arXiv 2003

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Laszlo Kurti and Barbara Czakó

Pubchem 2025 update.Nucleic acids research, 53(D1):D1516–D1525. Laszlo Kurti and Barbara Czakó. 2005.Strategic ap- plications of named reactions in organic synthesis. Elsevier. Greg Landrum, Paolo Tosco, Brian Kelley, Ricardo Ro- driguez, David Cosgrove, Riccardo Vianello, sriniker, Peter Gedeck, Gareth Jones, Eisuke Kawashima, NadineSchneider, Dan Nealsc...

work page 2025

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Beyond chemical qa: Evaluating llm’s chemical reasoning with modular chemical operations.arXiv preprint arXiv:2505.21318, 2025

rdkit/rdkit: 2025_03_5 (q1 2025) release. Hao Li, He Cao, Bin Feng, Yanjun Shao, Xiangru Tang, Zhiyuan Yan, Li Yuan, Yonghong Tian, and Yu Li. 2025a. Beyond chemical qa: Evaluating llm’s chem- ical reasoning with modular chemical operations. ArXiv preprint, abs/2505.21318. Hao Li, He Cao, Bin Feng, Yanjun Shao, Xiangru Tang, Zhiyuan Yan, Li Yuan, Yonghong...

work page arXiv 2025

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ChemBERTa- 2: Towards chemical foundation models.arXiv preprint arXiv:2209.01712, 2022

IEEE. David Rogers and Mathew Hahn. 2010. Extended- connectivity fingerprints.Journal of chemical in- formation and modeling, 50(5):742–754. Philippe Schwaller, Daniel Probst, Alain C Vaucher, Vishnu H Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond. 2021. Predicting the outcomes of organic reactions with a language model.ACS Central Science, ...

work page arXiv 2010

[5] [5]

Mohammadamin Tavakoli, Ryan J Miller, Mirana Claire Angel, Michael A Pfeiffer, Eugene S Gutman, Aaron D Mood, David Van Vranken, and Pierre Baldi

Rmechdb: A public database of elementary radical reaction steps.Journal of chemical informa- tion and modeling, 63(4):1114–1123. Mohammadamin Tavakoli, Ryan J Miller, Mirana Claire Angel, Michael A Pfeiffer, Eugene S Gutman, Aaron D Mood, David Van Vranken, and Pierre Baldi

work page

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Gemini: A Family of Highly Capable Multimodal Models

Pmechdb: A public database of elementary po- lar reaction steps.Journal of Chemical Information and Modeling, 64(6):1975–1983. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean- Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Mil- lican, and 1 others. 2023. Gemini: a family of highly capable multimodal models.Ar...

work page internal anchor Pith review Pith/arXiv arXiv 1975

[7] [7]

LLM-Augmented Chemical Synthesis and Design Decision Programs

Language models for predicting organic syn- thesis procedures. Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ram- prasad, Philippe Schwaller, Yuanqi Du, and Chao Zhang. 2025. LLM-augmented chemical synthe- sis and design decision programs.ArXiv preprint, abs/2505.07027. Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. L...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[8] [8]

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Scibench: Evaluating college-level scientific problem-solving abilities of large language models. ArXiv preprint, abs/2307.10635. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. Chain-of-thought prompting elic- its reasoning in large language models.Advances in neural information processi...

work page internal anchor Pith review Pith/arXiv arXiv 2022

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Qwen3 Technical Report

Qwen3 technical report.ArXiv preprint, abs/2505.09388. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tian- wei Zhang, Fei Wu, and Guoyin Wang. 2023. In- struction tuning for large language models: A survey. ArXiv preprint, abs/2308.10792. Zihan Zhao, Da Ma, Lu Chen, Liangtai Sun, Zihao Li, Yi Xia, Bo Chen,...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[10] [10]

level:Difficulty level:easy,medium, orhard, based on mechanistic complexity and reasoning depth

work page

[11] [11]

