Title: Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
Venue: NeurIPS 2025
Accepted: yes
Keywords: yield prediction

molecular representation learning

chemical reaction prediction
Abstract: Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, we propose ReaDISH, a novel reaction prediction model that learns permutation-invariant representations while incorporating interaction-aware features. It introduces two innovations: (1) symmetric difference shingle encoding, which extends the differential reaction fingerprint (DRFP) by representing shingles as continuous high-dimensional embeddings, capturing structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions at the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with an average improvement of 8.76\% on R$^2$ under permutation perturbations.

Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

Abstract

Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, we propose ReaDISH, a novel reaction prediction model that learns permutation-invariant representations while incorporating interaction-aware features. It introduces two innovations: (1) symmetric difference shingle encoding, which extends the differential reaction fingerprint (DRFP) by representing shingles as continuous high-dimensional embeddings, capturing structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions at the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with an average improvement of 8.76% on R 2 under permutation perturbations. 1

1 Introduction

Accurate modeling of chemical reactions is a fundamental problem in organic chemistry, as it provides critical insights into reaction mechanisms, predicts reaction outcomes, and guides experimental design [1-4]. Reaction representation learning is central to tasks such as reaction yield prediction [5], enantioselectivity prediction [6], conversion rate estimation [7], and reaction type classification [8]. These tasks have gained considerable attention with the rise of machine learning (ML). Nevertheless, representing reactions for ML is challenging due to the complexity of reaction spaces and the multitude of factors influencing chemical experiments like substrates, catalysts, and reaction conditions [9-11]. While many reactions may appear theoretically feasible [12], in practice, successfully executing them requires a deeper understanding of how these factors interact. Even slight variations in any of these elements can significantly influence the outcome [13, 14], making the reaction prediction problem a complex and nuanced challenge.

Despite advances in ML models, they still face two major limitations that hinder their broader applicability, as shown in Figure 1. First, many models, especially those based on sequential representations like SMILES [15], fail to account for the inherent permutation invariance of chemical reactions [16, 17]. They tend to produce inconsistent predictions when changing the ordering of input molecules (e.g., swapping reactants and reagents) or the ordering of atoms (e.g., alternative

Input set of molecules

Substructure&Interaction

HN

Pd

CI

HN

Cc1ccc(Nc2ccccn2)cc1

c1cnc(Nc2ccc(C)cc2)cc1

-NH

Pd-

c1(Nc2ccccn2)ccc(C)cc1

(a) Permutation

(b) Interaction

SMILES). Such sensitivity to input ordering undermines reliability and generalizability. While data augmentation techniques can partially alleviate this issue, training on all possible permutations is computationally inefficient and impractical at scale [18, 19]. Achieving permutation invariance is essential for robust and unbiased reaction modeling across diverse chemical scenarios.

Second, current works often fail to capture the structural interactions that drive chemical reactivity in reaction modeling. Many approaches overlook critical substructures, particularly those that change from reactants to products (e.g., reaction centers [20]), and lack explicit modeling of interactions between such substructures. Although atom-level interactions have been widely explored in molecular representation learning [21-23], capturing higher-order interactions among chemically meaningful substructures across molecules remains limited in the reaction prediction problem. This lack of interaction awareness leads to suboptimal predictions and diminishes the model's ability to capture underlying reaction mechanisms [24]. As a result of these two limitations, many models perform poorly on out-of-sample reactions that better reflect real-world chemical diversity [25-27].

To address these challenges, we propose the ReaDISH model for Rea ction prediction via interaction modeling of symmetric DI fference SH ingle sets. First, we construct chemically meaningful substructure sets named molecular shingle [28-30] sets via the circular topology for each molecule to capture key structural components. Then we compute and encode the symmetric difference of shingle sets between reactants and products adopted from DRFP [31], which is naturally robustly permutation-invariant. Second, we add intra- and inter-molecular interaction pair representation on shingle-level attention based on geometric distance, structural distance, and chemical connectivity of shingles to improve interaction awareness. We conduct numerical experiments on various reaction representation learning tasks and benchmarks, including reaction yield prediction , enantioselectivity prediction , conversion rate estimation , and reaction type classification . Experimental results show that compared with baseline models, our proposed ReaDISH achieves SOTA on prediction accuracy and uncertainty estimation in most scenarios. It increases R 2 by an average of 8.76% under out-of-sample splits when performing permutation perturbations, presenting a better generalization capability. We present related work in Appendix A. Our key contributions are summarized as follows:

We introduce a new way to present reaction structures based on the symmetric difference of molecular shingles between reactants and products, which is inherently permutationinvariant and highlights critical substructures that drive reaction outcomes.

We design an interaction-aware attention mechanism that integrates geometric distance, structural distance, and chemical connectivity between shingles. This pair representation enables the model to capture both intra- and inter-molecular interactions.

Extensive results across a wide range of tasks demonstrate that our proposed ReaDISH outperforms baseline models, achieving higher prediction accuracy and lower prediction uncertainty, especially under out-of-sample conditions.

2 Background

2.1 Problem definition

We denote the dataset of chemical reactions and their associated labels as D = { ( R i , y i ) } N i =1 , where each reaction R i consists of a set of participating molecules and y i ∈ Y is the corresponding property label. Depending on the task, y i may represent (1) a real-valued scalar for regression tasks, such as reaction yield or energy barrier, i.e., y i ∈ R , or (2) a categorical label for classification tasks, such as reaction type or success/failure, i.e., y i ∈ N .

Formally, each reaction is denoted by R i = { M r i ; M p i } = { M r 1 i , . . . , M r m i ; M p 1 i , . . . , M p n i } , where M r i ( M p i ) denotes the set of reactant (product) molecules and m ( n ) is the number of reactant (product) molecules. For simplicity and consistency, we refer to molecules other than the products collectively as reactants (including catalysts, solvents, etc.). Each molecule M j i is represented as the 3D conformer C j i = { ( a k , x k ) } N a k =1 , where a k is the atomic type, x k ∈ R 3 is the spatial coordinate, and N a is the number of atoms. The goal of reaction representation learning is to design a mapping function f ϕ : R→Y , parameterized by ϕ , such that the predicted label ˆ y i = f ϕ ( R i ) approximates the true label y i . During training, the model minimizes an objective function, such as root mean squared error (RMSE) for regression or cross-entropy loss for classification.

2.2 Molecular shingles

In cheminformatics, molecular shingles refer to structured fragments designed to capture local connectivity patterns in molecules. These shingles are typically defined as sets of atoms and their connectivity within a defined neighborhood, which are also utilized in classical fingerprints like ECFP [32], effectively representing the structural information of the molecule.

Formally, let a molecule M be represented by a conformer C = ( V, E, X ) with atoms V , bonds E , and coordinates X . We define an r -sized shingle of an atom v ∈ V as a connected subgraph induced by the center atom and its neighboring atoms with radius r , along with the corresponding bonds and their 3D positions. Let N r ( v ) denote the set of neighboring atoms to atom v with radius r . The corresponding shingle is then given by

where C [ · ] denotes the sub-conformer of C restricted to the specified subset of atoms { v } ∪ N r ( v ) . The set of all r -sized shingles in M is denoted as S ( r ) M , and its representation is invariant to the arrangement of atoms.

