Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
Authors: Runhan Shi
Letian Chen
Gufeng Yu
Yang Yang
Venue: NeurIPS 2025 poster ()
Keywords: yield prediction
molecular representation learning
chemical reaction prediction
Primary area: machine_learning_for_sciences

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
Runhan Shi', Letian Chen 1.2, Gufeng Yu', Yang Yang
AGI Institute, School of Computer Science, Shanghai Jiao Tong University
SShangaii Innovation Institute
{han. run. comn.run.jiangaeeen jiangming, clt2001, jm5820zz)ss edu. yangyang9cs. sjtu. edu. edtu.edu.co cn
Abstract
Chemical reaction prediction remains fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity input permutations (molecule/atom orderings) and inadequate modeling of substruc- tural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, propose ReaDISH, novel reaction prediction model that learns permutation invariant representations while incorporating interaction -warr fea- tures. It introduces two innovations: (I) symmetric difference shingle encoding, which extends the differential reaction fingerprint (DRFP) by representing shingles continuous nigh-dimensional embeddings, capturing structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with average improvement of 8.76% on R² under permutation perturbations
Introduction
Accurate modeling of chemical reactions a fundamental problem in organic chemistry, as it provides
critical insights into reaction mechanisms, predicts reaction outcomes, and guides experimental
design [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 g
These tasks have gained considerable attention with the rise of machine learning (MI.). Nevertheless,
representing reactions for MI. is challenging due the complexity of reaction spaces and the multitude of factors influencing chemical experiments like substrates, catalysts, and reaction conditions 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 making the reaction prediction problem complex and nuanced challenge.
Despite advances in ML models, they still face two major limitations that hinder their broader applicability, as shown in Figure [ 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) the ordering of atoms (e.g., alternative
*Corresponding author. This work was supported by the National Natural Science Foundation of China (No. 62272300).
'The code is available at at/https://gitthu http:///tthubbccmmm/teeeree www.:/teoooooeeoooo//ooeo R
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Input of molecules
Substructure & Interaction
Alternatvve SMILES for the same molecule
Cel Celee((N22ceee2))eell el Clcno(Nc2ccc())cc2ee (NNe2cccen2)ecccCeee
(a) Permutation
(b) Interaction
Figure Challenges for reaction representation learning. (a) Permutation perturbations by inter-molecular order variation (top) and intra- molecular SMILES token randomization (bottom). (b)
Key substructures that determine the outcomes of reactions and their inherent interactions.
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 [T8 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 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 23), capturing higher-order interactions among chemically meaningful substructures across molecules remains limited the reaction prediction problem. This lack of interaction awareness leads to suboptimal predictions and diminishes the model's 's ability to capture underlying reaction mechanisms [24]. As a result of these two limitations, many models perform poorly out-of-sample reactions that better reflect real-world chemical diversity 25 27
To address these challenges, we propose the ReaDISH model for Reaction prediction via interac- tion modeling of symmetric DIfference SHingle sets. First, we construct chemically meaningful substructure sets named molecular shingle [28 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 cormutatiioninvariann Second, add intra- and inter-moleculat interaction pair representation shingle-level attention based on geometric distance, structural distance, and chemical connectivity of shingles to improve interaction awareness. We conduct numerical experiments various reaction representation learning tasks and benchmarks, including reaction yield prediction, enantioselectiv- ity 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 by an average of 8.76% under out-of-sample splits when performing permutation perturbations, presenting better generalization capability. We present related work in Appendix A Our key contributions summarized follows:
We introduce a new way to present reaction structures based on the symmetric difference of molecular shingles between reactants and products, which inherently permutation- invariant and highlights critical substructures that drive reaction outcomes.
We design 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-moleculat interactions.
Extensive results across wide range of tasks demonstrate that our proposed ReaDISH outperforms baseline models, achieving higher prediction accuracy and lower prediction uncertainty, especially under out-of sampla conditions.

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2 Background
2.1 Problem definition
We denote the dataset of chemical reactions and their associated labels as 'D = ((R,, 9: ] where each reaction R, consists of of participating molecules and y, ) is the corresponding property label. Depending on the task, may represent (1) a real-valued scalar for regression tasks, such as reaction yield or energy barrier, i.e., yt IR, or (2) a categorical label for classification tasks, such as reaction type or success/failure, i.c., E N.
Formally, each reaction is denoted by R. {MM = {M M,",; M. Mf where M; (Mf)) denotes the set of reactant (product) molecules and (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,' is represented as the
3D conformer C {(ag, xx)) k==- where aş is the atomic type, X E IR3 is the spatial coordinate, and N₂ the number of atoms. The goal of reaction representation learning is to design mapping function fe R - V/ parameterized by Ø, such that the predicted label j fo (R,) approximates the true label 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 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 molecule M be represented by conformer and coordinates X. We define an r-sized shingle of an atom E 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 (v) denote the set of neighboring atoms to atom v with radius The corresponding shingle is then given by
E, X) with atoms bonds E, (V,E,
s [()))C((
where C denotes the sub-conformer of C' restricted to the specified subset of atoms {u} (.).
The set of all r-sized shingles in M is denoted ((r) and its representation is invariant to the arrangement of atoms.
Method
3.1 Model architecture
We propose ReaDISH, novel framework for reaction property prediction that learns permutation invariant 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, encoder that captures geometric and structural features, and lightweight predictor that outputs reaction properties.
Given reaction R {C ...., C!T; .f....., C. consisting of reactant and product molecules in 3D conformer format, ReaDISH first encodes each molecule C/ using a 3D molecular encoder fnot to generate atom-level representations X2 R.N.F. where the embedding dimension. shingle- generation algorithm then extracts molecular shingles and computes the symmetric difference between reactant and product shingle sets to capture praction-speciff aggregates the atom-level representations within each shingle through a pooling operation, producing initial shingle-level embeddings X R.N.F., where N, 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 = =Ppg P., P.) N. To incorporate these relationships into the attention mechanism, we apply K Gaussian kernels to each pairwise matrix produce the initial pair representation P The ReaDISH encoder
transformations. Next, ReaDISH
3