Nazarov Cyclization Reac- tion

name:The formal or commonly accepted name of the reaction (e.g., “Nazarov Cyclization Reac- tion”). 13

work page

[12] [12]

reactants_smiles / products_smiles:Chemically valid SMILES strings representing all reactants and products

work page

[13] [13]

H+ OSO2Me

conditions:Concise expression of reaction conditions, including catalysts, reagents, or solvents (e.g., “H+ OSO2Me”). 6.description:A short natural-language summary of the overall mechanism and rationale. 7.mechanism:A list of ordered elementary steps. Each step must contain: •step: Step number (integer, starting from 1). •type: The high-level mechanistic...

work page

[14] [14]

Each mechanism must align with those recordings in literature, textbooks or databases

work page

[15] [15]

All SMILES strings, including reactants, products and intermediates, must satisfy chemical validity and are compilable by RDKit

work page

[16] [16]

Assign step types and subtypes according to the standardized taxonomy provided in Tab. 5

work page

[17] [17]

reaction_id

Annotators should provide rationales and descriptions inconcise academic language, emphasizing reactivity, resonance, or stability reasoning rather than descriptive repetition. Example Annotation. Mechanism JSON 1{ 2"reaction_id": "NR-201", 3"level": "medium", 4"name": "Nazarov Cyclization Reaction", 5"reactants_smiles": ["C(C)=CC(=O)C=C(C)", "CS(=O)(=O)O...

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[18] [18]

reaction_id

focuses on radical reactions, aggregating over 5,000 manually curated steps linked to specific transition states. Its counterpart, PMechDB, specializes in polar reactions (Tavakoli et al., 2024), providing a complementary collection of elementary steps. While valuable, both databases focus narrowly on single mechanistic families, and do not integrate radi...

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[19] [19]

” concatenates multiple molecules and “≫

are redundant but not erroneous, yielding an accurate evaluation of mechanistic reasoning. By contrast, conventional DP misaligns these intermediate steps, lowering bothS total andS partial despite chemically valid logic. Edge Case 2: Incomplete but near-correct predictions.Here, the model predicts a shorter sequence that captures most intermediates corre...

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[20] [20]

Database preprocessing: We pre-compute difference fingerprints for all reactions in the external database and cache them as{(d i,reaction i)}|D| i=1

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[21] [21]

Query fingerprint generation: For a query reaction, we compute its difference fingerprint dq using the procedure described above

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Similarity scoring: We compute Tanimoto similarity between dq and each cached fingerprint di, filtering out identical reactions (by reaction ID or exact SMILES match)

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[23] [23]

G.4 Implementation Details We implement this retrieval system using RDKit version 2023.09 with Python 3.9

Top-k selection: We rank candidates by decreasing similarity and retrieve the top k (typically k= 3 ) most similar reactions. G.4 Implementation Details We implement this retrieval system using RDKit version 2023.09 with Python 3.9. The external database consists of Next standardized reactions from diverse sources, stored in JSONL format with fields for r...

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[24] [24]

Provide valid SMILES strings (no placeholder notation)

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[25] [25]

Include diverse functional groups: alkyl, aryl, heteroatoms, etc

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[26] [26]

Do not include any H (H, CH3, CH2CH3, etc.) in the suggestions, only include C instead (C, CC)

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[27] [27]

Ensure chemical stability and synthetic accessibility

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[28] [28]

Include both electron-donating and electron-withdrawing groups

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[29] [29]

[*:1]": [

If there are already ring systems in the template, do not include any ring systems in the suggestions Format your response as JSON, below is an example { "[*:1]": ["C", "CC", "C(C)(C)C", "c2ccccc2", "CCCC(C)C", "CC(C)(C)OC1=CC=C(C=C1)C2=CC=C(OC(C)(C)C)C=C2", "C(F)(F)F"], "[*:2]": ["C", "c2ccccc2", "CCCCCCCCCC4=CC2=CC=CC=C2C3=CC=CC=C34", "COC1=CC(=CC=C1)C2...

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