3 Method

3.1 Model architecture

We propose ReaDISH, a novel framework for reaction property prediction that learns permutationinvariant and interaction-aware representations of chemical reactions. The overall architecture is illustrated in Figure 2. It comprises three main components: an embedding layer that processes molecular inputs, an encoder that captures geometric and structural features, and a lightweight predictor that outputs reaction properties.

Given a reaction R = { C r 1 i , . . . , C r m i ; C p 1 i , . . . , C p n i } consisting of reactant and product molecules in 3D conformer format, ReaDISH first encodes each molecule C j i using a 3D molecular encoder f mol to generate atom-level representations X a ∈ R N a × F , where F is the embedding dimension. A shinglegeneration algorithm then extracts molecular shingles and computes the symmetric difference between reactant and product shingle sets to capture reaction-specific transformations. Next, ReaDISH aggregates the atom-level representations within each shingle through a pooling operation, producing initial shingle-level embeddings X 0 ∈ R N s × F , where N s is the number of shingles. We compute three types of pairwise relations to model interactions between shingles: geometric distances, structural distances, and chemical connectivity. These are encoded as pairwise matrices P = { P g , P e , P s } ∈ R 3 × N s × N s . To incorporate these relationships into the attention mechanism, we apply K Gaussian kernels to each pairwise matrix to produce the initial pair representation P 0 . The ReaDISH encoder

Predictor

Xt+1

LayerNorm

Attentionblock

Molecular encoder

Shingle generation

FFN

Attention block

pt+1

Pair-wise analysis

Pooling

Encoder

XL

LayerNorm

×K

Gaussian kernel

Attentionblock

Multi-head attention

PO

Xo

PO

(c)Embedding layer

Embedding

xt

Linear

GELU

Linear

pe

SSUM-

property

(a)ReaDISH

(b) Attention block

(d) Predictor

then processes the shingle-level representations using L transformer-style [33] attention blocks. Each block contains a multi-head attention (MHA) layer that integrates the pairwise attention biases P ℓ at each layer ℓ . Finally, ReaDISH uses a special [SSUM] token to summarize the reaction representation and passes it to a multilayer perceptron (MLP) head to predict reaction property ˆ y .

3.2 Embedding layer for shingles

Molecular encoder. The 3D molecular encoder f mol transforms each molecule C = { ( a k , x k ) } N a k =1 , comprising atom types a k and 3D coordinates x k , into atom-level feature representations X a ∈ R N a × F followed by layer normalization:

Symmetric difference shingle set generation.

Clc1ccccn1.Cc1ccc(N)cc1>O=S(=O)(O[Pd]1c2ccccc2-c2ccccc2N~1)C(F)(F)F..>Cc1ccc(Nc2ccccn2)cc1

In reaction modeling, comparing shingles across reactants and products allows for the precise identification of structural transformations [31]. For each reaction, we treat molecules except products as reactants. Shingles within a given radius are generated for reactants and products, respectively. The generated shingles are expressed in SMILES strings to perform the symmetric difference operation, as shown in Figure 3. This operation yields the set of shingles that are exclusive to either reactants or products, thereby identifying structural changes associated with the reaction mechanism. The complete algorithm and illustration for computing the symmetric difference shingle set are provided in Appendix C. Formally, for a reaction R = {M r ; M p } and a radius r max, we define the symmetric difference shingle set S R as

C+HN-

{[cH2][c]([nH])[CI],..,[cH2][cH][n][c]([cH2])[NH2]}

where S react ( S prod) is the shingle set for reactants (products), and △ denotes the symmetric difference operator A△B = ( A\B ) ∪ ( B\A ) . This set-based strategy is invariant to the ordering of molecules.

Shingle pooling. To construct shingle-level representations from atom-level embeddings, we apply a shingle pooling operation over the set of symmetric difference shingles. Each shingle S ∈ S R consists of a subset of atoms, and its representation is computed by averaging the embeddings:

where A ( S ) ⊆ { 1 , . . . , N a } denotes the indices of atoms in shingle S and x a i is the embedding of the i -th atom. This operation aggregates localized chemical information within each shingle, enabling the model to learn chemically meaningful shingle-level representations. The resulting set of initial shingle-level representations is denoted as

These pooled embeddings serve as the input to the subsequent encoder, which models interactions between shingles to capture the overall reaction context.

Pairwise interaction. We introduce an interaction framework that explicitly captures pairwise dependencies between molecular shingles. This formulation allows ReaDISH to model both intraand inter-molecular interactions, leveraging geometric and structural features to represent chemical transformations. As illustrated in the right panel of Figure 4(b), we define three pairwise metrics between shingles S i and S j as

where c i and c j denote the geometric centers of shingles S i and S j , respectively, and sim ( · , · ) is the Tanimoto similarity [34] of their Morgan fingerprints [35]. These metrics yield a structured pairwise representation P = { P g , P e , P s } = [ d g ij , d e ij , d s ij ] N s i,j =1 ∈ R 3 × N s × N s , where each component captures a specific interaction modality across all N shingles. The geometric distance d g ij encodes spatial relationships within the same molecule, analogous to atom-level distance modeling. The binary chemical connectivity d e ij indicates whether two shingles are part of the same molecule. The structural distance d s ij emphasizes functional dissimilarity, which is essential for modeling reactive interactions across different molecules. These pair interactions enable a finer-grained understanding of reaction mechanisms and facilitate more accurate prediction of reaction outcomes.

Gaussian kernel. After computing the pairwise interactions, we integrate these geometry-aware and structure-aware signals into the attention mechanism to better capture chemically meaningful context. To this end, we adopt the Gaussian Kernel with Pair Type (GKPT) [36], a technique effectively applied in molecular representation learning [21]. GKPT applies an affine transformation to pairwise distances based on the interaction type, followed by a classical Gaussian kernel, as illustrated in Figure 4(a). Formally, the GKPT is defined as

where G ( x ′ , µ , σ ) = 1 √ 2 π σ exp ( -1 2 ( x ′ -µ σ ) 2 ) is the standard Gaussian kernel. Here, x is the input distance, e is the pair type index, E 1 , E 2 ∈ R N e × K are learnable embedding layers, N e is the number of pair types, and µ , σ ∈ R K are learnable parameters for K Gaussian kernels. We apply separate GKPT modules to process geometric distance ( d g ij ) and structural distance ( d s ij ) along with chemical connectivity ( d e ij ) as shown in Figure 4(b). Each output is projected into the attention space and combined to form the pairwise bias term as

K

Linear

Linear

Linear

Gaussiankernel

Linear

Linear

MatMul

Mask (opt.)

Gaussiankernel

x

Scale

MatMul

(a) Gaussiankernel

where w g , w s ∈ R K are learnable projection vectors. The full interaction-aware pairwise bias matrix used in attention is denoted as

This pairwise representation encodes chemically relevant interactions and informs the subsequent attention computation, enabling the model to learn complex spatial and structural dependencies in chemical reactions.