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Predictor
LayezNorm
Attention block
Molecular encoder
Shingle generaatin
FFN
Attention block
pft
Pair-wisc analysis
Pooling
Encoder
X
LayerNorm
k
Gaussian
Attention block
Multi-beud atlention
X
x
po
(c)
Embedding layer
Embeddling
Linear
GELU
p
SSUM
property
ReaDISH
Attention block
(d) Predictor
Figure 2: Overall architecture ReaDISH (a). consists an embedding layer (c), an encoder incorporating L attention blocks (b), where the extended self-attention module with the Gaussian kernel depicted in Figure aa), a), and lightweight predictor (d) for predicting reaction properties.
then processes the shingle-level representations using L transformer-style S attention blocks. Each block contains multi-head attention (MHA) layer that integrates the pairwise attention biases P at each layer f. Finally, ReaDISH uses special [SSUM] token to summarize the reaction representation and passes it multilayer perceptron (MLP) head predict reaction property ÿ.
3.2 Embedding layer for shingles
Molecular encoder. The 3D molecular encoder fioi transforms each molecule C = {(ax, xx) } comprising atom types dg and 3D coordinates Xs. into atom-level feature representations X R.NxF followed by layer normalization:
k=11
X LayerNorm (Cm)
Symmetric difference shingle set generation. 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 reactants. Shingles within given ra- dius are generated for reactants and products, re- spectively. The generated shingles are expressed in SMILES strings to perform the symmetric dif- ference operation, as shown Figure 3 This operation yields the set of shingles that are exclu- sive either reactants products, thereby iden- tifying structural changes associated with the reaction mechanism. The complete algorithm and illustration for computing the symmetric dif- ference shingle set are provided in Appendix C Formally, for a reaction R (MM",PP) and radius Tmax. we define the symmetric difference shingle set SR
Okienend Coloah sut GyO weweeaddddddddddddde KKIPie wttp../....dddaeciiii
C S D..0..9.. 080
10.70
ot YY
Figure Shingles generation. remove intersection part (in gray), and keep the remaining shingles for reactants (in green) and products (in yellow).
SR = Specc A Sprid
UUS i=1==
( Mr
U s aPpk
(3)
i=
A=1

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where Speaet (Sprod) is the shingle set for reactants (products), and A denotes the symmetric difference operator AAB (A ( A). This set- sat-based strategy is invariant to the ordering of molecules.
Shingle pooling. To construct shingle-level representations from atom-level embeddings, we apply shingle pooling operation over the set of symmetric difference shingles. Each shingle E SR consists of a subset of atoms, and its representation is computed by averaging the embeddings:
hg = Pooling (5, = [A(S)
Σ x
(EA((9)
where A(S) S {I...., Na denotes the indices of atoms in shingle S and x² 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
N X hs, 1=1
R.N.P.
(5)
These pooled embeddings between shingles to capture the overall reaction context.
the input to the subsequent encoder, which models interactions
Pairwise interaction. We introduce an interaction framework that explicitly captures pairwise dependencies between molecular shingles. This formulation allows ReaDISH to model both intra- and inter-moleculat interactions, leveraging geometric and structural features to represent chemical transformations. As illustrated in the right panel of Figure b), we define three pairwise metrics between shingles S, and Sj as
Icc
Cg l2,
if St, belong to the same molecule, otherwise;
d
0,
1, S; Sj belong to the same molecule,
d
0, otherwise;
d sim(S;, Sy)).
where c₁ and e, denote the geometric centers of shingles S and Sj. respectively, and sim(..-) the Tanimoto similarity [34] of their Morgan fingerprints [35]. These metrics yield structured pairwise representation
Pa. = dry di di ij=1
N. E R3..... where each component
captures a specific interaction modality across all N shingles. The geometric distance di encodes spatial relationships within the same molecule, analogous atom-level distance modeling. The binary chemical connectivity di indicates whether two shingles are part the same molecule. The structural distance di emphasizes functional dissimilarity, which is essential for modeling reactive interactions across different molecules. These pair interactions enable 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 (2I). GKPT applies an affine transformation pairwise distances based on the interaction type, followed by a classical Gaussian kernel, as illustrated in Figure a). Formally, the GKPT is defined
GKPT ((x, c), u1 σ) 9 (c) x (e(c)...o))
o ) is the standard Gaussian kernel. Here, is the
where G (1', ()) V2 exp
input distance, is the pair type index, E2 N,* are learnable embedding layers, No is the number of pair types, and μ. a E RK are learnable parameters for K Gaussian kernels. We apply separate GKPT modules to process geometric distance (dK)) and structural distance (diy) along with chemical connectivity (di,) shown in Figure
4 b). Each output is projected into the attention space and combined to form the pairwise bias term as
Pij = GKPT, (dde d: ,1 σr) We GKPT, ((dr,) diz), pc, a) w..
(8)
5

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K
Linear
Linear
Linear
Gaussian kome!
MatMul
Mask (opt.)
Gaussian
Scale
MatMul
(a) Gaussian kernel
(b) Geometry- and structure-enhanced attention
Figure 4: Interaction-aware attention. (a) Gaussian kernel with learned pair type transformations. (b) Self-attention enhanced by geometric and structural interactions. Each pairwise representation incorporates one intra-molecular relationship (geometric distance) and two inter-molecular relation- ships (structural distance and chemical connectivity).
where Wg, W. E R are learnable projection vectors. The full interaction-awart pairwise bias matrix used in attention denoted as
p Pis N
IR NxM.
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 learnable pairwise bias derived from previously defined pairwise representations. At each layer e, we update pairwise bias ply between shingle S; and Sj using the query-key interaction:
Q (K)) Vd
T
++1 P.j
+ P4j
(10)
where Q and K are the query and key vectors of shingles i and j at layer É, respectively, and is the hidden dimension. The attention score between shingles and j is then computed by incorporating this updated bias into the scaled dot-produs attention:
(Q Vd
Attention (Q K.S Vt) softmax
+
Pij
V
(11)
where V 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 learnable [SSUM] token to the shingle sequence X at each layer, which serves global summary token for downstream reaction property prediction.
3.4 Predictor
ReaDISH utilizes a lightweight predictor, as depicted in Figure (d), to predict reaction properties. 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 pseuuo-reaction-tyye classifica- tion task as the pre-training objective a large-scale dataset. Specifically, assign each reaction

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aseudo-labed by clustering its structural representation at different granularities. This multi- classification setting encourages the encoder to capture both coarse-graing grassnd and fine-grained structural semantics. We generate K, pseudo-labels for each reaction. Formally, the pre-training loss is defined as
N K, AE FL
Lpseedoo
(WalsooX, PR)) wi!
(12)
N
where fe denotes the ReaDISH encoder, L is the cross-entropy loss, WA E REXN with target dimension N₂ for E {I,..., Ki) are parameters of fully connected layers, respectively, and (1) ya E N are pseudo-labels for the n-th sample. More information can be found in Appendix D
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. comprehensively evaluate the effectiveness ReaDISH, we use seven datasets across wide range of chemical tasks, including: (1) yield prediction, the Buchwald-Hartwig
(BH) dataset [[3], 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) enantiosclcctivitiy prediction, the asymmetric N,S-acetal formation (N.S-acetal) dataset [42]
(3) conversion rate estimation, the C-heteroatom-coupphh
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: Yield- BERT (5), YieldBERT-DA 44 UA-GNN (45), UAM [46], and ReaMVP [47], which we introduce
in related work in Appendix A We additionally compare method with cross-attention bascline
that directly models reactant-produce interactions as provided Appendix E Given the importance
of spatial structural patterns in molecules, we adopt the SE(3)-invariant [48 Uni-Mol 21 our 3D molecular encoder fmel for its excellent performance. For regression tasks, we report mean absolute error (MAE 4), root mean squared error (RMSE 4), and coefficient of determination (R² 1). For classification tasks, we measure accuracy (ACC ?), Matthews correlation coefficient (MCC f), and confusion entropy (CEN 4). 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 aj) 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 and the USPTO TPL dataset for accuracy. On the SM and NiCOlit datasets, ReaDISH consistently matches or outperforms existing models. Figure b)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. shows a greater advantage under out-of -smplle 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 R² below 0.3. This underscores the difficulty 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
7

