Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis

## Abstract

Abstract Graph neural networks have demonstrated remarkable success in predicting molecular properties by leveraging the rich structural information encoded in molecular graphs. However, their black-box nature reduces interpretability, which limits trust in their predictions for important applications such as drug discovery and materials design. Furthermore, existing explanation techniques often fail to reliably quantify the contribution of individual atoms or substructures due to the entangled message-passing dynamics. We introduce SEAL (Substructure Explanation via Attribution Learning), a new interpretable graph neural network that attributes model predictions to meaningful molecular subgraphs. SEAL decomposes input graphs into chemically relevant fragments and estimates their causal influence on the output. The strong alignment between fragment contributions and model predictions is achieved by explicitly reducing inter-fragment message passing in our proposed model architecture. Extensive evaluations on synthetic benchmarks and real-world molecular datasets demonstrate that SEAL outperforms other explainability methods in both quantitative attribution metrics and human-aligned interpretability. A user study further confirms that SEAL provides more intuitive and trustworthy explanations to domain experts. By bridging the gap between predictive performance and interpretability, SEAL offers a promising direction for more transparent and actionable molecular modeling.

## Introduction

Predicting molecular properties is a cornerstone task in computational chemistry, with applications spanning drug discovery, materials science, and chemical synthesis (Elton et al. 2019). Graph Neural Networks (GNNs) have emerged as state-of-the-art models for this task, thanks to their ability to naturally represent molecular structures as graphs of atoms and bonds (Wieder et al. 2020). However, despite their impressive predictive performance, GNNs suffer from a critical limitation: their decision-making processes are largely opaque. Moreover, explaining the predictions of GNNs through external methods is challenging, primarily due to the message aggregation mechanism, which is invariant to node permutation. This results in a disorganized exchange of information among nodes, making explanations increasingly difficult with each graph layer. This often leads to the well-known issue of oversmoothing, where the atomic representation becomes independent of its localization within the graph (Zhang et al. 2023). This challenge creates a significant problem when applying GNNs in domains where interpretability is crucial for advancing scientific discovery.

We hypothesize that GNNs operating on molecular graphs decomposed into fragments are more interpretable while preserving sufficient expressivity. Many chemical prediction tasks inherently depend more on the presence and identity of specific fragments than on complex interactions between them. Functional groups and other substructures often dominate the behavior of molecules with respect to properties such as solubility, toxicity, and reactivity (von Korff and Sander 2006; Tanaka, Okamoto, and Bersohn 2010; Tuttle et al. 2023). For instance, in drug discovery, the binding affinity of a molecule to a target protein is frequently the cumulative result of individual fragment-level interactions, such as hydrogen bonds, hydrophobic interactions, or $\pi$-$\pi$ stacking from aromatic rings, each contributing additively to the overall binding energy (Murcko 1995). Similarly, properties like lipophilicity or metabolic stability are often strongly associated with the presence of particular chemical motifs, irrespective of subtle global dependencies. While GNNs are capable of modeling complex dependencies between atoms, in many real-world tasks, fragment-level contributions offer sufficient explanatory power for guiding chemical reasoning and decision-making. By focusing on these contributions, the fragment-based model can produce explanations that are chemically intuitive and well-suited for application in molecular design.

Figure: Figure 1: Overview of SEAL comprising a message passing stage (a), in which a molecular graph is divided into fragments and different weights are applied within and between fragments, and a fragment aggregation stage (b), in which fragment representations are computed and used to produce fragment contributions; the model prediction is a sum of all fragment contributions.
Refer to caption: x1.png

Recent efforts have sought to interpret GNN predictions by attributing importance to individual nodes, edges, or subgraphs (Ponzoni, Páez Prosper, and Campillo 2023). While these methods provide some insight, they often fail to produce explanations that align with the way chemists conceptualize molecular behavior. Chemists typically reason about molecules not as collections of individual atoms or bonds but in terms of fragments, such as functional groups, aromatic rings, or side chains, that are strongly associated with specific properties or behaviors (Talanquer 2022). Existing explainability methods fail to capture this crucial level of abstraction, limiting their practical relevance for molecular modeling.