3.3 Enhanced attention block

To incorporate geometric and structural information, we introduce an attention mechanism enhanced by pair representation. Specifically, we augment the standard self-attention with a learnable pairwise bias derived from the previously defined pairwise representations. At each layer ℓ , we update the pairwise bias p ℓ ij between shingle S i and S j using the query-key interaction:

where Q ℓ i and K ℓ j are the query and key vectors of shingles i and j at layer ℓ , respectively, and d is the hidden dimension. The attention score between shingles i and j is then computed by incorporating this updated bias into the scaled dot-product attention:

where V ℓ j is the value vector of shingle j . This formulation allows the attention mechanism to be guided by chemically meaningful geometric and structural priors. Additionally, we prepend a learnable [SSUM] token to the shingle sequence X ℓ at each layer, which serves as a global summary token for downstream reaction property prediction.

3.4 Predictor

ReaDISH utilizes a lightweight predictor, as depicted in Figure 2(d), to predict reaction properties. It consists of two linear layers with GELU activation [37]. This simple yet effective architecture maps the final [SSUM] token representation, which encodes the global context of the reaction, to the target prediction space.

3.5 Pre-training via pseudo-reaction-type classification

To enhance the generalization capability of ReaDISH, we introduce a pseudo-reaction-type classification task as the pre-training objective on a large-scale dataset. Specifically, we assign each reaction

a pseudo-label by clustering its structural representation at different granularities. This multi-scale classification setting encourages the encoder to capture both coarse-grained and fine-grained structural semantics. We generate K t pseudo-labels for each reaction. Formally, the pre-training loss is defined as

where f ϕ denotes the ReaDISH encoder, L is the cross-entropy loss, W k ∈ R F × N k with target dimension N k for k ∈ { 1 , . . . , K t } are parameters of fully connected layers, respectively, and y ( k ) n ∈ N are pseudo-labels for the n -th sample. More information can be found in Appendix D.

4 Experiments

4.1 Settings

Pre-training datasets. We collect 3.7M chemical reactions for pre-training based on the United States Patent and Trademark Office (USPTO) dataset [38] and the Chemical Journals with High Impact Factor (CJHIF) dataset [39]. We employ the DRFP [31] method with the K -means algorithm to compute pseudo-labels. More information can be found in Appendix B.

Downstream datasets. To comprehensively evaluate the effectiveness of ReaDISH, we use seven datasets across a wide range of chemical tasks, including: (1) yield prediction, the Buchwald-Hartwig (BH) dataset [13], the Suzuki-Miyaura (SM) dataset [14], the real-world electronic laboratory notebook (ELN) dataset [40], and the Ni-catalyzed C-O bond activation (NiCOlit) dataset [41]; (2) enantioselectivitiy prediction, the asymmetric N,S-acetal formation (N,S-acetal) dataset [42]; (3) conversion rate estimation, the C-heteroatom-coupling reactions (C-heteroatom) dataset [43]; and (4) reaction type classification, the USPTO_TPL dataset [8]. We consider both random and more challenging out-of-sample splits, which better reflect practical deployment scenarios. More information can be found in Appendix B.

Baselines and metrics. We use five representative models as baselines in our experiments: YieldBERT [5], YieldBERT-DA [44], UA-GNN [45], UAM [46], and ReaMVP [47], which we introduce in related work in Appendix A. We additionally compare our method with a cross-attention baseline that directly models reactant-product interactions as provided in Appendix E. Given the importance of spatial structural patterns in molecules, we adopt the SE(3)-invariant [48] Uni-Mol [21] as our 3D molecular encoder f mol for its excellent performance. For regression tasks, we report mean absolute error (MAE ↓ ), root mean squared error (RMSE ↓ ), and coefficient of determination (R 2 ↑ ). For classification tasks, we measure accuracy (ACC ↑ ), Matthews correlation coefficient (MCC ↑ ), and confusion entropy (CEN ↓ ). More information can be found in Appendix F.

4.2 Main results

ReaDISH achieves SOTA or competitive performance across diverse benchmark datasets. As shown in Figure 5(a), ReaDISH achieves top or competitive results on six datasets under random splits. It ranks first on the BH, N,S-acetal, and C-heteroatom datasets for R 2 , and the USPTO_TPL dataset for accuracy. On the SM and NiCOlit datasets, ReaDISH consistently matches or outperforms existing models. Figure 5(b) presents results on more challenging out-of-sample splits, where the test distribution differs from the training one. ReaDISH consistently outperforms all baseline models across six benchmark datasets. It shows a greater advantage under out-of-sample splits, which better reflects real-world scenarios and offers greater practical relevance than random splits, demonstrating ReaDISH's strong robustness across diverse reaction types and chemical spaces.

On the particularly challenging ELN dataset, however, all methods show limited performance with R 2 below 0.3. This underscores the difficulty in generalizing to complex, real-world reaction data. The poor results can be attributed to two main factors: (1) the ELN dataset includes a broader range of substrates, ligands, and solvents than other datasets, with significantly greater structural variability, making it harder for models to capture meaningful patterns [26]; and (2) limited fine-tuning data, which constrains the model's ability to adapt to the diverse chemical contexts. Performance regarding training size is presented in Appendix G. Full results are available in Appendix H.

(a) Random splits

(b) Out-of-sample splits

Permutation-invariant modeling enhances prediction robustness. A key strength of ReaDISH is its permutation-invariant design, which ensures consistent predictions regardless of the ordering of input molecules or SMILES tokens. To test whether this property enhances robustness in out-ofsample settings, we measure the standard deviation of model predictions across five runs, each using different random molecule orderings and SMILES permutations, the same as YieldBERT-DA [44]. As shown in Table 1, ReaDISH consistently shows the lowest prediction variance. It increases R 2 by 11.11% and 6.40% on the BH and SM out-of-sample splits (average 8.76%), respectively. In contrast, models without permutation invariance show greater variability and reduced performance under input perturbations. We show an example in Figure 6.

These findings highlight the practical benefits of permutation invariance in enhancing model reliability. By modeling interactions between symmetric difference shingles in an order-independent manner, ReaDISH offers more stable and reliable predictions under out-of-sample conditions.

4.3 Ablation study

To evaluate the contributions of individual components within ReaDISH, we conduct a series of ablation studies concerning pair representation, symmetric difference, pre-training strategies, and radius of shingles. Table 2 presents the results for the BH and SM datasets under out-of-sample splits.