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47.9
48.6
BH USPTO_ TPL
16.
Icatelyst based)
BH
783
7LA
46.7
0.8
75.
42.0
SM
R-Sooaatil Caatalys--Based
78.0
70.6
66-4
NICOlit
N,S-acetal
54.1
(substrate arree--hhasel hased)
BLM
44.3
835
67.
WeidBERT
YeldBERT-DA
UN-GNN
UAM
ReaMVP
ReaDISH
Random splits
(b) Out-of-sample splits
Figure 5: Performance comparison. (a) Results under random splits for six datasets and (b) results under out-of-sample splits for six datasets, where we report accuracy (%) for the USPTO_ TPL. dataset and R (%) for other datasets.
Table 1: Impact of permutation -nntutatttoonnnaiiaat modeling prediction uncertainty scores in out-of-sample splits. denotes methods that perform permutation data augmentation, and denotes permutation- invariant methods. Bold entries highlight the best performance.
freactant-basee
(liand-based)
Method
MAE
RMSE
R²
MAE
RMSE
R²
YickdBERT
17.65 + 1.13
23.96
1.17
0.342 = 0.082
14.98 + 0.38
20.05 + 0.39
0.117 = 0.029
YiekBBRR-DA"
18.41 + 0.48
26.30
0.33
0.215 = 0.020
15.78
1: 0.24
19.64 4: 0.26
0.467 : 0.014
GNN
0.339
LAM
17.31 +: 0.63
22.61
1:24
0.421 = 0.072
15.81
+ 0.53
19.61
0.53
0.503
0.042
ReaMVP
17.62 1 1.04
22.52
1.47
0.432 0.068
13.91
1: 0.29
18.64 1: 0.40
0.516
0.026
ReaDISH
16.29 -: 0.30
21.76
0.80
0.480
0.032
14.22 1 0.33
18.05
0.37
0.549 0.019
Permutation-invarti modeling enhances prediction robustness. A key strength of ReaDISH its cormutationninvariin design, which ensures consistent predictions regardless the ordering of input molecules or SMILES tokens. To test whether this property enhances robustness in out-of- sample 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 ReaDISH consistently shows the lowest prediction variance. increases R 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 example in Figure
These findings highlight the practical benefits of permutation invariance in enhancing model reliability. By modeling interactions between symmetric difference shingles in an order- order-indepen 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, conduct series of ablation studies concerning pair representation, symmetric difference, pre-training strategies, and radius of shingles. Table pressens the results for the BH and SM datasets under out-of-sample splits.

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YieldBERT
UAM
ReaDISH
41.5
44.3
35.2
42.3
41.0
35.2
Prediction (%)):
41.8
36.2
35.2
40.9
39.7
42.1
37.2
35.2
True yield (%)): 30.3
Figure 6: Permutation influence on predictions. We sample one reaction from the SM dataset under the out-of-sample split and perform five permutations on the molecule and SMILES token orderings with consistent conformers. While YieldBERT and UAM produce varying predictions with standard deviations of 0.49 and 2.88, respectively, ReaDISH consistently yields an invariant result with the least error.
Table 2: Ablation study results key components of ReaDISH, evaluated under out-of-sample splits for the BH and SM datasets. Bold entries highlight the best performance.
based split)
SM (ligand based split)
Method
MAE
RMSE
MAE
RMSE
R²
w/o pd
16.82 0.34
22.24 + 0.87
0.430
0.038
14.81
0.29
19.24 + 0.40
0.506 0.026
w/o P
16.55 + 0.44
22.10
0.91
0.457
0.040
14.47
0.44
18.36 : 0.42
0.535 + 0.028
w/o
16.41 0.36
0.464
14.30
0.530
w/o A
16.52 1: 0.45
21.95 1: 0.98
0.465
0.039
0.37 14.35
18.21 1: 0.41
0.538 1: 0.017
w/o pre tre-training
16.40 0.35
1:
0.472
0.035
0.540 0.020
ReaDISH (r=2)
16.50 0.15
22.11 0.25
0.465
0.014
14.41 0.33
18.57 + 0.44
0.523 0. 228
ReaDISH (r=4)
17.91
0,421 0.006
0.55
0.82
0.505 0.043
ReaDISH (r=3)
16.29
0.30
21.76 = 0.80
0.480 0.032
14.22
0.33
18.05 = 0.37
0.549 0.019
(i) Impact of pair representation. A central feature of ReaDISH is its use of pair representation capture interactions between molecular shingles. We evaluate three ablated variants: (1) w/o po. which entirely removes pairwise modeling from self-attention, (2) w/o P which excludes geometric distance information, and (3) w/o P,, which omits structural distance features. The results show that removing the full pair representation causes 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 A) 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 exposed to noise from irrelevant redundant molecular features, which hampers learning.
(iii) Contribution of pre-training. We evaluate a version trained from scratch (wlo 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 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 yields the poorest performance, likely because produces excessive shingles, introducing redundancy and higher complexity. A radius of 2 performs slightly worse than radius indicating that radius offers a good balance between coverage and efficiency.

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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-eevel 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 imput permutations, particularly under out-of-sample scenarios. ReaDISH offers 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 [ respectively. Future directions include extending ReaDISH to complex tasks, such as retrosynthesis planning and the incorporation of reaction conditions to enable more accurate predictions.
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Appendix
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] [4] [9] 40 41]. Recent advances in deep learning for 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 S and YieldBERT-DA
reaction prediction can be grouped into three main categories: siquence-baseed graph-
44 which leverage BERT-bbased
50 encoders; MolFormer [9]. 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 Hy- pergraph [17] employs hypergraph attention 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. Local Transforr [53] focuses on local structural changes by encoding atom-level differences between reactants and products. Conformer-basse based models incorporate 3D atomic coor- dinates to learn geometry-awan 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 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-enhancee 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-basee based approaches have been proposed to decompose molecules into chemically meaningful parts. RECAP [6 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 [1] extracts shingles and applies symmetric difference operation on reactant and product shingle sets to obtain reaction fingerprints. Unlike DRFP, our method directly models the interaction- symmetric difference via a transformer, allowing it to capture fine-gred fine-grained transformations at the shingle level.
B Datasets statisties
We use two datasets for pre-training and seven datasets for downstream evaluation, summarized in T We remove duplicate records and invalid reactions for pre-training by RDKit [59]. To assess the generalizability of our approach, 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 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 the form of SMILES arbitrary target specification (SMARTS).
21

