Towards Knowledge‑and‑Data‑Driven Organic Reaction Prediction: RAG‑Enhanced and Reasoning‑Powered Hybrid System with LLMs
Authors: Qingyu Wang
Xinyuan Cai
Xiang Cheng
YuZhe Gao
Bo XU
Venue: ICLR 2026 Poster (2026)
Keywords: Organic Reaction Prediction
Large Language Models
Retrieval‑Augmented Generation
Chain‑of‑Thought Reasoning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: In organic reaction prediction, many recent approaches ranging from traditional task-specific models to Large Language Models (LLMs), have demonstrated notable success. However, these methods are inherently data-driven, exhibit constrained interpretability, and have hit fundamental performance bottlenecks. To overcome these limitations, we present Reaction-Thinker, a hybrid, knowledge‑and-data‑driven system that is enhanced by Retrieval‑Augmented Generation (RAG) and powered by advanced reasoning, improving both the interpretability of prediction process and the explainability of results. We develop similar-case retrieval database and train a RAG‑based LLM through supervised fine-tuning (SFT) to apply both reaction types and similar reaction cases as knowledge. We also construct a reaction reasoning chain-of-thought (CoT) dataset and train a reasoning-based LLM through SFT, then further optimize it using Group Relative Policy Optimization (GRPO). Experimental results show that our method outperforms all compared LLMs and task-specific models, achieving the highest accuracy (Exact Match) and fingerprint similarity (FTS). Ablation study indicates improvements in relative accuracy of 7.5% and 13.9% for RAG and GRPO, respectively. Further analysis of mispredictions reveals limitations in conventional evaluation metrics, which motivates our proposed benchmarking refinement.

Published as a conference paper at ICLR 2026
TOWARDS KNOWLEDGE-AND--ATA--DE GANIC REACTION PREDICTION: RAG-ENHANCED AND REASONING-POWERBR HYBRID SYSTEM WITH LLMS
OR-
Qingyu Wang!.2 Xinyuan Cai
Xiang Chengè
Yuzhe Gao
Bo Xu
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
ABSTRACT
In organic reaction prediction, many recent approaches ranging from tradi- tional task-specific models to Large Language Models (LLMs), have demon- strated notable success. However, these methods are inherently data-driven, ex- hibit constrained interpretability, and have hit fundamental performance bottle- necks. To overcome these limitations, we present Reaction- Reaction-Thinker, hy- brid, knowledge-anne -atee-anddddddddddiaeaaa flta--riven system that is enhanced by Generation (RAG) and powered by advanced reasoning, improving both the inter- pretability of prediction process and the explainability of results. similar-cas retrieval database and train RAG-based LLM through supervised min-ttning (SFT) to apply both reaction types and similar reaction cases as knowl- edge. We also construct reaction reasoning chain-of- of-thought (CoT) dataset and train a reasoning-base based LLM through SFT, then further optimize it using Group Relative Policy Optimization (GRPO). Experimental results show that method outperforms all compared LLMs and task-specific models, achieving the highest accuracy (Exact Match) and fingerprint similarity (FTS). Ablation study indicates improvements in relative accuracy of 7.5% and 13.9% for RAG and GRPO, re- spectively. Further analysis of mispredictions reveals limitations in conventional evaluation metrics, which motivates our proposed benchmarking refinement.
develop
INTRODUCTION
In organic chemistry, predicting reaction outcomes has long been a challenge. Traditionally, expert chemists relied on years of hands-on experience and well-established principles to design experiments and anticipate products. Today, artificial intelligence offers powerful augment to this approach, enhancing the efficiency and precision prediction.
Current approaches to predicting organic reaction outcomes can be mainly categorized into template- methods integrate machine learning with prede-
based and template-free methods. Template- fined structural transformation rules, also known reaction templates, either curated by experts or extracted from atom-mapped datasets (Chen & Jung. 2022 Sacha et al.] 2021). In contrast, template-free methods employ data-driven architectures, such graph neural networks (GNNs) or Transformer-bas sequence models, to infer reaction patterns directly from large reaction corpora without relying on explicit templates. (Schwaller al. 2018 Irwin al., 2022). Recent advances in large language models (LLMs) have garnered significant attention (Achiam et al. [0233 Team al. 2024, Bai et al.) 2023 Liu et al., 2002)). By undergoing large-scale pre-training followed by fine-tuning or instruction tuning, these models acquire extensive knowledge, proficiently follow instructions, and exhibit strong reasoning abilities. As a result, LLMs now achieve stat-of-thh-an
"These authors contributed equally.
1 Corresponniig author. Email: xuho@ia.ac.cn

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(SOTA) performance comparable even exceeding that of humans, in general Natural Language Processing (NLP) tasks such as language understanding and question answering. as well as special- ized applications including mathematical moblem--slving and code generation. Hence, natural idea is to explore whether L.Ms can replicate the cognitive processes of expert chemists, enabling more accurate reaction predictions.
Human chemists predict organic reactions through a multi-step cognitive process. Initially, they an- alyze molecular structures, identifying functional groups, bond connectivity, stereochemistry, and reactive sites, which are fundamental to mechanistic analysis (Smith 2023). Then, they apply core principles to hypothesize bond cleavage and formation. They propose reaction pathways and eluci- date mechanistic steps. including identifying reaction centers, considering mechanisms such as SNI, SN2, or pericyclie, and evaluating thermodynamic and kinetic feasibility (Levy) (017). Finally, they integrate insights to predict the main product and account for side reactions. Apart from that, known reaction cases are also frequently referenced inform predictions.
Recent works have applied LLMs to chemistry, particularly targeting organic reactions prediction. Notable examples include the ChemDFM series (Zhao et al.] (222bbaa) a). ChemLLM (Zhang et al.)
2024). ChemCrow (M. Bran et al., 2024p, and Coscientist (Boiko et al. (023). Some approaches
leverage large proprictary models such as GPT-4o (OpenAl 2024) directly, exploiting their innate zero-shot reasoning capabilities. Others build on open- LLMs such as LLaMA (Touvron et al.
2023) and fine-tune them on chemical literature and curated datasets, resulting in domain-specific models with enhanced accuracy on chemical question answering (Q&A) and prediction tasks.
However, current fine-tuning methods for LLMs in chemistry primarily rely on data-driven strate- gies, which are similar to traditional end-to-end deep learning approaches, and often fail to fully leverage the rich chemical knowledge embedded in the pre-trained parameters of LLMs, and fine- tuned models tend underutilize their robust reasoning and in-context learning capabilities. Con- sequently, the predictions often lack interpretability and do not outperform established task-specific methods accuracy. Although LLMs hold immense promise for organic reaction prediction, owing to their pre-trained chemical knowledge as well as robust in-context learning and reasoning capabil- ities, two critical bottlenecks must still be addressed before this potential can be fully realized.
First, high-quality, structured training data is severely scarce in chemistry. Domains like mathemat- ies benefit from extensive open-source communities (e.g.. Lean Community) and web-scale datasets, whereas chemistry lacks publicly available resources for reaction reasoning. As result, models un- dergo pre- training and fine-tuning with limited exposure to task-relevant chemical data, hindering their ability to develop advanced capabilities for complex reaction prediction tasks. Furthermore, creating large-scale, annotated chemical datasets is a time-consuming and labor-intensive process that demands substantial domain expertise.
Second, learning strategies for chemistry LLMs remain underdeveloped. Most existing chemical LLM frameworks rely standard pre-training followed by supervised fine-tuning (SFT), which often fails to unlock the full potential of these models. Recent research highlights two advanced techniques, including Retrieval-Augmentt which can inject domain-
Generation (RAG) (Ke al. 2024 Chen et al. 2025). knowledge as well as mitigating hallucinations, and reinforcement learning (RL) (Guo et al.) 2025), which can enhance reasoning and interpretability. However, their adoption in chemical LLMs remains limited. Therefore, developing hybrid learning framework that integrates SFT, RAG and RL, combining both data-driven and knowledge-drives paradigms, represents a promising direction for achieving interpretable, high-performance prediction.
In this paper, we propose Sraction-Thinker hybrid moowledg-anndddat---re organic reaction prediction system, comprising both RAG-based predictor and sasoning-basedd predictor. The main contributions of our work can be concluded in the following parts.
We categorize reactions based on the given reaction inputs, define a standardized similar- ity metric, and construct similar-case retrieval database for each type. Training and test samples are partitioned based on whether similar retrieved cases exist, and each subset is processed through a dedicated, specialized pipeline.