C(C)(C)([O-])C.[Li+].O=CN(C)C. 01C(n2c3.....>>c1(-c2c(C)ccc3c2cnn3C2CCCCO2)ccc2ncccc2c1

C(=O)N(C)C.O1B(c2ccc3ncccc3c2)0C(C)(C)......>>c1cc2ncccc2cc1-c1c(C)ccc2c1cnn2C1CCCC01

[Li+].N(C)(C=O)C.C(C)(C)([0-])C.c1(B2O.....>>c1cc2c(cnn2C2CCCC02)c(-c2ccc3ncccc3c2)c1C

CC1(C)0B(c2ccc3ncccc3c2)0C1(C)C.N(C).....>>c1cc2cc(-c3c(C)ccc4c3cnn4C3CCCC03)ccc2nc1

YieldBERT

UAM

ReaDISH

41.5

44.3

35.2

42.3

41.0

35.2

41.8

36.2

35.2

40.9

39.7

35.2

42.1

37.2

35.2

Trueyield(%):30.3

(i) Impact of pair representation. A central feature of ReaDISH is its use of pair representation to capture interactions between molecular shingles. We evaluate three ablated variants: (1) w/o P 0 , which entirely removes pairwise modeling from self-attention, (2) w/o P g, which excludes geometric distance information, and (3) w/o P s, which omits structural distance features. The results show that removing the full pair representation causes a clear performance drop. Excluding either geometric or structural features also leads to consistent degradation in performance.

(ii) Efficacy of symmetric difference shingles. To test the effectiveness of the symmetric difference encoding strategy, we compare against a variant (w/o △ ) that processes all shingles from both reactants and products without filtering duplicates. This modification leads to a noticeable increase in error, suggesting that without explicitly focusing on molecular transformations, the model is exposed to noise from irrelevant or redundant molecular features, which hampers learning.

(iii) Contribution of pre-training. We evaluate a version trained from scratch (w/o pre-training) to measure the effectiveness of the pseudo-reaction-type pre-training task. Removing this pre-training leads to reduced performance across the board. Though the performance gap is modest compared to other ablations, it highlights that even a simple pre-training task helps the model converge to better representations of the reaction space.

(iv) Impact of shingle radius. We assess how varying the maximum shingle generation radius affects downstream performance. Using a radius of 4 yields the poorest performance, likely because it produces excessive shingles, introducing redundancy and higher complexity. A radius of 2 performs slightly worse than radius 3, indicating that radius 3 offers a good balance between coverage and efficiency.

5 Conclusion

In this work, we present ReaDISH, a novel method for chemical reaction prediction that leverages the symmetric difference of molecular shingles to model chemical interactions. Our method incorporates geometric and structural information through a shingle-level attention mechanism that captures both intra- and inter-molecular interactions. Through comprehensive experiments across multiple reaction prediction tasks, we demonstrate that ReaDISH consistently outperforms baseline models and exhibits improved robustness to input permutations, particularly under out-of-sample scenarios. ReaDISH offers a flexible and effective framework for reaction modeling and paves the way for more interpretable and generalizable machine learning models in computational chemistry. We discuss limitations and impact statements in Appendix I and J, respectively. Future directions include extending ReaDISH to more complex tasks, such as retrosynthesis planning and the incorporation of reaction conditions to enable more accurate predictions.

References

C. W. Coley, R. Barzilay, T. S. Jaakkola, and et al. Prediction of organic reaction outcomes using machine learning. ACS Central Science , 3(5):434-443, 2017.

Ian W. Davies. The digitization of organic synthesis. Nature , 570:175-181, 2019.

Markus Meuwly. Machine learning for chemical reactions. Chemical reviews , 2021.

W. L. Williams, L. Zeng, T. Gensch, and et al. The evolution of data-driven modeling in organic chemistry. ACS Central Science , 7(10):1622-1637, 2021.

Philippe Schwaller, Alain C Vaucher, Teodoro Laino, and Jean-Louis Reymond. Prediction of chemical reaction yields using deep learning. Machine learning: science and technology , 2(1):015016, 2021.

J. P. Reid and M. S. Sigman. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature , 571 (7765):343-348, 2019.

P. L. Kang and Z. P. Liu. Reaction prediction via atomistic simulation: from quantum mechanics to machine learning. iScience , 24(1), 2021.

Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. , 3:144-152, 2021.

Sina Stocker, Gábor Csányi, Karsten Reuter, and Johannes T Margraf. Machine learning in chemical reaction space. Nature communications , 11(1):5505, 2020.

Philippe Schwaller, Alain C Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, and Teodoro Laino. Machine intelligence for chemical reaction space. Wiley Interdisciplinary Reviews: Computational Molecular Science , 12(5):e1604, 2022.

Yifei Yang, Runhan Shi, Zuchao Li, Shu Jiang, Bao-Liang Lu, Qibin Zhao, Yang Yang, and Hai Zhao. Batgpt-chem: A foundation large model for chemical engineering. Research , 8:0827, 2025. doi: 10.34133/ research.0827.

Xin Hong, Qi Yang, Kuangbiao Liao, Jianfeng Pei, Mao Chen, Fanyang Mo, Hua Lu, Wenbin Zhang, Haisen Zhou, Jiaxiao Chen, Lebin Su, Shuoqing Zhang, Siyuan Liu, Xu Huang, Yizhou Sun, Yuxiang Wang, Zexi Zhang, Zhunzhun Yu, Sanzhong Luo, Xuefeng Fu, and Shuli You. Ai for organic and polymer synthesis. Science China Chemistry , pages 1-36, 2024. ISSN 1674-7291. doi: 10.1007/ s11426-024-2072-4.

Derek T. Ahneman, Jesús G. Estrada, Shishi Lin, Spencer D. Dreher, and Abigail G. Doyle. Predicting reaction performance in C-N cross-coupling using machine learning. Science , 360(6385):186-190, 2018.

Damith Perera, Joseph W Tucker, Shalini Brahmbhatt, Christopher J Helal, Ashley Chong, William Farrell, Paul Richardson, and Neal W Sach. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science , 359(6374):429-434, 2018.

David Weininger. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences , 28(1):31-36, 1988.

Z. Tu and C. W. Coley. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. Journal of Chemical Information and Modeling , 62(15):3503-3513, 2022.

M. Tavakoli, A. Shmakov, F. Ceccarelli, and et al. Rxn hypergraph: A hypergraph attention model for chemical reaction representation. arXiv preprint arXiv:2201.01196 , 2022.

E. J. Bjerrum. Smiles enumeration as data augmentation for neural network modeling of molecules. arXiv preprint arXiv:1703.07076 , 2017.

Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee. Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS Central Science , 5(9):1572-1583, 2019. doi: 10.1021/acscentsci.9b00576.

X. Wang, C. Y. Hsieh, X. Yin, and et al. Generic interpretable reaction condition predictions with open reaction condition datasets and unsupervised learning of reaction center. Research , 6:0231, 2023.

Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: A universal 3d molecular representation learning framework. In The Eleventh International Conference on Learning Representations , 2023.

Yoni Choukroun and Lior Wolf. Geometric transformer for end-to-end molecule properties prediction. In Lud De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , pages 2895-2901. International Joint Conferences on Artificial Intelligence Organization, 7 2022. doi: 10.24963/ijcai.2022/401. Main Track.

Y. Wang, S. Li, T. Wang, and et al. Geometric transformer with interatomic positional encoding. Advances in Neural Information Processing Systems , 36:55981-55994, 2023.