In this paper, we introduce SEAL (Substructure Explanation via Attribution Learning), a novel interpretable GNN that generates fragment-wise explanations of model predictions for molecular property prediction (Figure [1](https://arxiv.org/html/2508.15015v1#Sx1.F1)). SEAL decomposes molecular graphs into chemically meaningful fragments and quantifies the individual contribution of each fragment to the model output. By aligning interpretability with chemical intuition, our approach enables domain experts to leverage machine learning predictions for better hypothesis generation and experimental design. Our contributions are threefold:

- 1.
We propose a novel approach for fragment-based interpretability in GNNs for molecular property prediction.
- 2.
We define a new graph convolutional layer and regularization terms that facilitate information retention in the fragments, resulting in more intuitive explanations.
- 3.
We conduct extensive experiments that demonstrate that our model produces more coherent and actionable explanations compared to existing methods while maintaining competitive predictive performance.

The code is available at https://github.com/gmum/SEAL.

## Related Work

##### Graph neural networks.

GNNs have become a standard method for analyzing molecular data, often using either a message-passing mechanism (Gilmer et al. 2020) or a transformer-based architecture (Rong et al. 2020; Maziarka et al. 2024). Some of these networks work on fragment graphs where atom groups serve as nodes instead of individual atoms. For example, Cao et al. (2024) proposed a GNN that uses fragment-level message passing for better explainability but still relies on external explainers to determine fragment contributions. Wang et al. (2025) recently introduced FragFormer, a transformer that operates on fragments and employs a variant of the CAM method (Zhou et al. 2016) to explain its predictions. In both models, fragments can contain significant signals coming from other parts of the molecule, potentially reducing local interpretability.

##### Graph-based explainers.

Many explainable AI (XAI) techniques have been proposed to elucidate the predictions of GNNs. Some identify important subgraphs by perturbing the input graph (Ying et al. 2019; Vu and Thai 2020; Yuan et al. 2021), while other methods analyze the message-passing mechanism in each layer (Feng et al. 2022; Gui et al. 2023). Often, explaining GNNs is difficult due to the large number of subgraphs and the complex message-passing process. Therefore, Henderson, Clevert, and Montanari (2021) proposed regularization techniques that disentangle node representations, aiding in generating better explanations. Another approach involves presenting explanations at the fragment level. For instance, Wu et al. (2023) employed BRICS (Degen et al. 2008) to break down molecules into chemically plausible segments and elucidate predictions by masking entire molecular fragments. Inherently interpretable methods are also being developed, including prototype-based graph neural networks (Zhang et al. 2022; Rymarczyk, Dobrowolski, and Danel 2023) and attention-based models (Xiong et al. 2019; Lee et al. 2023). However, prototypical parts and attention maps on graphs can still be difficult for humans to interpret because of the multitude of explanation patterns that need to be analyzed.

## Methods

### Fragment Contributions

The interpretability of our model is achieved by redesigning the prediction head in graph-based models. Typically, a readout function in GNNs is used to create a graph-level representation, and then an MLP is applied to make predictions. However, the graph readout aggregates information from all atoms in the graph, hindering the ability to attribute predictions to specific atoms or functional groups.

Our model first aggregates information within graph fragments, which can be predefined or learned through a differentiable graph pooling layer. In our method, we use sum pooling followed by a LayerNorm (Ba, Kiros, and Hinton 2016) to create the fragment representation from the fragment atom representations. Then, the contribution for each fragment is computed with an MLP, and the final prediction is the sum of all fragment contributions.

Let us define a molecular graph $\mathcal{G}=(\mathcal{V},\mathcal{E},X)$, where $\mathcal{V}=\{v_{i}\}_{i=1}^{N}$ is a set of nodes corresponding to atoms, $\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}$ is a set of edges corresponding to chemical bonds, $X\in\mathbb{R}^{N\times D}$ is an atom feature matrix, and $D$ is the number of node features. After passing this graph through a sequence of GNN layers, a matrix of atom representations $H\in\mathbb{R}^{N\times M}$ is obtained. Each atom is assigned to exactly one of the $K$ fragments $\mathcal{F}_{1},\dots,\mathcal{F}_{K}$. Then, the model output is computed as follows:

$$ $\displaystyle\bar{\mathbf{h}}_{i}=\sum_{v_{j}\in\mathcal{F}_{i}}\mathbf{h}_{j},\quad c_{i}=\operatorname{MLP}\left(\bar{\mathbf{h}}_{i}\right),$ (1) $\displaystyle\hat{y}=\sum_{i=1}^{K}c_{i}+b,$ (2) $$

where $\bar{\mathbf{h}_{i}}$ is the representation of $i$-th fragment, $c_{i}$ is the contribution of this fragment, $b$ is a trainable bias term, and $\hat{y}$ is the model prediction. The fragment contributions can be interpreted as the importance of these fragments. The bias is crucial to calibrate the model predictions, particularly when the label distribution is not centered at zero. In that case, the lack of bias would cause an equal distribution of contributions across all fragments, diminishing the interpretability of the model.

In all of our experiments, we use a variant of the BRICS algorithm (Degen et al. 2008) similar to that proposed by Zhang et al. (2021). Briefly, we isolate side chains from rings, even if the side chain contains only one atom, and non-ring atoms with four or more neighbors are also treated as separate fragments. Additionally, we cut all non-ring bonds that connect two rings and all halogen groups.

### SEAL-GCN

The aggregation of messages from neighboring nodes in GNNs is invariant to node permutations. While this mechanism is effective in extracting meaningful information from molecular graphs needed for making correct predictions, the information from each node is easily diffused in the graph, hurting the model’s ability to localize crucial atoms and leading to the known problem of oversmoothing.

To mitigate the problem of leaking unnecessary information to neighboring nodes, we propose SEAL-GCN, a new graph neural network variant that operates on pre-fragmented graphs, controlling the information exchanged between fragments. In our implementation, graph layers have separate weights for interfragment and intrafragment edges. This enables the network to extract relevant information within molecular fragments and block information leaks to neighboring fragments. The SEAL-GCN layer is defined as follows:

$$ $\mathbf{h}_{i}^{\prime}=W\mathbf{h}_{i}+W_{\text{intra}}\operatorname*{mean}_{j\in\mathcal{N}_{\text{in}}(i)}\mathbf{h}_{j}+W_{\text{inter}}\operatorname*{mean}_{j\in\mathcal{N}_{\text{out}}(i)}\mathbf{h}_{j},$ (3) $$

where $\mathcal{N}_{\text{in}}(i)$ is a set of neighbors of the $i$-th node within the same fragment, and $\mathcal{N}_{\text{out}}(i)$ is the set of its neighbors outside the fragment. If any of these sets is empty, the corresponding term is removed from the formula.

To avoid leakage of information that is not crucial for prediction, we introduce a regularization term to the loss function, which is the $L_{1}$ norm of the interfragment weights $W_{\text{inter}}$. This term is controlled by a hyperparameter $\lambda$ that should be chosen on a case-by-case basis, but typically higher values lead to more interpretable results. The loss function in our model is:

$$ $\mathcal{L}=\mathcal{L}_{\text{pred}}+\lambda\sum_{l=1}^{L}\left\|W_{\text{inter}}^{(l)}\right\|_{1}$ (4) $$

where $\mathcal{L}_{\text{pred}}$ is the prediction error loss function (mean squared error for regression and cross entropy for classification), $W_{\text{inter}}^{(l)}$ are the interfragment weights in the $l$-th layer.

To balance the trade-off between model performance and interpretability, we perform a cross-validation testing multiple values of $\lambda$. The selected model is the one with the highest $\lambda$ values that is not significantly worse than the best model in terms of the target metric (e.g. RMSE or ROC AUC) according to the Wilcoxon signed-rank test.

## Results

All experiments were conducted on an NVIDIA Grace Hopper GH200 (see Appendix A for training details and implementation).

### Synthetic dataset benchmark

Real-world molecular datasets only offer graph-level labels without assigning importance to specific atoms. Therefore, we chose to first use a synthetic dataset that allows for controlled and reliable assessment of Explainable AI (XAI) methods by providing direct ground-truth explanations.
We evaluate our method on the B-XAIC benchmark (Proszewska, Danel, and Rymarczyk 2025), which is designed to compare GNN-based XAI methods in the molecular domain. The dataset includes various tasks focused on identifying different substructures: boron atoms (B), phosphorus atoms (P), halogens (X), indole rings, and pan-assay interference compounds (PAINS). The remaining two tasks focus on counting rings or atoms within rings. Each task has a known ground truth explanation, enabling a precise evaluation of the model’s explanation quality.