For samples with similar cases retrieved, we inject reaction type and case-specific knowl- edge into user prompt and curate a customized SFT dataset for a RAG-based LLM. This enhances the ability of the L.M retrieve domain-specific contextual information.
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For samples lacking similar reaction cases, introduce mtlti-stage reasoning enhance- ment pipeline. First, we construct a thouuht (CoT) dataset for organic reaction reasoning. Then, we employ SFT cold-start to establish initial foundation of high-accuracy CoT reasoning. Finally, we refine the deductive reasoning using reinforce- ment learning through Group Relative Policy Optimization (GRPO).
The system outperforms all compared LLMs and even exceeds traditional task-specific models, in both accuracy (Exact Match) and fingerprint similarity performance (FTS). The ablation study indicates improvements in relative accuracy of 7.5% and 13.9% for RAG and GRPO, respectively.
A detailed error analysis reveals that some incorrect predictions correspond to chemically plausible byproducts or alternative reaction pathways, despite not matching the canonical ground truth. To better account for such chemically plausible outcomes, propose novel evaluation paradigm by incorporating retrosynthetic validation. Notably, our analysis indicates that 47.8% of these incorrect predictions are chemically reasonable.
2 METHODS
As illustrated Figure our proposed organic reaction predict system integrates four core modules: (1) reaction type classifier, (2) a similar-cart case case retrieval database, (3) a RAG-basee reaction predictor, and (4) a reasoning- based reaction predictor. Given a set of reaction inputs including reactants, solvent, and reagents, the system first applies the classifier to determine the most probable reaction type. Based on the predicted type, it queries the similar-case retrieval database for analogous reaction examples. If similar reaction cases are found, they are then incorporated alongside the user prompt into the RAG-based predictor; otherwise, the reaction inputs are routed directly to the reasoning- based reaction predictor for or reasoning- based module generates the final reaction outcome.
analysis. Depending on the pathway, either RAG-enhanced
Knowledge-and--Dta Driven
Reaction inputs
L.M oulput
1. Analyzing the given conditions
Similar-Case Retrieval Database
Supervised Tnne-Tuning
Ideniiying reaction type
Hypothesizing reaction mochanism
OA
CoT
Predicting the reaction pathway
5. Considering altemnative possibilities
Reinfoncement Learnnng
Verifying predicted product
Rechecking the reaction mechanism
reactand solvents reagents
reaction case
GRPO
Final reasoning result
Reasoning- bused
LLM
Or
Reaction Type Classifier
RAG-based LLM
L.M output
casus
Supervised Fine Tuning
given reaction inputs,
Rexction Cauegory Resction Eibholling
RAG
the reaction category, and similar
cases, predicted ooceesoooooox---oouu wtwps//wewo....oeeeeeetaaaaa budgal
Figure The system architecture, training process, and inference pipeline of Reaction-Thinker.
Subsequent sections detail the training process of reaction type classifier, the construction and usage of the similar-case retrieval database, the preparation of a CoT dataset for organic reaction reasoning, and the training strategies for both RAG-based and reasoning- predictors.
2.1 REACTION TYPE CLASSIFIER
We implement a two-layer MLP as the classifier. The original reaction inputs, provided as SMILES strings, processed with RDKit (Landrum) 2016) to compute multiple structural fingerprints. These fingerprints Fingerprint) are then concatenated and fed into the MLP to predict reac- tion type (Classifier-Oui fier-Oul).
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Inspired by previous work (Safizadeh et al. 2021), employ combination of various molecu- lar fingerprint methods to comprehensively capture molecular information, including RDK (suitable for general molecular similarity searches) (Schneider et al. 2015b), LAYERED (useful for substruc- ture screening) (RDKit-Book 2025). PATTERN (focused on identifying specific chemical features) (RDKit-Book) 2025), AVALON (effective for both substructure screening and similarity matching in complex molecules) (Gedeck et al.] 2006), and MORGAN fingerprints (especially suitable for cyclic substructure and comparing structural features) (Rogers & Hahn 2010).
We train the reaction type classifier on the Schneider-50K dataset (Schneider et al., 2015a), which contains 50K reaction SMILES classified into 50 representative types, providing granulariy well- suited for robust classification. After training, we extract the first layer output of the classifier as a compact representation of the reaction inputs, providing a molecular embedding (Rea- Embedding) of the reaction. The architecture of the reaction type classifier is described in Equation T
Rea-Embedding = Layer1 (Mot-Fingerprint)
(1)
Classifier-Out = Layer2 (Rea- Embedding)
2.2 SIMILAR- CASEE RETRIEVAL DATABASE FOR RAG
For each reaction in both training and test splits of Open Reaction Dataset (ORD) (Kearnes et al. 2021], we feed its reaction inputs (including reactants, solvents, and reagents, all formatted as SMILES) into our trained reaction type classifier. This yields two outputs: (1) a molecular em- bedding from the first layer, and (2) a predicted reaction type.
Using the ORD training set, we construct a similar-case retrieval library for each reaction type by computing the Euclidean distance (L2 norm) between the molecular embedding of each query reaction and that of all other embeddings of the same type. Any training sample with an embedding distance smaller than M is considered similar, and its full reaction SMILES (including reactants, solvents, reagents, and products) will be added to the retrieval library for that type.
Based on this, we build a SFT dataset for RAG-based LLM, containing only reactions that success- fully retrieve at least one similar case. Each SFT sample includes the reaction inputs, its predicted reaction type, the retrieved similar cases, and the target products.
During inference, we apply the same embedding and classification process to reaction inputs from the test set. If the system retrieves one or more training cases within distance M, it follows the RAG pathway; otherwise, it proceeds to the reasoning pathway without external context.
2.3 COT DATASET FOR REACTION REASONING
System Procupt
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User Prompt
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plano profhet the pur sauhhee product wnhingg pour chere of exprricc Plane scavnn
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(Mealvsiag
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Figure 2: An example of chain-of-thought dataset for reasoning, including system prompt, user prompt, and supervised output.
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Here two-stage approach is adopted to generate CoT data for reaction reasoning, involving both the USPTO-MIT (Jin et al., 2017) and ORD. Finally, we merge CoT samples obtained from both stages into unified dataset, serving the primary CoT resource for this work. The data open-sourced (refer to link and details Appendix).
2.3.1 STAGE 1: PRELIMINARY CONSTRUCTION
Following the methodology in HK-Olaw (Lab 2024), we extract reaction SMILES from a random subset of the USPTO-MIT training set. We have carefully filtered the USPTO-MIT training data to ensure that no reaction SMILES from it appears in the ORD test set, eliminating any risk of data leakage during training and inference. These SMILES were processed using Qwen2.5-72B-Instene [Qwen Team 2024) with instructions to reconstruct the reaction mechanism through deductive rea- soning, systematically deriving the products from the given reactants, solvents, and reagents through chain-of ofttuhuttthhht reasoning process, even though the model has access to the final answer (refer to Appendix for the prompt details). We then apply rigorous post-processing to refine the gener- ated contexts, including format standardization and keyword-basee validation, ultimately obtaining 119K high-quality CoT samples after careful filtering. example of the dataset shown in Fig- ure 2 Although directly predicting reaction outcomes from conditions is challenging, approach leverages the observation that when provided with full reaction SMILES, LLMs can systematically deduce the reaction pathway by analyzing the transformation from reactants products.
2.3.2 STAGE 2: DISTILLATION AND VALIDATION
We first fine-tune DeepSeck-RI-DD RI-t--Diillli-enne---- (DeepSekkAA] 2025c the CoT samples gen- erated from USPTO-MIT using SFT. Then, we further train the model on the ORD training set using Group Relative Policy Optimization (GRPO) (see experimental results in Appendix). The GRPO will be explained in detail in the following section. During this stage, only those generated reasoning trajectories that lead correct predicted products retained. Overall, we collected 575K validated CoT examples, covering approximately 55K unique samples from the original ORD dataset.
2.4 TRAINING STRATEGY OF RAG-BASED LLM
2.4.1
SUPERVISED FINE-TUNING
We fine-tune RAG-based LLM the dataset constructed from similar-case retrieval database, using SET with full parameter updates. The Qwen3-32B (Qwen Team 2025) selected as the backbone model for this process.
2.5 TRAINING STRATEGY REASONING- BASED
BEASDD LLM
We employ two-stage training strategy (SFT followed by RL) for reasoning- based LLM, using DeepSeck- -i-cck--R-DDstllllll RI-Distill-Qwen-322 (DeepSeck-A) 2025b) as the backbone model due to its strong reasoning performance compared to other LLMs of similar size.