G. N. Simm, A. C. Vaucher, and M. Reiher. Exploration of reaction pathways and chemical transformation networks. The Journal of Physical Chemistry A , 123(2):385-399, 2018.

Martin Fitzner, Georg Wuitschik, Raffael J Koller, Jean-Michel Adam, and Torsten Schindler. Machine learning C-N couplings: Obstacles for a general-purpose reaction yield prediction. ACS Omega , 8:3017 3025, 2023.

Wiktor Beker, Rafał Roszak, Agnieszka Wołos, Nicholas H. Angello, Vandana Rathore, Martin D. Burke, and Bartosz A. Grzybowski. Machine learning may sometimes simply capture literature popularity trends: A case study of heterocyclic suzuki-miyaura coupling. Journal of the American Chemical Society , 144 (11):4819-4827, 2022. doi: 10.1021/jacs.1c12005. PMID: 35258973.

J. Bradshaw, A. Zhang, B. Mahjour, and et al. Challenging reaction prediction models to generalize to novel chemistry. ACS Central Science , 11(4):539-549, 2025.

D. Probst and J. L. Reymond. A probabilistic molecular fingerprint for big data settings. Journal of Cheminformatics , 10:1-12, 2018.

A. Capecchi, D. Probst, and J. L. Reymond. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of Cheminformatics , 12:1-15, 2020.

M. Orsi and J. L. Reymond. One chiral fingerprint to find them all. Journal of Cheminformatics , 16(1):53, 2024.

Daniel Probst, Philippe Schwaller, and Jean-Louis Reymond. Reaction classification and yield prediction using the differential reaction fingerprint drfp. Digital Discovery , 2022.

D. Rogers and M. Hahn. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling , 50(5):742-754, 2010.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems , NIPS'17, page 6000-6010, Red Hook, NY, USA, 2017. Curran Associates Inc. ISBN 9781510860964.

D. Bajusz, A. Rácz, and K. Héberger. Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations? Journal of Cheminformatics , 7:1-13, 2015.

H. L. Morgan. The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of Chemical Documentation , 5(2):107-113, 1965.

M. Shuaibi, A. Kolluru, A. Das, and et al. Rotation invariant graph neural networks using spin convolutions. arXiv preprint arXiv:2106.09575 , 2021.

D. Hendrycks and K. Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 , 2016.

Daniel Mark Lowe. Extraction of chemical structures and reactions from the literature, 2012. PhD thesis, University of Cambridge.

Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, and Ning Xia. When smiles smiles, practicality judgment and yield prediction of chemical reaction via deep chemical language processing. IEEE Access , 9:85071-85083, 2021.

Mandana Saebi, Bozhao Nan, John E. Herr, Jessica Wahlers, Zhichun Guo, Andrzej M. Zura´ nski, Thierry Kogej, Per-Ola Norrby, Abigail G. Doyle, Nitesh V. Chawla, and Olaf Wiest. On the use of real-world datasets for reaction yield prediction. Chem. Sci. , 14:4997-5005, 2023. doi: 10.1039/D2SC06041H.

Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert, Laurence Grimaud, and Rodolphe Vuilleumier. Machine learning yield prediction from nicolit, a small-size literature data set of nickel catalyzed c-o couplings. Journal of the American Chemical Society , 144(32):14722-14730, 2022.

Andrew F. Zahrt, Jeremy J. Henle, Brennan T. Rose, Yang Wang, William T. Darrow, and Scott E. Denmark. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science , 363, 2019.

Alexander Buitrago Santanilla, Erik L. Regalado, Tony Pereira, Michael Shevlin, Kevin P. Bateman, Louis-Charles Campeau, Jonathan E Schneeweis, Simon Berritt, Zhi-Cai Shi, Philippe G. Nantermet, Yong Liu, Roy Helmy, Christopher J. Welch, Petr Vachal, Ian W. Davies, Tim A Cernak, and Spencer D. Dreher. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science , 347:49-53, 2015.

Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, and Jean-Louis Reymond. Data augmentation strategies to improve reaction yield predictions and estimate uncertainty. Machine Learning for Molecules Workshop @ NeurIPS 2020 , 11 2020. doi: 10.26434/chemrxiv.13286741.

Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, and Seokho Kang. Uncertainty-aware prediction of chemical reaction yields with graph neural networks. Journal of Cheminformatics , 14(1):1-10, 2022.

Jiayuan Chen, Kehan Guo, Zhen Liu, Olexandr Isayev, and Xiangliang Zhang. Uncertainty-aware yield prediction with multimodal molecular features. In Proceedings of the AAAI Conference on Artificial Intelligence , pages 8274-8282. AAAI, 2024.

Runhan Shi, Gufeng Yu, Xiao Huo, et al. Prediction of chemical reaction yields with large-scale multi-view pre-training. Journal of Cheminformatics , 16(1):22, 2024.

Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. Se (3)-transformers: 3d roto-translation equivariant attention networks. Advances in Neural Information Processing Systems , 33:1970-1981, 2020.

Frederik Sandfort, Felix Strieth-Kalthoff, Marius Kühnemund, Christian Beecks, and Frank Glorius. A structure-based platform for predicting chemical reactivity. Chem , 2019.

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In North American Chapter of the Association for Computational Linguistics , 2019.

Jieyu Lu and Yingkai Zhang. Unified deep learning model for multitask reaction predictions with explanation. Journal of chemical information and modeling , 2022.

C. Raffel, N. Shazeer, A. Roberts, and et al. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research , 21(140):1-67, 2020.

Shuan Chen and Yousung Jung. A generalized-template-based graph neural network for accurate organic reactivity prediction. Nature Machine Intelligence , 4:772-780, 2022.

Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. On the properties of neural machine translation: Encoder-decoder approaches. In SSST@EMNLP , 2014.

R. Shi, G. Yu, L. Chen, and et al. Yieldfcp: Enhancing reaction yield prediction via fine-grained cross-modal pre-training. Artificial Intelligence Chemistry , 3(1):100085, 2025.

X. Q. Lewell, D. B. Judd, S. P. Watson, and M. M. Hann. Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. Journal of Chemical Information and Computer Sciences , 38:511-522, 1998.

J. Degen, C. Wegscheid-Gerlach, A. Zaliani, and M. Rarey. On the art of compiling and using 'drug-like' chemical fragment spaces. ChemMedChem: Chemistry Enabling Drug Discovery , 3:1503-1507, 2008.

Z. Ji, R. Shi, J. Lu, and et al. Relmole: Molecular representation learning based on two-level graph similarities. Journal of Chemical Information and Modeling , 62(22):5361-5372, 2022.

Greg Landrum. Rdkit: Open-source cheminformatics, 2024. https://zenodo.org/records/ 12782092 .

Sereina Riniker and Gregory A. Landrum. Better informed distance geometry: Using what we know to improve conformation generation. Journal of chemical information and modeling , 55(12):2562-2574, 2015.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research , 12:2825-2830, 2011.

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems . Curran Associates Inc., Red Hook, NY, USA, 2019.

Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2014.