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Table 3: The statistics of pre-training datasets (first row) and evaluation datasets (remaining rows).
Dataset
No. reactions
Split type
-ut-ol-sample
No. training
No. test
USPTO CJHIF [39
3,728,503
stratified
x
3,542,077
186,426
BH 13
3,955
random
X
2,768
1,187
3,955
reactant-based
/
2,372
1,583
SM
5,760
random
X
4,032
1,728
5,760
ligand-based
/
4,320
1,440
NiCOlit
1,406
random
x
1,124
282
1,406
substrate-basee
/
1,012
394
ELN 40
750
random
/
525
225
N,S-acetal
1,075
random
X
688
catalyst- based
/
384
304
C-heteroatom +5
1,536
random catalyst-based
X
1,075
461
1,536
1,152
384
USPTO_TPP TPI.
445,1 115
random
x
400,604
44,511
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-Hartwine (BH) dataset 13 contained 3,955 reactions from high-throughput experiments (HTEs) with 1,536-well plates
plates on the class of Pd-catalyzed Buchwald- Harttwig 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, shown in Figure (a).
The Suzuki-Miyaure (SM) dataset [14] was constructed from high- thro-ghhruuuutttt experiments on the class of Suzuki-Ma Miyuiaa cross- cross-coupling reactions, resulting in measured yields for a total of 5,760 reactions. We choose of ligands the pivot to construct the out-of-sample split condition, where nine ligands (including "None") are used for training and three for testing, shown in Figure(8b)
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 the pivot to construct the out-of split condition, where 247 OPiv substrates are used for training and 42 non-OPiv substrates for testing, as shown in Figure 8 c).
was created for Buchwald- Harttwg 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 real-world electronic laboratory notebook (ELN) dataset 40
The asymmetric N,S-acetal formation using CPA catalysts (N,S-acetal) dataset [22 included combinatorial variations of CPA catalysts, N-acyl imines, and thiols, resulting in total of 1,075 reactions. We choose set of catalysts (rest-cat) as the pivot to construct the out-of-sample split condition, including 24 catalysts for training and 19 for testing, as shown in d).
The nanomole-scale reactivity evaluation of C-heteroatom-coopline reactions (C- heteroatom) dataset (43) was performed by an automated high-throughn 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 155,1 reactions.
Figure 7 presents distributions of the number of symmetric difference and union shingles per reaction for each dataset.
22

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BH
ELN
NiColit
N,S-acetal
C-heteroatom
USPTO_TPP TPL
50
100
150
200
250
300
350
400
Number of Shingles
(a) Symmetric difference
SM
ELN
NiColit
N,S-acetal
C-hetercatom
USPTO_TRP TPL-
100
200
300
400
Number Shingles
(b) Union
Figure 7: Distributions of the number of shingles per reaction. (a) The symmetric difference of shingles removes many duplicate shingles in the intersection. (b) The union of shingles from reactants and products.
C Shingle generation
In practice, also treat ring structures within molecules shingles, they capture rich and informative substructures. We set the max radius to 3. To balance expressiveness and computational efficiency, we consider up 280 shingles per reaction and 100 shingles per molecule, allowing each unique shingle to appear up to 10 times. The per-reaction bound should adapted to the downstream dataset at hand. The per-molecule bound prevents rare cases where molecule generates excessive number of shingles and dominates the representation. The generation the symmetric difference shingle set is depicted in Algorithm [
Note that for the USPTO_TPP dataset, we generate shingles solely from reactants. This choice motivated by the observation that the symmetric difference of substructures between reactants and
23

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Train
Test
(8)
(b)
(c)
(d)
(c)
Figure 8: Example molecules used in out-of-sample splits. (a) Reactant- for the BH dataset. (b) Ligand-based for the SM dataset. (c) Substrate-based for the NiCOlit dataset. (d) Catalyst-based for the N,S-acetal dataset. (c) Catalyst-based for the C-heteroatom dataset.
products 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 Pipeline to extract symmetric difference shingles
Input: the reaction R MPP divided into reactants and products, and the maximum radius
m'ax for shingles
Output: symmetric difference shingle set SR between reactants and products
1: Sreaet f- !
Initialize reactants shingle set
2: Spodd f- 0
b Initialize products shingle set
3: for m in M do
D Enumerate molecules and atoms
4: for in m do
5: for r to maax do
6:
calculate and add shingles (v) to Sreaet
b Generate shingles for reactants
7: end for
8: end for
9: end for
10: for m in MP do
b Enumerate molecules and atoms
11: for in do
12:
for r f 1 to Tmax do
13:
calculate and add shingles (tr) (v) to Sprdl
Generate shingles for products
end for 15: end for
14:
16:
end for
SR (Sreact \ Sprod) U (Sprod Sreact)
Compute symmetric difference
18:
return SR.
D Pre-training settings
We introduce three pseudo-reaction-yy 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 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 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 (igureyyconcernigg selection of K)) to cluster reactions to obtain clusters with different granularities from coarse-gran -rarined 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 queries, while reactant embeddings used as keys and values within standard multi-head cross-attention module [33]. The model consists of 4 cross-attention layers, 64 attention heads, and hidden dimension of 512, and it shares the same molecular encoders as ReaDISH.
We evaluate this architecture for six datasets 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 geometric guidance. In contrast, ReaDISH explicitly incorporates both inter- and intra-molecular relationships through structural distances, edge types, and -iffttrreddifference shingles, resulting in more chemically grounded representations.
Table 4: Comparison between ReaDISH and the cross-attention model for six datasets.
Model
BH
SM
NiCOlit
N,S-acetal
C-heteroatom
ELN (OOS)
Cross-attention 0.946 0.005
0.858 + 0.009
0.431 + 0.007
0.844 0.005
0.705
0.076
0.156 + 0.006
ReaDISH
0.976 0.001
0.886 + 0.008
0.539 + 0.024
0.916 0.007
0.763 0.026
0.230 + 0.047
25

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168
Distortion Score Elbow for KMeans Clustering
1.5
elbow 1000, score 112506385,594
8000
1.4
7000
Distortion score
5000
fit time (seconds)
4000
12
3000
1.1
2000
2000
4000
6000
8000
10000
k
Figure 9: The elbow for the K means algorithm. We use the distortion score to find the elbow and choose K 100, 1,000, 4,000 for reaction clustering.
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 fine-tune the model during reproduction. All methods tested on (I) the same ten random splits and (2) the same out-of-sample split across five random runs ensure fair comparisons, with the average results reported.
Table and present hyper-parameters used in ReaDISH during pre- 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 downstream datasets requires up hour. Generating shingles for the pre-training dataset takes around 7 hours using 50 CPU threads, while processing downstream dataset completes in under minutes. ReaDISH contains about 16.3M trainable parameters.
Table 5: Parameters during pre-training.
Parameters
Value Parameters
Value
Number of encoder layers
4
Number of encoder attention heads
64
Encoder FFN embedding dimension
2048
Encoder embedding dimension
512
Batch size
64
Initial learning rate
5e-5
Max epochs
3
Minimum learning rate
5c-6
Warmup steps
2000
Warmup learning rate
le-6
Dropout rate
0.1 Number of Gaussian kernels
128
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 the full 30% test split. Results in Table 7 and
26