2.5.1 SUPERVISED FINE-TUNING
First, fine-tune the base model using SFT with full parameter updates on the generated CoT dataset for reaction reasoning. Through this process, the model begins to internalize reasoning patterns specific organic reaction.
2.5.2 REINFORCEMENT LEARNING
Next, perform RL with LoRA adapters (Hu et al., 2022 the ORD training set to further enhance reasoning accuracy as well as reliability.
Specifically, GRPO as the learning algorithm. Given input query q P(Q), GRPO samples group of G responses {y1, ./............ from the current policy Toa. The core idea is to update the policy no by maximizing objective function that encourages responses with higher-
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than- man-averaage rewards within their group. The GRPO objective function is defined follows:
[(0)= P(Q)( (n)g a (11)
1//
min cr, (0)A.,, (1 e, + t) A..))
K (iTe
Tef
where e is the clipping ratio, 3 is the coefficient for KL-divergence loss, and Tee is the reference policy, (0) is the importance sampling ratio for token yi, (the 1-th token of the i-th response y;):
To Vr,ce))
Cs (0)
(3)
Tham (Vi.lg Fi,ce)
Ar, advantage estimate for all tokens response y, and calculated by normalizing the rewards (Ti,r2,...,7 using the group mean and standard deviation:
A F2 mean((rr,,
(ri, r....,........ ra])
std((ri, ......
2.5.3 REWARD FUNCTIONS
Here we design custom reward function for RI, specifically tailored to organic reaction reasoning, composed of four components:
Format Reward:
Assess whether the response format strictly follows the user instructions, awarding 0.1 for correct compliance; or otherwise zero.
Length Reward: Encourages concise reasoning, awarding 0.1 if the chain-of-thought
length falls within a predefined range (500 to 2000 tokens); or otherwise zero.
Validity Reward: Assess the validity of the generated product SMILES, awarding 0.1 for chemically valid; or otherwise zero.
Accuracy Reward: Canonicalize the SMILES of generated product and compare it to the ground truth. Award 2.0 if the two match exactly; or otherwise zero.
The final reward is calculated as the sum of these components. This composite reward structure ensures that the model incentivized to produce well-formed, appropriately concise, and chemically accurate reasoning.
3 EXPERIMENTS
In this section, we first present a comprehensive comparative evaluation between our method and existing baselines. Next, evaluate the contribution of RAG. We also conduct ablation study to assess how variations in reward function design and the use of cold-start strategy affect GRPO performance in this task. Finally, through detailed analysis of mispredictions, limitations in conventional evaluation metrics. Leveraging these insights, we propose a novel refer- ence metric and present its evaluation result. Additional details are provided in Appendix.
identify critical
3.1 EXPERIMENTAL SETTING
3.1.1 DATASET AND METRICS
The raw Open Reaction Database (ORD) has been preprocessed by ORDerly (Wigh et al. (2024) and split into 832K for training and 86K for testing. For evaluation, we employ various metrics includ- ing Validity (whether the product SMILES can be successfully processed by RDKit), Exact Match (after canonicalization) and molecular fingerprint similarity (FTS, including MORGAN, RDK, and AVALON fingerprints). We deliberately avoid relying text-based similarity metries (e.g., BLEU and Levenshtein), since they poorly reflect actual molecular differences, even a single alteration in SMILES string correspond substantial change in the chemical structure.

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3.1.2 BASELINES
We compare our system with: (1) open-source LLMs including GPT-4o, DeepSeek-RI (Guo
et al. 2025), SeesSeckRR--issii--Llaaaes
(DeepSeck-Al 2025a). DeepSeck-Rl-Distil-Ooeeeee
series 32B//44/7BB and Qwen2.5-72B-Insee
(2) chemical LLMs including ChemDFM-133AM
[OpenDFM Team 2024) and Text-Chem-TSS (Christofidellis et al. 2023); and (3) traditional task-
specific models including Chemformer, which is reported to achieve best Top-1 accuracy on
USPTO-MIT (Chen & Jung. 2022), and Molecular Transformer (Schwaller et al. 2019).
For open-source LLMs,, we use the same user prompt template as our method, and for chemical LLMs, we adopt the training prompts specified in the relevant papers.
3.2 MAIN RESULT
shown in Table 1 our method outperforms all compared LLMS and traditional task task-specific models, achieving the highest Exact Match and fingerprint similarity (FTS).
Comparison of method with various baselines on the task of organic reaction prediction. The results for Molecular Transformer are directly taken from existing work (Wigh al. (022). Due API cost constraints, the results for GPT-4o (marked f) are based on a randomly sampled subset of 500 instances from the ORD test set. The top results are highlighted in bold.
Table I:
FTS (%)
Model
Model Type
Validity (%)
Exact Match (%)
MORGAN
RDK
AVALON
Chemformer
88.13
92.40
94.35
95.12
Task-Specifi Model
Molecular Transformer
99.66
85.84
GPT-4o f
28.26 f
64.93
72.13
72.09
DeepSeck-R1
68.54
11.68
55.71
64.09
64.98
Qwen2.5-72H-Inninnn
35.27
0.54
37.46
45.95
46.01
DeepSeek-RI
Diteeck--R--iiiil----- Distill-llama-0BB
General
66.42
7.20
49,06
59.17
59.67
DeepSeek-RR R1 S1--Dittill-uue 3211l30
57.58
6.52
50,50
61.05
61.05
DeepSeck-RI-DD R----isiil--oeee-------
43.72
1.69
32.72
40,92
40,95
DeepScek-R DiistiDl--wernn
47.74
1.21
43.50
54.58
53.77
ChemDFM-13B
98.29
52.41
77.27
82.03
82.15
ChemDFM-8B
Chemicul
97.80
48.02
74.69
79.85
80.02
Text-Chem-TT
95.67
47,88
76,45
81.81
81.81
Reaction- Thinker (Ours)
89.86
95.22
96.24
96.37
The final performance of our method is achieved through integration. In the test set, 81.7% of the samples have similar reaction cases available. For these, our RAG-based approach achieves an Exact Match of 94.70%. For the remaining 18.3% samples without similar cases, based approach and achieve an Exact Match of 68.24%. Combining these two approaches yields an overall accuracy of 89.86% across the entire test set. The FTS score is computed using the same weighted approach, combining the RAG-based and reasoning-based results according to their respective proportions.
apply reasoning-
3.3
ABLATION STUDY
3.3.1 CONTRIBUTION OF RAG
The effectiveness of RAG has been widely documented in recent works. By grounding generation with retrieved context, it significantly reduces hallucinations and improves accuracy across many domains. To verify the benefit of RAG for current task, we continue to use Qwen3-32B as the base model and conduct a controlled comparison. Rather than following the conventional RAG setup that retrieves reaction types and similar cases for prompt augmentation, we perform direct end-to-end supervised fine-tuning, mapping reaction input SMILES directly to product SMILES. As shown in Table 2 using RAG yields better performance, with relative accuracy improvement of 7.5%. This matches chemical intuition: just as chemists reference analogous reactions, LLMs benefit from RAG to improve prediction accuracy.

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Table 2: Accuracy performance with and without RAG.
w/ RAG
w/o RAG (Ead-to-End)
Exact Match (%)
83.13
77.35
3.3.2 RAG ABLATION STUDY
We also explore how the embedding distance threshold M (which determines what reaction counts as similar, and is detailed in Section Similar-Case Retrieval Database for RAG) affects both the proportion of queries having similar cases and the RAG pathway performance (in Table 3
Table 3: Exact Match performance under different RAG thresholds.
Proportion with
RAG-based Exact Match (%)
Reasoning- based Exact Match (%)
M
Total Acc (%)
similar cases (%)
10
81.70
94.70
68.24
89.86
30
92.52
87.33
69.78
86.02
40
94.68
87.99
68.15
86.93
100
99.10
88.94
67.27
88.74
The results show that tight thresholds (e.g. M=10) yield high Exact Match (94.70%) but for fewer queries, while looser thresholds increase coverage yet degrade accuracy. This highlights the need to identify optimal operating point. Ultimately, we selected M=10 in this work because yields the highest overall accuracy (Total Acc).
3.3.3 INFLUENCE OF GRPO
GRPO is an effective reinforcement learning framework for LLMs, where the design of reward functions and selection of the base model critically determine its performance. To further explore its application in current task, we conduct controlled experiments.
1. Reward Function Ablation Study
Building on the baseline reward function (comprising format, length, validity, and accuracy re- wards), we introduce MORGAN fingerprint similarity reward (FTS reward, ranging from 0.0 to 1.0) order structurally align predictions with ground truths.