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A Related work

Reaction performance prediction. Chemical reactions involve complex transformations among multiple molecular entities, such as reactants, catalysts, and reagents. Accurate prediction of reaction outcomes requires expressive and chemically informed representations. Early approaches rely heavily on handcrafted features as molecular fingerprint features derived from domain knowledge, to encode atomic and physicochemical properties [13, 14, 49, 40, 41]. Recent advances in deep learning for reaction prediction can be grouped into three main categories: sequence-based, graph-based, and conformer-based models. Sequence-based models represent chemical reactions using linear notations such as SMILES and apply neural sequence architectures to learn patterns in tokenized strings. Notable examples include YieldBERT [5] and YieldBERT-DA [44], which leverage BERT-based [50] encoders; MolFormer [19], which adopts a transformer [33] architecture; and T5Chem [51], which builds on the T5 language model [52]. Graph-based methods encode molecular structures as graphs and extract meaningful representations using graph neural networks (GNNs). Rxn Hypergraph [17] employs hypergraph attention to capture molecular interactions. UA-GNN [45] sums reactant embeddings via GNNs and concatenates them with product representations. UAM [46] combines multi-view inputs, including graphs, SMILES, and molecular fingerprints, and aggregates their embeddings. LocalTransform [53] focuses on local structural changes by encoding atom-level differences between reactants and products. Conformer-based models incorporate 3D atomic coordinates to learn geometry-aware representations, which can better capture stereoelectronic factors influencing reactivity. ReaMVP [47] combines Bi-GRU networks [54] with multi-view pre-training on conformers and SMILES. YieldFCP [55] introduces a cross-modal projector to align conformer and SMILES representations for yield prediction. Despite their respective advantages, these methods often overlook the rich intra- and inter-molecular interactions that influence reaction outcomes. In this work, we address this gap by introducing a geometry- and structure-enhanced modeling approach that explicitly incorporates such interactions into the representation learning process.

Molecular substructure learning. Substructures (functional groups, motifs, and fragments) play a pivotal role in determining molecular properties and, by extension, reaction outcomes. Several fragment-based approaches have been proposed to decompose molecules into chemically meaningful parts. RECAP [56] introduces predefined cleavage rules to generate fragments at drug-like bond types, while BRICS [57] focuses on retrosynthetically relevant splits. ReLMole [58] further generalizes this process using graph-based heuristics to automatically extract relevant substructures. In parallel, fingerprinting methods such as ECFP [32] iteratively encode local atomic environments, capturing circular subgraphs of increasing radii. These subgraphs, referred to as molecular shingles [28-30], serve as a compact and effective representation of chemical structures. DRFP [31] extracts shingles and applies a symmetric difference operation on reactant and product shingle sets to obtain reaction fingerprints. Unlike DRFP, our method directly models the interaction-aware symmetric difference via a transformer, allowing it to capture fine-grained transformations at the shingle level.

B Datasets statistics

We use two datasets for pre-training and seven datasets for downstream evaluation, as summarized in Table 3. We remove duplicate records and invalid reactions for pre-training by RDKit [59]. To assess the generalizability of our approach, we consider both random and out-of-sample splits. In the out-of-sample split, the test set contains reactions involving molecules that do not appear in the training set. This setup mimics real-world scenarios, where unseen molecular structures are encountered during reaction property prediction. To strike a balance between accuracy in spatial coordinates and computational efficiency, we utilize the ETKDG algorithm [60] to generate up to 100 conformers and sample one for each molecule.

The United States Patent and Trademark Office (USPTO) dataset [38] was collected from 1976 to September 2016, containing over 1.8 million chemical reactions stored in the form of SMILES arbitrary target specification (SMARTS).

Appendix

The Chemical Journals with High Impact Factor (CJHIF) dataset [39] included over 3.2 million chemical reactions in the form of SMARTS extracted from chemistry journals by Chemical.AI.

The Buchwald-Hartwig (BH) dataset [13] contained 3,955 reactions from high-throughput experiments (HTEs) with 1,536-well plates on the class of Pd-catalyzed Buchwald-Hartwig C-N cross-coupling reactions. We choose the pyridyl reactants as the pivot to construct the out-of-sample split condition, where training includes nine pyridyl reactants and testing uses three non-pyridyl reactants, as shown in Figure 8(a).

The Suzuki-Miyaura (SM) dataset [14] was constructed from high-throughput experiments on the class of Suzuki-Miyaura cross-coupling reactions, resulting in measured yields for a total of 5,760 reactions. We choose a set of ligands as the pivot to construct the out-of-sample split condition, where nine ligands (including 'None') are used for training and three for testing, as shown in Figure 8(b).

The Ni-catalyzed C-O bond activation (NiCOlit) dataset [41] was extracted from organic reaction publications to form C-C and C-N bonds, containing 1,406 reactions. We choose the OPiv substrates as the pivot to construct the out-of-sample split condition, where 247 OPiv substrates are used for training and 42 non-OPiv substrates for testing, as shown in Figure 8(c).

The real-world electronic laboratory notebook (ELN) dataset [40] was created for Buchwald-Hartwig reactions from electronic laboratory notebooks, including 750 reactions. The structural diversity of the real-world ELN dataset is much higher than that of the HTE datasets. The random split also simulates an out-of-sample scenario.

The asymmetric N,S-acetal formation using CPA catalysts (N,S-acetal) dataset [42] included combinatorial variations of CPA catalysts, N-acyl imines, and thiols, resulting in a total of 1,075 reactions. We choose a set of catalysts (test-cat) as the pivot to construct the out-of-sample split condition, including 24 catalysts for training and 19 for testing, as shown in Figure 8(d).

The nanomole-scale reactivity evaluation of C-heteroatom-coupling reactions (Cheteroatom) dataset [43] was performed by an automated high-throughput screening on a nanomole scale, yielding 1,536 reactions. We choose a set of catalysts (test-cat) as the pivot to construct the out-of-sample split condition, where the split uses 12 catalysts for training and 4 for testing, as shown in Figure 8(e).

USPTO_TPL dataset [8] labels were generated by extracting the 1,000 most common templates from the USPTO dataset, containing 445,115 reactions.

Figure 7 presents distributions of the number of symmetric difference and union shingles per reaction for each dataset.

BH

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Number of Shingles

(a) Symmetric difference

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In practice, we also treat ring structures within molecules as shingles, as they capture rich and informative substructures. We set the max radius to 3. To balance expressiveness and computational efficiency, we consider up to 280 shingles per reaction and 100 shingles per molecule, allowing each unique shingle to appear up to 10 times. The per-reaction bound should be adapted to the downstream dataset at hand. The per-molecule bound prevents rare cases where one molecule generates an excessive number of shingles and dominates the representation. The generation of the symmetric difference shingle set is depicted in Algorithm 1.

Note that for the USPTO_TPL dataset, we generate shingles solely from reactants. This choice is motivated by the observation that the symmetric difference of substructures between reactants and

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products is often minimal. In practice, we can apply the symmetric difference, reactants-only, and union strategies, and select the one that yields the best performance.