---

Table 6: Search space of parameters during fine-tuning.
Parameters
Search space
Max epochs
150, 200
Batch size
64, 128
Initial learning rate
5e-3, le-3, 5e-4
Minimum learning rate
5e-4, le-4, 5e-5
Figure show that predictive accuracy (R²) steadily improves without saturation, suggesting further gains with larger datasets. But the rate of increase slows down.
Table Performance regarding training size on the BH and SM datasets.
Dataset
5%
10%
20%
30%
50%
70%
BH
0.713 + 0.017
0.808 + 0.017
0.874 + 0.016
0.919 + 0.006
0.955 + 0.002
0.976 0.001
SM
0.508 + 0.018
0.649 + 0.008 0.751 + 0.010 0.810 + 0.001 0.851 + 0.010 0.886 0.008
BH Dataset
SM Dataset
1.0
0.9
10
20
30
40
50
60
70
10
20
30
40
50
60
70
Training Data Percentage (%)
Figure 10: The learning curve regarding training size for the BH (left) and SM (right) datasets.
H Additional experimental results
The full experimental results are detailed in Table X and Table
I
Limitations
While ReaDISH effectively models chemical reactions through the symmetric difference of molecular shingles, one limitation is its computational overhead. cases where all substructures are considered, the number of shingles can exceed the number of atoms, leading 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 chemical reaction prediction hold the potential to accelerate scientific discovery in areas such as drug development, materials science, and sustainable chemistry. However, enhanced
27

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0.353 1: 0.070
0.373 0.060
0.017
0.515 + 0.053
0.541 + 0.053
0.539 +: 0.024
CEN
0.006
0.006
0.005
0.250
0.006
0.005
R²
0.550
1:111.10 1.38
26 53 + 1.28
0:3
1.12
1.12
+ 0.57
USPTO TPL
NiCOlit
RMSE
MCC
0.989
0.990
0.991
0.954
0.990
0.992
27.23
22.23
22. 22.91
22. 01
21 18.89
19.96 1 1.95
.882
0.26
16.51 # 0.87
16.21 0.87
15.36 + 0.28
Table 8: Results under random splits. Bold entries highlight the best performance.
MAE
ACC
0.989
0. 0.990
0.991
0.954
0.990
0.992
19.53
16.19
0.815 1 0.013
0.012
0.010
0.888 + 0.009
0.864 + 0.010
0 0.886 + 0.008
0.664 + 0.057
0.709 + 0.043
0.751 + 0.038
0.718 0.047
0.726 0.043
0.026
R²
R²
0.859
0.886
0.763
C-heteroatom
12.07 0.46
10.52 0.48
9.47 - 0.46
9. 9.23 + 40
10.37 0.42
9.55 9.55 + 0.39
2.61 + 0.11
2.58 + 0.06
2.30 + 0.17
2.65 + 0.10
1.5710.1 0.11
1.94
SM
RMSE
RMSE
2.57
8.13
6.60 0.27
0.22 0.22
6.04 0.18
0.18
0.59 0.20
0.20
6.09 0.18
1.23 1.23 0.14
1.03 0.12
0.90 + 0.08
1.04 + 0.11
0.90 1 0.09
0.87
MAE
MAE
6.12
6.59
0.951 + 0.005
0.004
0.001
0.001
0.971 + 0.002
0.976 + 0.001
0.861 + 0.015
0.889 + 0.017
0.905 - 0.007
0.012
0.012
0.916 0.007
R²
R²
0.969
0.974
0.976
0.859
0.901
6.01 1 0.27
0.26
4.43 = 0.09
4.36 4 0.10
4.63 0.14
4.36 + 0.09
N.S acetal
1 0.02
0.22 0 0.01
0.21 0.01
0.26 +: 0.02
0.21 0.01
0.20 00011
BH
RMSE
RMSE
4.80
0.23
3.99 + 0.15
2.92 + 0.06
2.89 + 0.06
3.11 0.07
2.98 + 0.05
0.16 + 0.01 0.01
0.16 0.16+0.00 0.01
0.15 0.00
0.14 0.01
0.14 0.00
MAE
MAE
0.19
YieldBERT-DA
YiclDBERT-DA
YieldBERT
YieldBERT
UA-GNN
RcaMVP
ReaDISH
UA-GNN
ReaMVP
ReaDISH
Method
Method
UAM
UAM
28

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C-heteroatom (atalyst-based))
NiCOlit (substrate- (aute-bbsed]
0.463
0.483
0.563
0.446
0.489
0.574
0.309
0.351
0. 0.394
0.346
0.361
0.409
R²
R²
RMSE
21.91
21.27
22.19
22.99
21.43
21 11
RMSE
3.70
3.58
3.46
3.62
3.54
3.42
MAE
15.53
14.82
17.64
15.01
15.09
MAE
2.11
1.96
1.77
2.01
2.04
2.02
15.
Table 9: Results under out-of sample splits. Bold entries highlight the best performance.
0.021
0.014
0.420 0.022
0.520 + 0.029
0.534 + 0.018
0.549 + 0.019
0.735
0. 0.764
0.780
0.630
0.762
0,790
R²
R²
0.469
0.467
acS-acetal (catalyst (aassst-bddd))
SM (ligand -aasbb)))))))
10. 59 1 0.39
19 64.64 + 0.26
20.45 48 - 0.39
18.63 - 0.41
18.36 = 0.35
18.05 + 0.37
RMSE
RMSE
0.361
0.340
0.329
0.429
0.342
0.327
14.85 + 0.36
15.78 15.88 + 0.24
15. 15.59 + 0.36
14.46 + 0.37
13.90 + 0.29
14.22 + 0.33
MAE
MAE
0.258
0. 2.2
0. 1.2
0.305
0.234
0.226
0.350 1: 0.00 0.060
0.215 + 0 .20
0.301 + 0.035
0.458 + 0.051
0.479 + 0.056
0.480 + 0.032
0.151 0 1 0.102
0.171 + 0.112
0.203 + 0.054
0.179 0.050
0,212 1: 0.057
0.230 +: 0.047
R²
R²
BH (reactant- (ractant-bused)] -bssdd
23. 90 1.13
0.33
24.82 + 0.63
21.78 + 1.18
21.40 .1.40 1.15
0.80
2.14
+ 1.98
1.08
1.78
1 1.29
+ 1.12
RMSE
ELN
RMSE
26. 30
21.76
27.67
26.97
26.50 50
26. 65
26 26.36
25.99 .9
17.44 + 1.01
18.41 0.48
16.90 0.21
16. 16.99 = 0.55
17.17 17.17 0.83
16.29 +: 0.30
21.98 2.27
21.58 2.19
20.64 + 1.13
22.10 + 1.35
20.69 1: 1.33
20.82 #: 1.01
MAE
MAE
VielBBRRT-A
VieldBERTDA
YieldBERT
YieldBERT
UA ANN
ReaMVP
ReaDISH
UA-GNN
ReaMVP
ReaDISH
Method
Method
UAM
UAM
29

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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.
30