This modification aims to mitigate re- ward sparsity by guiding the LLM to generate outputs from structurally similar to fully accurate. We record
w/ FTS Reward
w/o FTS Reward
w/o FTS Reward
the reward curves during training in Figure 3 and evaluate the resulting model with results presented in Ta- ble 4 The experiments reveal a paradoxical phenomenon, while the reward curve shows continuous im- provement, the evaluation accuracy actually declines. Through detailed analysis, we identify that the fine- tuned LLM tended to verbatim copy reactant SMILES outputs. This indicates suboptimal optimization strategy, since product and reactants structures share chemical similarities, directly copying reactants could still achieve relatively high reward.
Training Reward
Validation Reward
0. 0.6
150 Steps
Steps
Figure 3: The reward curves under different combinations of (i) whether SFT applied before RL and (ii) whether the FTS reward is introduced.
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Table 4: Accuracy performance with and without fingerprint similarity reward in GRPO.
w/ FTS reward
w/o FTS reward
Exact Match (%)
56.83
68.24
To address this issue, downweight the FTS reward and incorporate explicit penalties for reactant copying. However, subsequent experiments demonstrate these measures are insufficient to com- pletely prevent this behavior. This phenomenon exemplifies reward hacking, well-documented RL failure mode where agents optimize the proxy reward in unintended ways, achieving higher scores while failing the true task objective. The tendency LLM cheat by exploiting structural corre- lations between reactants and products remains significant challenge, now recognized as a critical focus for our ongoing optimization efforts.
2. Base Model Capability Analysis
To evaluate the impact of base model capability on GRPO performance, we conduct controlled experiment comparing two approaches: (1) direct application of GRPO to the initial DeepSeek-Ri- Distill-Qwen-32B, and (2) implementing GRPO following SFT on our reaction reasoning dataset.
The experimental results, including the reward curves in Figure 3 and final performance in Table reveal markedly different outcomes between the two settings. This underscores that proper initial- ization through SFT is critical for unlocking the potential of GRPO in reaction reasoning tasks.
Table 5: Exact Match performance (%) under different SFT and GRPO settings. On the base model with enhanced initial reasoning capabilities (w/ SFT), applying GRPO yields relative accuracy improvement of 13.9%.
w/o SFT
w/ SFT
w/o GRPO
6.52
59.93
w/ GRPO
9.67
68.24
3.4 ANALYSIS OF INCORRECT PREDICTIONS
Through systematic error analysis comparing L.M predictions with ground truth, we identified two major failure modes: (1) complex reactions involving multiple functional groups or multi-step processes, and (2) incomplete reaction conditions (c.g., missing temperature or catalysts, which is confirmed by GPT-4o). Figure 4 presents an example of incorrect prediction along with detailed analysis using GPT-4o and retrosynthetic validation.
Analysis from GPT-4o
Ground Truth
Exist retrosynthetic route Retrosynthetic
Acidic or neutral medium (pll 6), CuCl (0.1-1 equiv) and stir at 25-40 "C' generate radicals and promote aryl coupling.
Reaction inputs
M1
+
Analysis from GPT-4o
Predicted by LLM
Need NaOH in aqueous solution (basic medium, pHl 7-8), maintain low moderate temperature, and slowly add the diazonium salt the amine solution under stirring.
Retrosynthetic
reaction
Planning
inputs
product
Figure 4: Analysis of incorrect prediction using GPT-4o and retrosynthetic validation. The key reac- tion conditions influencing final product marked in bold red. Both products are valid candidate answers in the absence of specific condition constraints.
Fundamentally, many organic reactions inherently generate byproducts via parallel or competing pathways, yet existing datasets typically record only to three major products. This exposes

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critical limitation in current evaluation metrics for reaction prediction tasks, where exclusive com- parison to a single ground truth fails to reflect chemical reality and may hinder LLMs from devel- oping genuine understanding of organic reaction mechanisms.
To address this, we propose a novel evaluation paradigm by incorporating retrosynthetic validation. For each product predicted by reasoning- LLM,, we verify whether plausible retrosynthetic route exists based on the given reaction inputs. chemically reasonable, a prediction is deemed correct even if it does match the ground truth. Applying the retrosynthetic analysis tool Retro* (Chen et al. 2020) to our previously mispredicted examples, 47.8% of them are validated as chemi- cally reasonable, bringing the total fraction of reactions passing retrosynthesis validation 92.64%. Appendix reports experiments for the baseline models using the retrosynthetic validation method.
4 CONCLUSION
system that In this study, we introduce Reaction-Thinker, a hybrid, RAG-based LLM with reasoning- significantly advances organic reaction prediction by combining based LLM. Experiments on ORD demonstrate that our system achieves state-of-the-ar (SOTA) re- sults in Exact Match and fingerprint similarity, outperforming all compared LLMs and task-specifir models. The result shows the potential of leveraging LLMs for addressing fundamental challenges in chemical research. We also identify several promising directions for future enhancement. For example, the reasoning-based LLM still has significant room for improvement. The optimization of reward functions will be our focus in further research to help LLMs have better understanding of organic reaction mechanisms. Moreover, enhanced CoT datasets incorporating chemical synthesis processes will be developed and integrated into current training framework, enabling more rigorous analysis of how reaction condition variations affect outcomes. Last but not least, current system implements RAG and reasoning as separate LLM modules, and future work will integrate these capabilities into unified architecture.
REPRODUCIBILIITY STATEMENT
We have made the code and data publicly available to support reproducibility. The experimental details and GitHub link can be found in Appendix.
ACKNOWLEDGMENTS
This work was supported by the Strategic Priority Research Program the Chinese Academy of Sciences under Grant No. XDA0480303 and the National Science and Technology Major Project under Grant No. 2023ZD0120901. The Al-driven experiments, simulations, and model training were performed the robotic Al-Scientist platform of the Chinese Academy of Sciences.
ETHICS STATEMENT
This research complies with the ICLR Code of Ethics. Our study uses publicly available benchmark data and does not involve human subjects or collection of sensitive information. The authors declare no potential conflicts of interest sponsorship that could influence the work reported in this paper.
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A
APPENDIX
A.1
RELATED WORK
A.1.1
RETRIEVAL-AUGMENTTD GENERATION IN LLMS
RAG can be an effective paradigm for infusing LLMs with non non-parametric knowledge (Gao al. 2023 Gupta et al. 2024 Li et al. 2025), with demonstrated impact in knowledge-intensive domains such as medicine (Li et 2024) and law (Wiratunga et al. (2024). By retrieving and conditioning on external documents, RAG significantly improves performance
generation tasks. RAG methods
can be broadly categorized into single-round and multi-round strategies. Basic RAG approaches typically retrieves knowledge based solely the initial query (Guu et al., 2020; Borgeaud et al.) 2022). Some works have also explored multi-round retrieval strategies that iteratively refine or rewrite queries across steps (Shao et al. 2023) Jiang et al. (023),, interleave retrieval with reasoning (Trivedi et al. 2022). or utilize multi-stage self-asking mechanisms (Press et al. 2022). Depending on the task, either single-round multi-round retrieval strategies can be employed.
A.1.2 REINFORCEMENT LEARNING FOR COT REASONING
Chain-of-Thougee (CoT) reasoning (Trivedi et al., 2022) represents significant methodological advancement in enhancing the reasoning capabilities of LLMs. This approach prompts models to explicitly generate intermediate reasoning steps before arriving a final output. Such structured rea- soning processes substantially improve prediction accuracy, as higher-quality intermediate contexts often contribute to more reliable and consistent final results. Reinforcement learning (RL) has also emerged as a powerful technique for improving the reasoning ability of LLMs, particularly in do- mains such as mathematics, where structured reward signals allow models to learn beyond what SFT alone can achieve. Recent developments have introduced RI. frameworks with numerical feedback, often relying on online policy optimization algorithms such as Proximal Policy Optimization (Schul- man et al. 2017), Group Relative Policy Optimization (GRPO) (Shao et al. (024)) REINFORCE 1992). and Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) Yu
(Willimms
et al. 2025
A.1.3 ARTIFICILL INTELLIGENCE APPLICATIONS IN CHEMISTRY
Artificial intelligence has found extensive application in the chemistry domain, using deep learning to learn from large-scale data and thereby accelerating research in complex tasks. The key areas include molecular design, property prediction, and reaction-related applications. In molecular de- sign, the goal is to generate small molecules with desired properties while maintaining synthetic accessibility. Current approaches commonly use language models (Edwards et al., 2025), autoen- coders (Hong al. 2019), and diffusion models (Wang et al. 2025a),, which together enable flexible and targeted compound generation. For molecular property prediction, the task involves forecast- ing properties based on molecular structure. State-of-the art methods largely rely on pre-trained Transformer models (Song et al., 2023) and GNNs (Prakash et al. 2023).