Algorithm 1 Pipeline to extract symmetric difference shingles

Input: the reaction R = {M r ; M p } divided into reactants and products, and the maximum radius r max for shingles

D Pre-training settings

Weintroduce three pseudo-reaction-type classification tasks as pre-training objectives. The underlying intuition is that similar chemical structures tend to exhibit consistent semantic behavior across various reactions. Specifically, we employ the DRFP [31] with default parameters, which is a 1024-length one-hot descriptor as the reaction fingerprint. These reaction fingerprint sequences can be used for clustering, where shorter distances between fingerprints indicate a higher likelihood of belonging to the same cluster. We apply K means clustering by scikit-learn [61] with different values of K (100, 1,000, 4,000, see Figure 9 concerning selection of K ) to cluster reactions to obtain clusters with different granularities from coarse-grained to fine-grained. Subsequently, we use three classification heads with output sizes of 100, 1,000, and 4,000, respectively, to predict the pseudo labels associated with each cluster. This approach leverages the structural consistency of chemical reactions, enabling the model to learn more robust and transferable representations.

E Comparison with standard cross-attention architecture

We implement a cross-attention baseline that explicitly models interactions between reactants and products. In this architecture, product embeddings serve as queries, while reactant embeddings are used as keys and values within a standard multi-head cross-attention module [33]. The model consists of 4 cross-attention layers, 64 attention heads, and a hidden dimension of 512, and it shares the same molecular encoders as ReaDISH.

We evaluate this architecture for six datasets to compare it with ReaDISH. As shown in Table 4, the cross-attention baseline consistently underperforms our proposed model. This result suggests that simple cross-attention treats inter-molecular interactions uniformly, lacking chemical or geometric guidance. In contrast, ReaDISH explicitly incorporates both inter- and intra-molecular relationships through structural distances, edge types, and symmetric-difference shingles, resulting in more chemically grounded representations.

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F Implementation details

We extract the results of the baseline models from the existing literature as much as possible, and otherwise reproduce them based on publicly available code. Note that UAM applies contrastive pre-training on the whole dataset in downstream tasks, which may introduce data leakage. Hence, we randomly initialize the model weights to fine-tune the model during reproduction. All methods are tested on (1) the same ten random splits and (2) the same out-of-sample split across five random runs to ensure fair comparisons, with the average results reported.

Table 5 and 6 present hyper-parameters used in ReaDISH during pre-training and fine-tuning, respectively. We use Pytorch [62] with the Adam [63] optimizer and the cosine learning rate decay strategy for training. All experiments are executed on 4 NVIDIA RTX3090 GPUs. Pre-training the model takes about 8 hours, whereas fine-tuning on downstream datasets requires up to 1 hour. Generating shingles for the pre-training dataset takes around 7 hours using 50 CPU threads, while processing a downstream dataset completes in under 4 minutes. ReaDISH contains about 16.3M trainable parameters.

G Dependence of the prediction performance on the size of the training data

We scale training size on BH and SM benchmarks to measure performance changes, using subsets of 5%, 10%, 20%, 30%, 50%, and 70% while testing on the full 30% test split. Results in Table 7 and

Figure 10 show that predictive accuracy (R 2 ) steadily improves without saturation, suggesting further gains with larger datasets. But the rate of increase slows down.

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H Additional experimental results

The full experimental results are detailed in Table 8 and Table 9.

I Limitations

While ReaDISH effectively models chemical reactions through the symmetric difference of molecular shingles, one limitation is its computational overhead. In cases where all substructures are considered, the number of shingles can exceed the number of atoms, leading to increased memory consumption and longer training times. We mitigate this challenge by setting an upper bound on the number of shingles per reaction, balancing expressiveness with efficiency. Future work could explore adaptive pruning strategies to further reduce resource demands without compromising predictive performance.

J Impact statements

Advances in chemical reaction prediction hold the potential to accelerate scientific discovery in areas such as drug development, materials science, and sustainable chemistry. However, enhanced

predictive models could be misused to facilitate the design of harmful substances, including toxic chemicals or controlled compounds. This raises ethical and security concerns regarding dual-use applications. Additionally, widespread adoption of automated chemical design tools may shift traditional roles in chemical research, with potential implications for education and workforce development.

Peer reviews:
comments: SUMMARY:
The authors present ReaDISH, a reaction prediction model that addresses permutation sensitivity and substructure interaction modeling by introducing two key innovations: a symmetric difference of molecular shingles and an interaction-aware attention mechanism integrating geometric and chemical distances. The method achieves improved accuracy and generalization across multiple reaction prediction tasks, especially under permutation and out-of-sample settings.

STRENGTHS AND WEAKNESSES:
## Strengths

- Well-written and easy to follow.
- Novel chemical reaction modeling method.
- Extensive experiments are conducted to demonstrate the efficacy of ReaDISH.

## Weaknesses

- The paper lacks a detailed analysis of ReaDISH's computational complexity and resource requirements.
- As acknowledged, the model's scalability is a key limitation. Handling a large number of shingles requires higher computational costs for complex reactions.

QUESTIONS:
- Can the authors provide more details on the computational efficiency of the shingle generation process?
- Is the symmetric difference between reactant and product shingles always well-defined and non-empty? If not, how are such cases handled？
- How does ReaDISH perform with large molecules or complex reaction datasets, especially in terms of computation time and memory? How much additional GPU memory and runtime does the union-based variant consume compared to symmetric difference, and does it lead to better predictive performance?
- While several tasks are evaluated, could the authors comment on task-specific strengths or weaknesses? For example, is ReaDISH particularly strong in stereoselectivity due to interaction modeling?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-21