Reviews:
[{"review_id": "urvCRUmJbw", "reviewer": "Reviewer_619R", "summary": "The 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.", "questions": "1 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?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: 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.", "was_revised": true, "final_justification": "All my concerns have been resolved, thanks authors for the rebuttal.", "rebuttal": "We sincerely appreciate your constructive and thorough comments. In the following, we have addressed your concerns and questions point-by-point.\n\n> **W1: Reproducibility and runtime**\n\nWe understand the reviewer's concerns regarding reproducibility and runtime. Shingle generation and pairwise interaction computation are highly efficient: even for large molecules, processing a downstream dataset takes under 4 minutes; generating shingles for 3.9M reactions from the pretraining dataset takes ~7 hours with 50 CPU threads. The **main cost lies in training attention layers**, not preprocessing: fine-tuning a benchmark dataset takes ~1 hour on a single GPU. We clarify that **code has already been submitted in the initial submission** to ensure reproducibility, and we will explicitly mention the computational cost in the appendix.\n\n> **W2: Chemically meaningful substructures**\n\nWe appreciate the reviewer's concern that the defined fragments may miss relevant chemistry. Our design mitigates this by focusing on **structural changes** rather than all fragments: the symmetric-difference operation highlights fragments that differ between reactants and products, naturally emphasizing **functional groups and reaction centers**. As a result, irrelevant or uninformative fragments contribute minimally to the representation.\n\n> **W3: Reactions beyond substructures**\n\nWe understand the reviewer's concerns regarding unseen functional groups or transformations beyond predefined substructures. Our method addresses this in two ways:   (1) **Shingles are represented as continuous embeddings of local topological environments**, not as a fixed dictionary of discrete functional groups. This distributed representation allows the model to generalize to unseen groups. (2) Most chemical transformations are **dominated by local rearrangements around functional groups**, a well‑established principle in organic chemistry [1,2]. Thus, focusing on local environments captures the majority of reaction mechanisms in our tasks.\n\n> **Q1: Obtain geometric distance in practice**\n\nAs described in Equation 6 (page 5), we generate 3D coordinates for each molecule using RDKit's conformer generation. For each shingle, we compute its centroid by averaging the coordinates of its constituent atoms, and define the geometric distance between shingles as the Euclidean distance between centroids.\n\n> **Q2: Molecular shingles definition**\n\nOur approach is consistent with the fragment strategy used in Morgan fingerprints but extends it by explicitly including ring structures, which are well established in cheminformatics [3,4]. Each shingle corresponds to a local atomic environment defined by a fixed radius around a central atom, supplemented with whole rings when present. Reactant and product shingles are generated separately, and their symmetric difference encodes structural changes.\n\n> **Q3: Incorporating condition variables**\n\nWe highly agree that reaction conditions can strongly influence outcomes. Hence, our model **explicitly incorporates molecular condition variables** (e.g., catalysts, solvents) by treating them as part of the shingle representation. Non-molecular conditions (e.g., temperature, pressure) are excluded due to sparse availability in datasets.\n\n---\n\n**References**\n\n[1] Larock, R. C. *Comprehensive Organic Transformations: A Guide to Functional Group Preparations* (3rd ed.). Wiley, 2018.  \n\n[2] OpenStax. *Organic Chemistry*, 2023.  \n\n[3] 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.  \n\n[4] M. Orsi and J. L. Reymond. One chiral fingerprint to find them all. *Journal of Cheminformatics*, 16(1):53, 2024."}, {"review_id": "USI3itJc6r", "reviewer": "Reviewer_NvNL", "summary": "In 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.", "questions": "The 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Pros: \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", "was_revised": true, "final_justification": "This paper introduces ReaDISH, a novel method for predicting chemical reaction properties like yield, selectivity, and classification. The authors rightly identify two key limitations in prior work: sensitivity to the order of input molecules (permutation variance) and the failure to adequately model substructural interactions that govern chemical reactivity.\n\nThe core contribution of this work is twofold. First, the proposed symmetric difference of molecular shingle sets is an elegant solution to isolate the substructures that are directly transformed during a reaction. This approach not only focuses the model's attention on the most salient parts of the reaction but also inherently guarantees permutation invariance. Second, the interaction-aware attention mechanism enriches the model by explicitly encoding geometric distance, chemical connectivity, and structural distance as pairwise biases. This allows for a more nuanced understanding of the relationships between the reacting molecular fragments.\n\nEmpirically, ReaDISH demonstrates state-of-the-art or highly competitive performance across all evaluated tasks. Its strength is particularly evident on the more challenging out-of-sample splits, highlighting the model's robust generalization capabilities. The claims are well-supported by thorough ablation studies that validate the effectiveness of the proposed components.\n\nPost-rebuttal: My initial concerns regarding the comparison to certain baselines (e.g., cross-attention) were satisfactorily addressed by the authors in their rebuttal. Consequently, I have raised my score to Borderline Accept.", "rebuttal": "Thank you for your valuable comments and suggestions on our submission. In the following, we have addressed your concerns and questions point-by-point.\n\n> **W & Q: Selection of baseline models**\n\nThanks for your suggestion! We emphasize that **Equiformer [1] is originally designed for single-molecule representation rather than reaction modeling**. To adapt it, we merge reactants and products into a unified graph and compute edges only within each molecule. While feasible, this adaptation **cannot capture inter‑molecular interactions**, which are central to our symmetric‑difference shingle approach. Reaction datasets are also **much larger and more complex** than molecular benchmarks like QM9, with more atom types and larger graphs (nodes and degrees), as summarized in the table below. This complexity significantly increases computation and memory for models like Equiformer. Due to these constraints, we do not extend to the USPTO_TPL dataset, which contains 445K reactions.