In cnnction-relate tasks, forward reaction prediction aims to predict reaction outcomes from given reactants and reagents. Traditionaa Task-specific models for predicting organic reaction outcomes can be mainly categorized into template-har based and template-free methods. Template-
methods integrate machine learning with predefined structural transformation rules, also known reaction templates, either curated by experts or extracted from atom-mapped datasets (Chen & Jung 2022] Sacha et al. )021). In contrast, template-free methods employ data-driven architectures, such as graph neural networks (GNNs) or Transformer-
sequence models, to infer reaction patterns directly from large reaction corpora without relying on explicit templates (Schwaller et al. 2018
Irwin et al. 2022). LLM-based reaction predictors combine chemical text pretraining with 2Sq supervised muninnnnnggg to generate product SMILEES from reactant inputs (Christofidellis al. 2023 2024b). Recent ad advances further integrate reasoning modules to enhance mechanistic fidelity and prediction accuracy (Zhao et al.) 2025b). Retrosynthesis planning works in reverse by deducing viable starting materials and intermediates to propose synthetic routes (Wang et al.)
Zhao et al.)
2022 Zhao al. 2025a Yao et al., 2024). Meanwhile, reaction condition recommendation seeks to suggest catalysts, solvents, and other reaction parameters for given transformation (Gao et al.)
2018 Wang et al. 2025b Qian et al. 2023).
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A.2
SUPPLEMENTARY MATERIAL
Code and data at
com Berr/Guliion
A.2.1
CHAIN-OF-THOUGHT DATASET
Supplementary to Section CoT Dataset for Reaction Reasoning in the main text.
Data Generation Script
The data generation script and associated prompt template for Section Stage Preliminary Con- struction has been made publicly available in CoT-Gen.py and User-Prompt.txf, respectively.
The prompt first provides explicit high-level instructions, guiding the model to assume the role of an expert chemist and to articulate the reaction reasoning process in step-by-step manner.
Prompt Template
You are an expert-level Al chemist capable of explaining the reasoning process for chemical reactions step by step.
You will be given standardized chemical reaction equation, including reactants, reagents, and products.
Your task construct reasoning datasct based this equation.
For each reasoning step. provide a describing what you
step and its content.
Requirements: Use reasoning steps. Verify given answer through different methods. Consider potential alternative answers and explain why they are rejected. Contemplate the possibility of error: If your reasoning is wrong, identify where the eπrо could occur. Fully test all other possibilities where might be mistaken. When you say you rechecking, genuinely recheck using another method instead of merely stating that you are rechecking. Format your response JSON. Each reasoning step should include the following keys:
"Title": : A description of the current reasoning step.
"Content": A detailed explanation of this step.
"Next-Action": Either "continue" or "final answer".
Subsequently, a concrete reaction example is included to demonstrate the expected reasoning struc- ture and output format, enabling the LLM imitate both the logical progression and the structured ISON representation during data generation. The textual descriptions corresponding to the key re- action mechanisms are highlighted in bold.
Reaction Reasoning Example
Reaction inputs:
Reaction output:
"Title": Analyzing the given reactants and reagents
"Content": The reactants and reagents include
molecule with central aromatic ring sub- stituted with pyridine moiety, and [F-], fluoride ion.
"Next-Action": "continuc"
"Title": Hypothesizing the possible reaction mechanism
The reaction may involve the following reaction centers: The silicon atom in the silyl ether group and its attached oxygen atom; The incoming fluoride ion interacts with the silicon. The reaction describes the transformation of silyl ether hydroxyl group. This process typically involves a deprotection reaction where the silyl protecting group is removed, exposing the original alcohol. Here, the fluorine acts as a nucleophile the mechanism, which common for desilylation reactions.
"Content"::
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"Next-Action":
continue
"Title":
Predicting possible reactions
"Content": Here are possible mechanistic steps reaction. Nucleophilic attack: The fluoride ion attacks the silicon atom of the silyl ether. Silicon has a high affinity for fluorine due to its ability to form strong Si-F bonds. Cleavage of Si-O bond: As the Si-F bond forms, the Si-O bond is weakened and subsequently cleaved. This releases the Si group (usually a volatile SiF compound), and the hydroxyl group restored. Reformation of hydroxyl group: With the removal the silyloxy group, hydrogen from the solvent or environment can bond to oxygen, completing the transformation to the hydroxyl group.
"Next-Action": continue
"Title": Validating the reaction pathway
nucleophile and attacks the silicon atom in the TBDMS group. Upon attack by fluoride, the silicon center forms a pentavalent transition stale, which is relatively stable because silicon can accommodate five bonds due to its size and electronic structure. This intermediate is often depicted with negatively charged fluoride attached to silicon, briefly forming complex where the oxygen-silicon bond is weakened. As the intermediate collapses, the Si-O bond between the silyl group and the oxygen atom is eleaved, breaking the silyl ether linkage and releasing free alcohol (-OH). After Si-O bond has been cleaved, the molecule is left with a free hydroxyl group (-OH) in place of the silyl ether. The Si-F bond formation results in the formation of tert-butyldimethylsi fluoride (TBSF) as a byproduct. TBSF is relatively stable and does not interfere with the reaction further.
"Content": Fluoride ion (F-)
"Next-Action":: continue
"Title":
Considering alternative possibilities
"Content": The first possible reaction is that fluoride could attack decctron-deficien aromatic ring near the nitrogen atoms, potentially displacing a substituent a nucleophilic aromatic substitution. However, Nucleophilic aromatic substitution (SNAr) generally requires strong deectro-wwithdrwiinn groups ortho para to the leaving group, which are not present in this molecule. Fluoride is more likely target the silicon atom in the silyl group due silicon'ss 's affinity for fluoride and the relatively low activation energy required for Si-O hond cleavage. The second possible reaction is that the nucle- ophilic fluoride could theoretically attack the methyl groups attached to the tertiary nitrogen, leading to N-dealkylation and removing one or both methyl groups. However, N-dealkylation would typically require more electrophilic carbon (such interaction very strong, making the silyl ether bond much
benzylic allylic carbon) harsher conditions, such
oxidation. In contrast, the silicon-ftuoric more susceptible to fluoride attack than the N-C bonds in the tertiary amine. Fluoride ions could, under special conditions, participate in deprotonating an aromatic C-H bond or causing other activation of the aromatic ring. However, Aromatic C-H activation is highly unlikely under these conditions and would generally require transition-metal catalyst and more stringent conditions. The Si-O bond in the silyl ether is far more reactive toward fluoride under mild conditions. The last possible reaction could be a fluoride- mediated deprotection of tert- butyldimethylsilyl (TBDMS) group. restoring the free hydroxyl (-OH) functionality in the aromatic compound. The high affinity between silicon and fluoride drives the reaction forward. The formation of the strong Si-F bond makes the reaction favorable and specific for silyl deprotection. The fluoride ion selectively attacks the silicon center due its high electrophilicity, leaving other functional groups (like the tertiary amine and aromatic rings) untouched.
fluoride-mediated
"Next-Action": continue
"Title": Confirming the reasoning process
"Content": After reasoning and validation,
CNCCpeloc((2nnesee(((H)))ca is confirmed be the final product.
"Next-Action"; continue
"Title": Final reasoning result
"Content": Based on the above analysis, the reaction product
CNCCpllcc(<2mmeccc((())ecaag Instead of the silyl ether group, there is a hydroxyl (OH) group.
":ext-Action": final-answer
17

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Published as a conference paper at ICLR 2026
Open-Source Samples
curated subset 100 randomly selected examples is provided in Open-Source,jsony for demon- stration purposes. The full dataset, consisting of 119K (from USPTO-MIT) and 575K (from ORD) reaction reasoning samples, is available at our GitHub repository.
A.2.2 TRAINING DEEPSEEK- - -II---IIIILNNN -WEILL
Supplementary to Section Stage 2: Distillation and Validation of CoT Dataset for Reaction Rea- soning in the main text. We document the experimental setting and training progress of DeepSeek- I.-isstll-Qwen--BB during the CoT dataset construction process.
Experimental Settings and Results
The implementation details are specified in the scripts SFT-D and GRPO-DeepSeek- 7B.sh. The experiments are run on 8 NVIDIA A800 GPUs.