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe article \"Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets\" describes the development of an architecture for reaction outcome prediction. The authors show interesting performance on relevant benchmark datasets. However, several details regarding the model architecture and its performance characterization are unclear. Please find my detailed comments below that should be addressed in order for this submission to be accepted.\n\nSTRENGTHS AND WEAKNESSES:\nI think the quality is good as the datasets used for model evaluation are diverse and good statistical metrics were used.\nI think the clarity is fair but requires further improvement as there are several points that require further clarification.\nI think the significance is fair as the model architecture improves performance relative to existing baselines. However, some baseline models are missing which hampers significance.\nI think originality is good as the model architecture provides several innovative improvements compared to baseline models.\n\nQUESTIONS:\nMajor aspects:\nPage 14, Section B: The authors state that they sample 100 conformers for each molecule and sample one of them. It is not clear how this one conformer is chosen. How critical is the necessity to sample 100 conformers? Additionally, I think the authors should demonstrate the sensitivity of their model to the particular conformer selected. This prompts the question whether the case illustrated in Figure 6 with permutations and token orderings was done under the assumption of exactly identical Cartesian coordinates or whether this also included the re-generation of Cartesian coordinates, which can lead to different Cartesian coordinates and thus different embeddings and predictions.\n\nIt is not clear from the text whether the performance comparisons shown in Table 1 and Table 2 were done on the training set or the test set. In case they were done on the test set, identifying the best model corresponds to a hyperparameter optimization, and the accuracy of the model cannot be really estimated. This would require a separate split.\n\nPage 16, Section C: The authors state that the max radius of the shingles was set to 3. Why was this done? I think it is critical to show a dependency of the model performance on shingle size. Additionally, the authors use an upper bound on the number of shingles per reaction. How critical is this parameter? Are there any strategies for when there are more than 100 potentially relevant shingles to select shingles that are meaningful to the reaction?\n\nThe ablation study by the authors summarized in Table 2 appears to show that, apart from the pairwise interaction treatment, the other components of the model only seem to marginally improve prediction performance. In addition, pre-training seems to have the lowest impact of all. I would like to see additional ablation studies, especially one that entirely removes the use of shingles from the model.\n\nIt is not clear whether the authors performed any hyperparameter optimization. I think this needs to be mentioned explicitly. If hyperparameter optimization was performed, it should be mentioned how it was performed.\n\nI think it is important to also show a learning curve for the new architecture. With that, I mean a dependence of the prediction performance on the size of the training data. This illustrates in what dataset size regime the model learns and it also indicates whether the model might continue learning if provided with more data.\n\nI think that the prediction performance should be compared to more alternative approaches. In particular, at least for some of the subsets used for training (e.g., BH, SM, etc.), other models have been developed. The corresponding performance should provided and compared to the current model. This is important even when data splits are not the exact same between different approaches.\n\nSome details regarding shingle generation are not clear based on the provided text. This is an issue as no code has been provided for the submission. For instance, it is stated that ring structures within molecules are shingles but how exactly they are extracted and what the additional environment apart from the ring structures is not clear. I think more details need to be provided for that.\n\nThe authors apply the model to various datasets that constitute, among others, yield prediction and enantioselectivity prediction. Does this mean that the model architecture is trained on each dataset separately to reach the described performance or is there one model trained on all the datasets?\n\nI think Tables 1 and 2 should not only provide results for BH and SM but also for the other datasets. This allows for a more balanced evaluation of model performance. Some of these results are provided in the Appendix but I think they should be provided in the main text.\n\nMinor aspects:\nTable 1: The best value is not always bolded.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a method named ReaDISH to predict the chemical reaction properties like yield, selectivity, and classification. Previous method may have two primary drawbacks: 1 . They are sensitive to the order of input molecules and atoms 2. Fail to adequately model the substructural interactions that drive chemical reactivity. Instead of using all shingles from both reactants and products, this paper introduces a novel idea called symmetric difference of molecular shingle sets. This operation isolates only the shingles that are unique to either the reactants or the products, effectively focusing the model's attention on the structural parts of the molecules that are consumed or formed during the reaction. The permutation invariance is automatically satisfied in this operation. Then the interaction-aware attention mechanism models geometric and structural relationships between these shingles.  In this attention meachanisum, the authors compute three types of pairwise relations, namely, Geometric Distance , Chemical connectivity and structural distance. Empirically ReaDISH achieves state-of-the-art or highly competitive performance across all tasks, especially on the more challenging out-of-sample splits where the test set contains molecules not seen during training. They also do some ablation study on pairwise interaction bias, geometric distance, structural distance and others components.\n\nSTRENGTHS AND WEAKNESSES:\nPros: \n\n\n1. The paper clearly identifies two critical weaknesses in existing reaction prediction models: permutation sensitivity and inadequate modeling of substructural interactions.\n2. The authors evaluate ReaDISH on seven benchmark datasets covering four distinct chemical tasks: yield prediction, enantioselectivity prediction, conversion rate estimation, and reaction type classification. This demonstrates the broad applicability and versatility of the proposed method.\n3.  Through detailed ablation studies, the authors systematically demonstrate  the contribution of each key component of the model.\n\nCons：\n\nThe authors propose a novel interaction-aware attention mechanism based on symmetric difference shingle sets. However, there is a natural baseline that uses a standard cross-attention Transformer architecture (and maybe its equivariant counterpart like equiformer [1] ). Such a model, where product embeddings attend to reactant embeddings, would also be permutation-invariant and is a standard approach for modeling interactions between two sets. \nIt is unclear whether the performance gains come from the specific and complex design of symmetric difference and shingle-level interactions, or if a more standard and conceptually simpler cross-attention model could achieve similar or even better results. I suggest the authors compare such baseline in the paper.\n\nThe selection of baselines reported in the paper could be strengthened. For instance, YieldBERT, as a sequential model operating on SMILES strings, inherently lacks the expressiveness to capture the complex, non-linear graph structure of molecules. Furthermore, while UA-GNN is included as a graph-based baseline, its backbone architecture may not represent the current state-of-the-art, potentially leading to an unfair comparison that favors the proposed model.\n\n\n[1] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs\n\nQUESTIONS:\nThe manuscript's evaluation would benefit from a more rigorous selection of baseline models to better contextualize the performance of ReaDISH. See my comments on Pros and Cons.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe study proposes ReaDISH, a reaction-property predictor built around two ideas. First, each molecule is decomposed into local “shingles”, and a reaction is represented by the symmetric difference between the shingle sets of reactants and products. This yields a representation that is both permutation-invariant and explicitly focused on the structural changes that define the reaction. Second, an interaction-aware attention module augments transformer layers with pairwise features that encode geometric distance, graph distance, and chemical connectivity between shingles, capturing intra- and inter-molecular interactions. Across multiple tasks ReaDISH achieves superior accuracy and more reliable uncertainty estimates, especially under out-of-sample and reactant-order perturbations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: 1. ReaDISH introduces an entirely new reaction representation based on the symmetric difference of reactant and product substructure sets. This provides a clever way to highlight reaction centers and changed motifs while being invariant to the ordering of inputs.\n\n2. ReaDISH is evaluated on a wide range of reaction prediction tasks. It outperforms baseline models across most of these tasks, achieving higher accuracy and better uncertainty estimates. Particularly noteworthy is its performance under out-of-sample conditions.\n\nWeaknesses:   1. ReaDISH’s pipeline is quite involved. This could impact reproducibility and runtime. The paper doesn’t explicitly mention the computational cost; if the number of shingles per molecule is large, computing pairwise interactions for all shingle pairs might be expensive. \n\n2. The method relies on the definition of “chemically meaningful substructures” via a circular topology. If this fragmentation misses relevant chemistry (e.g., a reaction mechanism including global rearrangement), the representation might not capture it. \n\n3.  The method currently appears tuned for reactions where structures are known and changes can be enumerated; it might not directly handle cases with unseen functional groups or need for reasoning beyond substructures.\n\nQUESTIONS:\n1 The model uses geometric distance between shingles. How is this obtained in practice? Do the authors generate 3D conformations for reactants (and possibly products) and then measure distances between fragment centroids or key atoms?\n\n2. What method is used to define “molecular shingles”? Is it based on a certain radius (like Morgan fingerprint fragments) or predefined functional groups? And does the model consider shingles of reactants and products separately before taking the symmetric\n difference? An\n\n3 Many reaction outcomes depend on conditions. The formulation treats anything not a product as “reactant” , and the permutation invariance addresses their presence. However, does the model explicitly incorporate condition variables if provided?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}