\n\n| Dataset        | QM9   | BH     | SM     | ELN    | NiCOlit | N,S-acetal | C-heteroatom |\n| -------------- | ----- | ------ | ------ | ------ | ------- | ---------- | ------------ |\n| _MAX_ATOM_TYPE | 5     | 10     | 16     | 17     | 24      | 9          | 10           |\n| _AVG_NUM_NODES | 18.03 | 116.15 | 110.00 | 101.35 | 81.71   | 103.49     | 100.29       |\n| _AVG_DEGREE    | 15.58 | 25.78  | 23.43  | 24.98  | 22.35   | 28.65      | 30.13        |\n\nResults (R$^2$) of Equiformer are provided in the table below, and we will update in the revised version. Although Equiformer benefits from its equivariant design, it falls notably short of our method. The key reason is that Equiformer's attention operates **only within molecules**, relying on intra‑molecular distances and edges, while **reaction modeling requires inter‑molecular attention**. Our method addresses this by leveraging the symmetric difference of shingles to model structural changes explicitly, yielding superior predictive performance. Regarding other baselines, YieldBERT (SMILES‑based) is included to cover NLP‑oriented methods despite lacking graph expressiveness. UA-GNN may not reflect the most recent architectures, but it remains a competitive graph-based baseline with strong performance in our experiments. \n\n| Model (random)            | BH                | SM                | NiCOlit           | N,S-acetal        | C-heteroatom      |                   |\n| ------------------------- | ----------------- | ----------------- | ----------------- | ----------------- | ----------------- | ----------------- |\n| Equiformer                | $0.958 \\pm 0.004$ | $0.847 \\pm 0.003$ | $0.497 \\pm 0.006$ | $0.875 \\pm 0.002$ | $0.713 \\pm 0.004$ |                   |\n| ReaDISH                   | $0.976\\pm0.001$   | $0.886\\pm0.008$   | $0.539 \\pm 0.024$ | $0.916 \\pm 0.007$ | $0.763 \\pm 0.026$ |                   |\n| **Model (out-of-sample)** | **BH**            | **SM**            | **NiCOlit**       | **N,S-acetal**    | **C-heteroatom**  | **ELN**           |\n| Equiformer                | $0.429 \\pm 0.005$ | $0.449 \\pm 0.014$ | $0.442$           | $0.731$           | $0.321$           | $0.178 \\pm 0.005$ |\n| ReaDISH                   | $0.480 \\pm 0.032$ | $0.549 \\pm 0.019$ | $0.574$           | $0.790$           | $0.409$           | $0.230 \\pm 0.047$ |\n\n---\n\n**References**\n\n[1] Liao, Y. L., & Smidt, T. *Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs*. ICLR 2023."}, {"review_id": "GNnXR6Vsnm", "reviewer": "Reviewer_RXFe", "summary": "The 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.", "questions": "Major 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.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "I 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.", "was_revised": true, "final_justification": "Based on the extensive discussion with the authors, I have decided to raise my review score to 4. I think the authors addressed all of my concerns and were able to resolve most of them. In my opinion, the main outstanding points that prevented an even higher score were the absence of an appropriate estimate of final model performance, the fact that ReaDISH, at least for some very well studied literature baselines that allow for comparison to a wide range of methods, likely reaches state of the art but does not outperform the current state of the art, and the fact that ReaDISH, while a powerful framework, is highly sensitive with respect to key hyperparameters, making it somewhat less straightforward to apply effectively in practice than some of the current baseline approaches in the field.", "rebuttal": "Thank you for taking your valuable time and providing us with valuable feedback. In the following, we have addressed your concerns and questions point-by-point.\n\n> **Q1: Conformer generation and sensitivity**\n\nWe understand the reviewer's concerns regarding conformers. We generate 100 conformers to approximate plausible 3D geometries, cluster them using the RDKit Butina algorithm (rdkit.ML.Cluster.Butina, threshold 0.25), and sample a conformer from the representative clusters with probabilities proportional to their sizes. This procedure ensures that the chosen conformer is both **structurally plausible (reflecting common conformations)** and **diverse enough** to avoid biasing the model toward a single geometry. To assess sensitivity, we have repeated training and evaluation five times **using independently sampled conformers** and performance variations (standard deviations) are minimal (see Tables 6 and 7 in the appendix), indicating robustness to conformer choice. Regarding Figure 6, we clarify that the permutation and token-ordering analysis is performed **using identical conformers**, and we will explicitly state this assumption in the revised version.\n\n> **Q2: Hyperparameter optimization**\n\nWe understand the reviewer's concerns regarding optimizing hyperparameters. All results reported in tables and figures are based on the test set, with hyperparameters selected using the training set following prior work to ensure fair comparison. While we acknowledge that an additional validation set could further guard against overfitting, our setup ensures **fair comparison** with existing methods.\n\n> **Q3: Radius and number of shingles**\n\nWe understand the reviewer's concerns regarding the detailed settings of shingles. (1) We set the radius to 3 as the best trade-off between performance and efficiency, as prior works in cheminformatics do [1,2]. We have **added ablation studies with radius** and will update in the revised version, as shown in the table below. Radius 4 performs the worst, likely due to generating too many shingles, leading to redundancy and increased complexity, as well as higher runtime and memory costs. Radius 2 is slightly worse than radius 3. (2) We apologize for the typo of \"100 shingles per reaction\". Actually, our implementation considers up to **280 shingles per reaction** and 100 shingles per molecule. The per-molecule bound prevents rare cases where one molecule generates an excessive number of shingles and dominates the representation. The per-reaction bound functions similarly to a maximum sequence length in Transformers, preventing rare, large reactions from causing OOM issues. Figure 7 in the appendix shows the shingle distribution, confirming that the bound comfortably covers nearly all reactions. (3) We currently do not apply additional selection strategies for reactions exceeding this bound and have noted this as a future direction in the limitations section.\n\n| Methods    | BH (OOS)         |                  |                   | SM (OOS)         |                  |                   |\n| ---------- | ---------------- | ---------------- | ----------------- | ---------------- | ---------------- | ----------------- |\n|            | MAE              | RMSE             | R$^2$                | MAE              | RMSE             | R$^2$                |\n| r=2        | $16.50 \\pm 0.15$ | $22.11 \\pm 0.25$ | $0.465 \\pm 0.014$ | $14.41\\pm0.33$   | $18.57\\pm0.44$   | $0.523\\pm0.228$   |\n| r=4        | $17.91 \\pm 0.40$ | $23.90 \\pm 0.01$ | $0.421 \\pm 0.006$ | $14.88\\pm0.56$   | $18.89\\pm0.82$   | $0.505\\pm0.043$   |\n| r=3 (ours) | $16.29 \\pm 0.30$ | $21.76 \\pm 0.80$ | $0.480 \\pm 0.032$ | $14.22 \\pm 0.33$ | $18.05 \\pm 0.37$ | $0.549 \\pm 0.019$ |\n\n> **Q4: More ablation studies**\n\nWe understand the reviewer's concerns regarding ablation studies. Beyond the radius ablation (see Q3), we have **conducted an additional ablation removing shingles and using only atoms**, and will update in the revised version. In this case, symmetric difference cannot be applied (atoms remain unchanged between reactants and products), so we adopt a union-based representation for comparison. Results in the table below show that this atom-only variant performs worse than the shingle-based model, confirming that **local substructures are critical** for modeling reaction transformations.\n\n| Methods          | BH (OOS)         |                  |                   | SM (OOS)         |                  |                   |\n| ---------------- | ---------------- | ---------------- | ----------------- | ---------------- | ---------------- | ----------------- |\n|                  | MAE              | RMSE             | R$^2$                | MAE              | RMSE             | R$^2$                |\n| w/o shingle      | $17.97\\pm0.26$   | $23.72\\pm0.18$   | $0.409\\pm0.015$   | $14.51\\pm0.32$   | $18.85\\pm0.47$   | $0.508\\pm0.024$   |\n| w shingle (ours) | $16.29 \\pm 0.30$ | $21.76 \\pm 0.80$ | $0.480 \\pm 0.032$ | $14.22 \\pm 0.33$ | $18.05 \\pm 0.37$ | $0.549 \\pm 0.019$ |\n\n> **Q5: Performance regarding training size**\n\nWe understand the reviewer's concerns regarding training size. We have scaled training size on BH and SM benchmarks and will update in the revised version, **using subsets of 5%, 10%, 20%, 30%, 50%, and 70% while testing on the full 30% test split**. Results in the table below show that predictive accuracy (R$^2$) steadily improves without saturation, suggesting further gains with larger datasets. But the rate of increase has slowed down. We will add this analysis to the appendix.