We record the reward curves during GRPO in Figure 5 We also evaluate the final models after SFT and GRPO, with detailed results presented in Table This is preliminary study to validate the integration of LLMs and RL for organic reaction prediction. This pilot study, which also yields dataset of CoT reasoning traces, provides compelling evidence for the viability of our method. However, anticipating the limitations of 7B model, we proceed with more powerful DeepSeek- RI RL-Distill-Qwen-322 in the main experiment.
Training Reward
0.8
0.2
500
1000
1.500
2000
2500
Steps
Figure 5: The reward curve during GRPO training for DeepSeck-Rt-Distill--wen
Table 6: The Validity and Exact Match performance of DeepSee--RI-Disiill-Quee the USPTO-MIT test set after SFT and GRPO, respectively.
Validity (%)
Exact Match (%)
After SFT
86.4
22.4
After GRPO
91.9
35.9
A.2.3 TRAINING REACTION TYPE CLASSIFIER
Supplementary to Section Reaction Type Classifier.
Experimental Settings and Results
The training and test code, as well as parameter configurations are implemented in Classifierpy file.
We randomly shuffled the raw Schneider-50K dataset and split it into 40K training set and 10K validation set. The final classifier achieves Top-1 accuracy of 94.35% on the validation set.
A.2.4 TRAINING RAG-BASED LLM
Experimental Settings
The implementation details are specified in the script on 8 NVIDIA A800 GPUs.
All experiments are run
18

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Published as a conference paper at ICLR 2026
A.2.5
TRAINING REASONING- BASED LLM
Experimental Settings
The SFT implementation details are specified in SFT-DeepSek-22-..tt SFT-DeepSeek-328 2.sh. The GRPO implementation details are specified in GRPO-DeepSekk-22-... and GRPO-
DeepSeek-32B-2..0 All experiments are run on NVIDIA A800 GPUs.
A.3 ADDITIONAL, EXPERIMENTS
A.3.1 RETROSYNTHETIC VALIDATION
This is the supplementary to Section Analysis of Incorrect Predictions. We conduct additional experiments for the baseline models using the new retrosynthetic validation method (in Table 77. Retro Validity assesses whether plausible retrosynthetic route exists based the given reaction input for products predicted by LLM. It is necessary to note that in our new experiment, Reaction- Thinker records retrosynthesis validation for both Reasoning-basse and RAG-based LLMss As result, the score increases from 92.64% to 93.89%. These results demonstrate the superior Retro Validity of our method.
Table 7: The results for GPT-4o (marked t) are based randomly sampled subset of 500 instances from the ORD test set. The top results are highlighted in bold.
Model
Model Type
Retro Validity (%)
Chemformer
Task Bask-Specific Model
92.91
GPT-4o
31.32 i
DeepSeek-R1
13.89
Qwen2.5-72B-Bme IIstruutt
1.44
DeepSeck-RI-Be -i-bskkk------iilll----- Hel--Dstilll-aamae
General LLM
8.22
DeepSeck-RI-Distil--Oeee
7.83
DeepStek-RI-DDstiill--eae
1.98
DeepSeed-RR--Disiil--eeeeee
1.90
ChemDFM-113
$8.60
ChemDFM-8B
Chemical LLM
$7.48
Text-Chem-TS
53.56
Reaction-Tiiee Thinker
93,89
A.3.2 RESULTS ON USPTO-MIT
We add the evaluation results on the USPTO-MIT test set (in Table Ky. Here, we focus on evaluat- ing task-specific models and chemical LLMs, and introduce LocalTransform (Chen & Jung 2022) (which also demonstrated excellent performance on the USPTO-MIT dataset) new baseline.
It is worth noting that approximately 65% of the USPTO-MIT test samples already appear in the ORD training set. To ensure a fair comparison, we evaluated Reaction-Thinker two settings, on the full USPTO-MIT test set, and a filtered version (marked t) that excludes all samples seen during training.
A.3.3 RESULTS ON RETRIEVED AND NON-RETRIEVED SUBSETS
We extend our evaluation to report baseline and ablation results on two subsets the ORD test set, including Retrieved-case subset (reactions for which at least one similar-case was retrieved) and No-retrieval subset (reactions for which no similar-case was found the retrieval library) (in Table 9). The results indicate that RAG-based LLM clearly benefits when similar reaction cases are retrieved, while on both subsets, the Reasoning- based LLM and other basclines show relatively small performance differences.
19

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Published as a conference paper at ICLR 2026
Table 8: Performance comparison of our method and various baseline models on the organic reaction prediction task using the USPTO-MIT test For task-specific models, directly report the results (Exact Match) from their original publications.
FTS (%)
Model
Model Type
Validity (%)
Exact Match (%)
MORGAN
RDK
AVALON
Chemformer
90.9
Molecular Transformer
Task-Specific Model
88.6
LocalTransform
90.8
ChemDFM-13B
98.45
50.83
76.96
81.89
82.04
ChemDFM-8B
Chemical LLM
98.08
48.76
74.88
80.03
80.16
Text-Chem-115
98.27
50.15
76.67
81.93
Reaction-Thinge
99.04
92.13
95.92
96.91
96.98
Soaction-Thinker
99.02
90.90
95.40
96.46
96.51
Table Exact Match performance on Retrieved-case and No-retrieval subset.
Exact Match (%) Retrieved-case subset
Exact Match (%) No-retrieval subset
Model
Chemformer
88.50
86.48
DeepSeek-R1
11.82
11.05
Qwen2.5572BB-nstrmuet
0.55
0.50
ReepSeet-RI--Dstiil-Laaaa
7.17
7.33
DeepScck-R11 1--Sstt
Sail---wwn3222
6,58
6,25
DeepSeck-RI-Distil-O-wwe -weesiiil-------4BBB
1.61
2.05
DeepSee--RI-Disill--eeeee
1.08
1.79
ChemDFM-13B
52.93
50.09
ChemDFM-8B
48.37
46.46
Texi-Chem-T5
47.91
47.84
Reaction- Teacker (Reasoning-basete
68.57
68.24
Reaction-Thinkee (RAG-based)
94.70
31.19
A.3.4 PERFORMANCE ROBUSTNESS
We conduct thorough analysis of base models with varying parameter sizes (e.g., 7B, 8B, 14B, 32B) to systematically evaluate performance robustness (in Table TOp. We perform analysis on the ORD dataset using the same training and test splits as EraatioonThinkee (32B).
The results demonstrate that our method (combining human-chemist-sslle reasoning and key LLM methodologies) performs robustly across various model scales, outperforms other chemical LLMs of similar size (ChemDFM-133) 13BB, ChemDFM-SB, and Text-Chem-T55 and shows consistent perfor- mance improvements as the number of model parameters increases.
Table 10: Exact Match performance of RAG-based and Reasoning- based LLMs across various model parameter sizes on the ORD dataset. The Total Accuracy is the final result obtained by weighting Qwen3 and DeepSeek-Rt-Dissill-ouee
81.7% and 18.3%. respectively.
Model Scaling
Model
Exact Match (%)
Total Accuracy (%)
Qwen3-328
94.70
32B
89.86
DeepSeek-PR R RI--ittillll RI--Disill--Qwen332D
68.24
Qwen3-14B
91,08
14B
85.89
DeepSeck-RI M-i-iisill--Qwe--1143
62.73
Qwen3-8B
90.61
84,53
DeepSeek-RI Se-i-itilll-wenneee
57.39
Qwen3-7B
89.87
7B
83.69
DeepSeek-RR I Beaill-Qwee--BBB
56.12
20

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Stage-2: We used the filtered CoT for SFT. Then applied RL on the SFT-model, and Published as a conference paper at ICLR 2026
A.4 INTERPRETABILITY ANALYSIS
In this section, we try to give a detailed analysis of the interpretability advantages provided by the Reaction-Thinker We elucidate these advantages from two main perspectives.
A.4.1 HUMAN- CENNERDD EXPLANATIONS
Our method generates outputs specifically for human chemists, providing reasoning process that aligns with their professional mindset. delivers not just a simple answer, but a step-by-step ra- tionale. This allows users quickly grasp the reaction mechanism and assess the credibility of prediction by examining the correctness of the reasoning logic. Consequently, our interpretability directly serves mechanism-driven organic reaction research, going beyond prediction task.
A.4.2 RELIABILITY OF THE REASONING PROCESS
It is well-known that LLM reasoning hallucinations common. Even when the final answer is correct, the intermediate chain-of-thought may be unfaithful. We enforce CoT quality through three progressively stringent checks for this task:
Format Compliance: Whether the reasoning follows standard template.
Framework Conformance: Whether it matches our predefined reasoning framework.
Detailed Correctness: Whether the chain correctly tracks molecular structures, functional groups, and reaction mechanism.
In practice, we concentrate on the first two levels (format and framework) because they can be efficiently filtered using keyword and structural checks during large-scale data cleaning. As detailed in Section CoT Dataset for Reaction Reasoning, our data pipeline is as follows:
Design CoT Template: We manually designed high-quality CoT templates. We used GPT generate reasoning chains from selected samples, which were then reviewed and cor- rected by both human chemists and GPT.
Stage-1: We generated numerous reasoning chains using these templates, and filtered out those violating format framework requirements.
collected correct reasoning chains as additional training data.
In the analysis of cases where the final product prediction correct, find that some reasoning chains contain detail errors including:
Incorrect descriptions molecular structures
Misnaming or mis-functionality of functional groups
Mistakes in the predicted reaction mechanism
We present a visualization of detailed analyses for two representative cases. One is the CoT Template case (in Figure(6). and the other is case where the result is correct but the reasoning process contains the various issues listed above (in Figure 7).
Considering that verifying detailed mechanistic correctness at scale is extremely time-consuming, test small sample of 50 chains. We observe that our fully trained model (SFT + RL) contained mechanistic errors in about 20% of the cases where the final product was correct. In contrast, the Stage-1 SFT-model had significantly higher error rate of 40-60%. This indicates that our carefully constructed data and effective training methodology substantially reduce mech- anistic errors while improving prediction accuracy. In other words, the explanations generated by Reaction- Thinker are highly consistent with the correct predictions, highlighting the reliability of the reasoning process.
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Published as a conference paper at ICLR 2026
Reaction inpuls
Reaction output
(Analyzing the given reactants and reagents)
The reactunts und reugents include CN(C) locol 22w2kx/cw0sssscccCCCCCC/caaaeee
Analyze the reaction inputs
molecule with a central aromatic ring substtitutd with a pyridine moiety, and [P-], fluoride ion.
Verified as correct
Hypothesizing the possible reaction mechanism)
The reaction may involve the following reaction centers: silicon storn silyl ether group and its attached oxygen utom; The incoming fluoride ion interacts with the silicon. The reaction may include transformaionn a silyl ether to a hydroxyl group. This process typically involves deprotection reaction where the silyl protecting group is removed, exposing the original alcohol. Here, the fluorine ion ([F-) I) may act as nucleophile in the mechanism, which is common for desilylation reactions.
Predict reaction mechanism
(Predicting possible reactions]
Verified
Here are the possible mochanistic steps of this reaction. Nucleophilic attack: The fluoride ion attacks the silicon stom of the silyl ether. Silicon has high alfinity for fluorine due to its ability form strong Si-F bonds. Cleavage of Si-O bond: As the Si-F bond forms, the Si-O bond is weakened and subsequently cleaved. This releases the group (usually as volatile SIF compound), and the hydroxyl group is restored. Reformation of hydroxyl group: With the removal of the silyloxy group, bydrrgen from solvent environment can bond the oxygen, completing the transformation to the hydroxyl group.
I (Validating the reaction pathway)
Predict reaction pathway
Fluoride ion (F-) acts as nacloophile and attacks the silicon stom in the TBDMS group. Upon attack pentavalent transition state, which relatively stable because silicon can uccommodate five bonds due to its size and electronic structure. This intermediate is often depicted with negatively charged fluoride attached to silicon, briefly foming complex where oxygen-silicon bond is weakened, As the intermediate collapses, the Si-O bond between the silyl group and the oxygen atom is cleaved, breaking the silyl ether linkage and releasing free alcehol (-OH). After the Si-O bond has been cleaved. the molecule left with free hydrasyl group (-OH) in place of the silyl ether. The Si-P bund formation results the formation of tert- let-bbuyldimetthllissy fluoride (TBSF) hyproduct. TBSP is relatively stable and does not interfere with the reaction further.
fluoride, the silicon conter
Verified as correct
|Considering alternative possibilities) The first possible reaction
the fluoride could attack the clecton-deficicnt aromatic ring near the nitrogen atoms, potentially displacing substiment in a mucleophilic anomatic substitution. However, Nacleophilic aromatic substitution (SNAr) generally requires strong electron-wiite witldwwwwii groups (e.g.-NO.) ortho or para to the lcaving group. which are not present in this molecule. Fluorido is more likely target silicon atom the silyl group due to silicon's affinity fluoride and relatively activation energy required for Si-O bond clenvage. The second possible reaction is that the nacleophilic flaoride could theoretically attack the methyl groups attached to the tertiary nitrogen, leading to both methyl groups However N- N-dcalkylation would typically require more electrophilic curbon (such as a benzylic or allylic carbon) or harsher conditions, such as oxidation. silicon-flurrieee interaction very strong, making the silyl ether band much to fluoride attack than the N-C bonds in the tertiary amine. Fluoride ions could, under special conditions, participate deprotoraainng However, Arematic C-H activation is highly unlikely under these conditions and would generally require hond
N-dealkylation and removing
Altemative Possibilities
In contrust, the silicon- fluoride
susceptible
Verified as correct
aromatic C-H bond causing other activation of aromatic ring.
transition metal catulyst and more stringent conditions. toward fluoride under mild conditions. The last possible renction could be flaoridemediitee deprotection a Nort-butyldin ldimcthyysiii isilyl (TBDMS) group, restoring the free hydroxyl (-OH) functionality in the aromatic compound. The high affinity between silicon and fluoride drives the reaction forward. The formation of the strong Si-F band makes the reaction favorable and specific for silyl deprotaction. The flooride ion selectively attacks the silicon center due its high electrohiliiity, larving other funetional groups (like the tertiary amine and aromatic rings) untouched.
silyl ether is more reactive
(Contirming the reasening process] After reasoning and validution, CNCCsslooc(comcccc(((()))eeeeee is confirmed he product.
Confirm the final output
(Final reasoning result)
Based on the above analysis, reaction product CXCeeeeessc//2ueeeooo///hccheeee Instead of the silyl ether group, there is hydrosyl (OH) group.
Verified as correct
Figure 6: Detailed analyses for the CoT Template case. All critical parts are verified correct.
22

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Published as a conference paper at ICLR 2026
Reaction inputs
(1) O-C(O)C(F(FFI
(2) Coclec2dee COCUc22CIINCCCNICCCCCCCO.
(3) CCN(CIC)CYOXCCCC
(4) 0=Celo22elen))OCCO0
HO
(5) <C(-D)O1BB=-000000-00000000
Na
(6) [Na+]
(7) CICCI
(8) CO
Reaction output
C0ctoc2ce BBlooz///CCCCCCCCCCC/////CCCCCCC
<think>
Analyze the reaction inputs
So need analys the reaction by slep reach the final answer. Here is step by-slep analysis.
Verified incomret
(Analyzing the given reactants and reageuts)
The reactants and reagents include: (FC) (riifueroseeric aeid),
CONCCCOCCCC
C0elcoc2tte (ecplclcee(ccCCCcCCccc INCCCCIICCCCCIJX<-0JCCCC (a complex anomatic componnd with an amide and firstional groups), CCN(CCCC)XCC (tributy famine', O-Cel leclelent JOC002 substitted benzens with an amide and ether), CC(-O)O(DBI- |JOO(C)=)OC((C- (a borohydride reugent with ester groups). [Na+] (sodium lon), CICCI (dichloromethane.. and CD (carbon manoxide).
Actually S-uulylldiisoppopylamit called DIPEA.
O=Colec2e(en JOC
Actually an aromatic heterocycle bearing an aldekyyd functional group.
(ldentifying the key fumetional groups und reagents)
key functional groups reactants include carboxylic acids, amines, amides, others, esters. The reagents include trifhorrccetic acid (TFA), tiburyylamiie (TBA), barobydride (BH-), and sodium ion (Na-), Dichlormethase (CICCl) and carbon monoxide (CO) also present, but their roles need determined.
CO
Not carbon monoside, but methamol.
[Hypothesizing possible reaction mechunism)
Predict reaction mochanism Predict reaction pathway
The rouction likely involves the following steps: 1) Deproteciom of a'midd and esher groups using TFA. Nuelacyhiili bllc bunds through coupling reactions, 4) Solvent effects and role of dichormmehhhee und surbon monoside.
hombydride reagent the carbonyl groups. Formation of amide
Verified incorrect
(Predicting primary reaction pathway)
Carboxylic acids
Deprotection: TFA deprotoct the amide and ester groups, leading the formation of free carbosylic alcohols. 2) Nucleophilic
Nu carbosylic acids actually.
can reduce carbonyl groups alcohols form now smide bonds. 3) Coupling The free carbonylic acids and amines can furm amide bunds through a coupling reaction, possibly catalyzed Na
CONCCCOOCCC
Not TBA, called DIPEA.