\n\n| Dataset | 5%                | 10%             | 20%             | 30%             | 50%             | 70%             |\n| ------- | ----------------- | --------------- | --------------- | --------------- | --------------- | --------------- |\n| BH      | $0.713 \\pm 0.173$ | $0.808\\pm0.172$ | $0.874\\pm0.168$ | $0.919\\pm0.006$ | $0.955\\pm0.002$ | $0.976\\pm0.001$ |\n| SM      | $0.508\\pm0.018$   | $0.649\\pm0.008$ | $0.751\\pm0.010$ | $0.810\\pm0.001$ | $0.851\\pm0.010$ | $0.886\\pm0.008$ |\n\n> **Q6: More baseline comparison**\n\nWe understand the reviewer's concerns regarding baselines. We have **added Equiformer** [3], an equivariant graph attention model, as a baseline and will update in the revised version. Although originally designed for single-molecule representation, we adapt it for reactions by merging reactants and products into a unified graph, computing edges within each molecule. Results (R$^2$) in the table below show that **our model outperforms Equiformer** on various benchmarks, underscoring the advantages of our symmetric-difference shingle design.\n\n| Model (random)            | BH                | SM                | NiCOlit           | N,S-acetal        | C-heteroatom      |                   |\n| ------------------------- | ----------------- | ----------------- | ----------------- | ----------------- | ----------------- | ----------------- |\n| Equiformer                | $0.958 \\pm 0.004$ | $0.847 \\pm 0.003$ | $0.497 \\pm 0.006$ | $0.875 \\pm 0.002$ | $0.713 \\pm 0.004$ |                   |\n| ReaDISH                   | $0.976\\pm0.001$   | $0.886\\pm0.008$   | $0.539 \\pm 0.024$ | $0.916 \\pm 0.007$ | $0.763 \\pm 0.026$ |                   |\n| **Model (out-of-sample)** | **BH**            | **SM**            | **NiCOlit**       | **N,S-acetal**    | **C-heteroatom**  | **ELN**           |\n| Equiformer                | $0.429 \\pm 0.005$ | $0.449 \\pm 0.014$ | $0.442$           | $0.731$           | $0.321$           | $0.178 \\pm 0.005$ |\n| ReaDISH                   | $0.480 \\pm 0.032$ | $0.549 \\pm 0.019$ | $0.574$           | $0.790$           | $0.409$           | $0.230 \\pm 0.047$ |\n\n> **Q7: Shingle generation details**\n\nWe understand the reviewer's concerns regarding generating shingles. We clarify that **code has already been submitted in the initial submission** to ensure reproducibility. Each shingle is defined by a fixed-radius local environment plus whole rings detected via RDKit (AllChem.GetSymmSSSR).\n\n> **Q8: Training separately or not**\n\nWe train **separately per dataset** to optimize for task-specific objectives (e.g., yield regression vs. reaction classification). While multi-task pretraining is possible, our goal is not to build a foundation model but to show the benefits of shingles compared to task-specific baselines.\n\n> **Q9: More results in the main text**\n\nWe agree that presenting more comprehensive results in the main text would improve clarity. We will move additional results from the appendix to the main text in the revised version. Performance trends remain consistent across datasets, but paper **space constraints** previously limited the inclusion.\n\n> **Q10: A typo error**\n\nWe thank the reviewer for identifying it. The missing bold formatting is a typographical error and will be corrected in the revised version.\n\n---\n\n**References**  \n\n[1] 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.\n\n[2] M. Orsi and J. L. Reymond. One chiral fingerprint to find them all. *Journal of Cheminformatics*, 16(1):53, 2024.\n\n[3] Liao, Y. L. & Smidt, T. Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. *ICLR 2023*."}, {"review_id": "5KAxMk3pd4", "reviewer": "Reviewer_FGGr", "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.", "questions": "- Can the authors provide more details on the computational efficiency of the shingle generation process?\n- Is the symmetric difference between reactant and product shingles always well-defined and non-empty? If not, how are such cases handled？\n- 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?\n- 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", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n- Well-written and easy to follow.\n- Novel chemical reaction modeling method.\n- Extensive experiments are conducted to demonstrate the efficacy of ReaDISH.\n\n## Weaknesses\n\n- The paper lacks a detailed analysis of ReaDISH's computational complexity and resource requirements.\n- As acknowledged, the model's scalability is a key limitation. Handling a large number of shingles requires higher computational costs for complex reactions.", "was_revised": true, "final_justification": "The author's response addressed my prior concerns. I maintain the original positive rating.", "rebuttal": "Thank you for your time and constructive feedback. In the following, we have addressed your concerns and questions point-by-point.\n\n> **Q1 & W2: Shingle generation efficiency**\n\nWe understand the reviewer's concerns regarding the efficiency of shingle generation. Shingle generation and pairwise interaction computation are highly efficient: even for large molecules, processing a downstream dataset takes under 4 minutes. For our large-scale pretraining corpus of 3.9M reactions, generation requires ~7 hours on 50 CPU threads. We will explicitly mention the computational cost in the appendix.\n\n> **Q2: Validity of symmetric difference**\n\nWe understand the reviewer's concerns regarding the validity of symmetric difference. The symmetric difference between reactant and product shingles is **almost always non‑empty**, as reactions involve meaningful structural changes. Rare empty cases arise from: (1) **Simple reactions**: The structural change is minimal, and the union of reactant and product shingles can still provide a meaningful representation. (2) **Data errors**: Some reactions in the pretraining datasets are incorrectly recorded (e.g., A -> A + B). These are identified and removed during preprocessing. We emphasize that such cases do not appear during fine-tuning.\n\n> **Q3 & W1: Computational efficiency analysis**\n\nWe understand the reviewer's concerns regarding computational efficiency. ReaDISH is computationally efficient across datasets. Shingle generation remains under 4 minutes for thousands of reactions, and the larger benchmark (e.g., BH) requires ~1 hour of training time compared to ~0.5 hour for smaller datasets (e.g., ELN). The union‑based variant **increases memory by ~3 times** on large datasets (e.g., SM) and **does not improve accuracy** due to redundant shingles. We will explicitly mention the computational cost in the appendix for clarity.\n\n> **Q4: Task-specific strengths or weaknesses**\n\nThank you very much for your interest in our method. By modeling the symmetric difference of shingles, ReaDISH captures **structural transformations** critical for reaction prediction, particularly for heteroatom‑centered and stereochemically sensitive tasks. We observe 1.5% (C-heteroatom) and 1.8% (ELN) gains in out‑of‑sample evaluations, demonstrating its suitability for such chemical changes."}]

Author rebuttal:

Meta-review: This submission proposes a reaction property prediction model called ReaDISH. The key idea is to decompose each molecule into local "shingles" and represent reactions using the symmetric difference between the sets of shingles for reactants and products. An interaction-aware attention mechanism is employed to incorporate three types of pairwise relationships between shingles: geometric distance, chemical connectivity, and structural distance. While concerns have been raised about computational cost and efficiency, the model demonstrates strong empirical performance across a wide range of experiments on multiple benchmarks, leading to an overall positive evaluation.

The authors have also committed to incorporating revisions suggested during the discussion phase. Given the potential relevance and interest of these findings to researchers in the field, I support accepting this paper.
Decision: Accept (poster)