CC[-O)O[BII- 11--o00[B---OCCC--OOX000--
(C Considering the role of dichloromethane and carbon monoxide) Didhloromethase (CICCI common solvest organic reactions. stabilize intensedictes transition states. Carbon monoxide (CO) can act a ligand in transition metal catalysis or reducing agent certain conditions. However, this context, more likely byproduct stabilizing agent.
[Na-]
redacing agent used for reductive aminition, not generic Bll
TFA
(Predicting the final product) The final product COc loeccceeeNNCC
reductive amination, promotes iminium Fontation,, but deprotccting reagent. Excess TEA hinders imine formarion interferes with the reduciant.
This
compounn has the same aromaric stricture as the starting material but with the amide and ether groups modi fied. new group reduced to an alaohel.
between the amine carboxylic scid groups, the
(Considering alternative possibilities)
Altemative deprolection: TFA might deprotect other functional groups, but the amide and ester groups are the most likely tirgets due their reactivity, Altemative mucloophilic anack: could anack other carbunyl groups. but the amide and ester groups are the most reactive, 3) Altemative coupling: The coupling reaction could form different amide bends, but the formation the new amide bond is the energetically favorable.
Actually is the resction input, but not byproduct.
Altenattve Possibilities
[Final reasoning result)
Based on a false premise, all the conclusions therefore meaningless.
Based on the above analysis, the renction product is cocloo22tc)))Ne WWNCCCNICCNCCckeao (acpe//ccaejjoooooocccc////////0 The amide bond formed between the amine and the carboxylic acid groups, and the ester group reduced to an alcohol.
<hhink?>
Confirm ourput
Under stated CohempCCelcoodd
expested products of are
Verified as correct
Figure 7:
In this case, the final product is correct, but the model exhibits substantial errors in rea- soning about molecular structures, functional-group identification, and the reaction mechanisms. Content shown in green is correct, content in red is clearly erroneous, and content in yellow may be subject debate.
23


Reviews:
[{"review_id": "b3JOLQ0jGf", "reviewer": "Reviewer_1yzM", "summary": "This paper introduces Reaction-Thinker, a system for organic reaction prediction with the following pipeline: 1. a 2-layer MLP that takes molecular fingerprints and generates a reaction embedding and reaction classification; 2. a reaction embedding database for RAG; 3. a RAG LLM for reactions that have similar reactions in the database, trained with SFT; 4. a reasoning LLM trained with SFT and GRPO for reactions that don’t have similar reactions in the database.", "questions": "1. The paper mentions the performance of RAG on reactions for which a reaction was retrieved, as well as the performance of the reasoning model on reactions for which no reaction was retrieved. I’m curious what the numbers are for the baselines and ablations for these two subsets as well.\n\n2. For the new evaluation method with retrosynthetic validation, only the number for Reaction-Thinker was given. I’m curious what the results are for the baselines? i.e., what does an additional column in Table 1 look like for this new evaluation method?\n\n3. For the USPTO-MIT test set, only the numbers for the reasoning model part of Reaction-Thinker (with SFT and GRPO ablations) were given (in Table 5). I’m curious what Table 1, which reports results for the ORD test set only, would look like for the USPTO-MIT test set?\n\nWhile not essential, I believe that answering the above questions in the main text would strengthen the paper’s evaluation and analysis.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. *Originality:* The work presents a novel application of NLP methods (RAG, SFT/RL post-training) to chemistry tasks.\n\n2. *Quality:* The work conducts extensive experiments and ablation studies, and the training and inference procedures are well-documented.", "weaknesses": "1. The methodology itself is not novel, and the novelty lies in applying existing methods to an existing task. (Although retrieval for chemistry has been previously explored as well, see, e.g., [1]).\n\n2. The approach seems a lot more costly than, e.g., Chemformer, which may be a limitation when being applied to screen large numbers of potential reactions. (And the performance benefits w.r.t. Chemformer seem marginal.)\n\n3. The related work section (Appendix A.1) currently only mentions related work from the NLP literature. For completeness, it should also describe past work from the AI-for-chemistry literature, including the baselines the authors cited, papers cited in the introduction section, as well as potentially other works such as [1, 2, 3].\n\n4. The provided anonymous link to the source code doesn’t work—while I can see the directory structure of the repo, all files show up as “The requested file is not found.”\n\n5. By the way, there’s a formatting issue where all citations appeared to have used \\citet{} instead of \\citep{}. There are also scattered typos (line 22 missing “a”; line 706 “LLM” -> “LLMs”; etc.). \n\n[1] Qian, Yujie, et al. \"Predictive Chemistry Augmented with Text Retrieval.\" *EMNLP*, 2023.\n\n[2] Gao, Hanyu, et al. \"Using machine learning to predict suitable conditions for organic reactions.\" *ACS Central Science 4*(11), 2018.\n\n[3] Edwards, Carl, et al. \"mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model.\" arXiv preprint arXiv:2505.12565, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HKshUYmm41", "reviewer": "Reviewer_q5vK", "summary": "The paper proposes Reaction-Thinker, a hybrid “knowledge + data” framework for predicting products of organic reactions. The pipeline includes a reaction-type classifier, a similar-case retrieval library, and two predictors (RAG and CoT). The authors report SOTA Exact-Match and FTS on ORD (ORDERly split).", "questions": "1. The paper states that the retrieval library is built entirely from the training split. But what is the dataset scope used to construct the SFT and CoT corpus, is there any risk of data leakage?\n\n2. Results are reported under a single fixed threshold M (RAG subset Exact-Match 94.70%, CoT subset 68.24%, overall 89.86%). How was this M chosen?\n\n3. How is the quality of the CoT corpus ensured, and how is reaction mechanism correctness enforced? Can the method provide white-box organic reaction explanations that are consistent with the correct product prediction?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The method uses two routes that mirror practice in organic synthesis: cases with similar precedents are handled by RAG, while cases without precedents are handled by CoT + GRPO to strengthen step-by-step reasoning.", "weaknesses": "As noted in section 3.4, the Retro* check only verifies that some retrosynthetic route exists and does not enforce consistency with the given reaction conditions (precursors, catalysts etc.). Counting “retrosynthesizable” as correct may therefore overestimate forward attainability in real settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NI4464rklA", "reviewer": "Reviewer_hZJv", "summary": "This work targets the forward reaction prediction problem in organic chemistry, leveraging a combination of RAG, SFT, and GRPO techniques. Compared to prior works described in the paper, Reaction-Thinker demonstrates strong performance with enhanced interpretability.", "questions": "- In Section 2.4, how exactly is the RAG-based LLM fine-tuned? Are the retrieved similar-case samples simply concatenated with the input during fine-tuning? What happens if retrieval is applied without additional fine-tuning?\n\n- Interpretability is emphasized as one of the main motivations, but no in-depth analysis is provided. Does “interpretable” simply mean that the model outputs a reasoning trace in natural language?\n\n- During inference, there could be cases where the final predicted product is correct but the intermediate reasoning steps are wrong. Is there any analysis of such discrepancies?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The authors effectively combine existing methods—SFT, RAG, and GRPO—to tackle the forward reaction prediction task.\n- A rigorous ablation study is presented to validate the contribution of each component in the proposed system.", "weaknesses": "- It is somewhat limiting that the proposed system only addresses forward reaction prediction. Given that the system is built on RAG and LLM architectures, it would be more impactful to extend it to a broader range of chemistry problems.\n\n- Most baselines are distilled LMs from DeepSeek-R1, but comparisons with larger proprietary models such as GPT or Claude would strengthen the evaluation.\n\n- The proposed method utilizes 32B-scale Qwen/DeepSeek models, whereas the compared chemical LLMs are mostly T5-based or smaller models (up to 13B). A more comprehensive and rigorous baseline search for forward reaction prediction is needed. The related-work section should include a dedicated discussion of existing forward-reaction models, along with direct comparisons.\n\n- The authors use the USPTO and ORD datasets; however, to ensure a fair benchmark evaluation, it is necessary to evaluate on the USPTO/Pistachio dataset. Based solely on Table 1, it is difficult to clearly verify the claimed superiority of Reaction-Thinker.\n\n- The paper does not provide reproducible code or pseudocode.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)