#### Metrics.

To evaluate both model performance and explanation faithfulness, we use a two-part evaluation strategy. For classification, we report standard metrics such as AUROC, F1 Score, and Accuracy, where the last two metrics are available in the Appendix B. For interpretability of explanations, we use the same as proposed by  Proszewska, Danel, and Rymarczyk (2025).
Subgraph Explanations (SE) is the AUROC metric computed between model explanation and the ground-truth explanation for the positive examples. Null Explanations (NE) is the percentage of outliers in explained node attributions computed using the interquartile range method for the negative examples.

**Table 1: AUROC score of various graph neural network architectures on the B-XAIC benchmark, including the GIN model used for explanations in the original benchmark. The last row shows the regularization value selected for SEAL on the validation set.**
| Model | rings-count | rings-max | X | P | B | Indole | PAINS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| GIN | 1.00 $\pm$ 0.00 | 0.93 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
| GCN | 1.00 $\pm$ 0.00 | 0.82 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |
| GAT | 0.88 $\pm$ 0.01 | 0.75 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.97 $\pm$ 0.00 | 0.92 $\pm$ 0.01 |
| SEAL | 0.98 $\pm$ 0.01 | 0.99 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
|  | $\lambda=10^{-3}$ | $\lambda=2$ | $\lambda=2$ | $\lambda=2$ | $\lambda=2$ | $\lambda=10^{-4}$ | $\lambda=0$ |

**Table 2: Performance of model explanations on the B-XAIC benchmark. The subgraph explanation (SE) metric is employed for positive examples containing the relevant pattern. The last row shows the regularization value selected for SEAL on the validation set.**
| Model | rings-count | rings-max | X | P | B | Indole | PAINS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Deconvolution | 0.55 $\pm$ 0.24 | 0.38 $\pm$ 0.18 | 0.07 $\pm$ 0.00 | 0.90 $\pm$ 0.00 | 0.72 $\pm$ 0.01 | 0.36 $\pm$ 0.21 | 0.33 $\pm$ 0.01 |
| GuidedBackprop | 0.69 $\pm$ 0.05 | 0.67 $\pm$ 0.02 | 0.94 $\pm$ 0.01 | 0.85 $\pm$ 0.11 | 1.00 $\pm$ 0.00 | 0.85 $\pm$ 0.03 | 0.78 $\pm$ 0.02 |
| IntegratedGradients | 0.36 $\pm$ 0.00 | 0.34 $\pm$ 0.03 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.84 $\pm$ 0.06 | 0.76 $\pm$ 0.02 |
| Saliency | 0.51 $\pm$ 0.04 | 0.55 $\pm$ 0.03 | 0.92 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.87 $\pm$ 0.02 | 0.81 $\pm$ 0.01 |
| InputXGradient | 0.49 $\pm$ 0.03 | 0.47 $\pm$ 0.05 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.74 $\pm$ 0.05 | 0.54 $\pm$ 0.03 |
| GNNExplainer | 0.49 $\pm$ 0.01 | 0.50 $\pm$ 0.00 | 0.50 $\pm$ 0.00 | 0.51 $\pm$ 0.01 | 0.53 $\pm$ 0.05 | 0.53 $\pm$ 0.03 | 0.54 $\pm$ 0.06 |
| SEAL | 0.98 $\pm$ 0.01 | 0.34 $\pm$ 0.03 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.83 $\pm$ 0.01 |
|  | $\lambda=10^{-3}$ | $\lambda=2$ | $\lambda=2$ | $\lambda=2$ | $\lambda=2$ | $\lambda=10^{-4}$ | $\lambda=0$ |

#### Models and baselines.

We benchmark our method against a diverse set of GNN explanation techniques, spanning both mask-based and gradient-based approaches: GNNExplainer (Ying et al. 2019), Saliency Maps (Simonyan, Vedaldi, and Zisserman 2014), InputXGradients (Shrikumar et al. 2016), Integrated Gradients (Sundararajan, Taly, and Yan 2017), Deconvolution (Mahendran and Vedaldi 2016), (Shrikumar et al. 2016), and Guided Backpropagation (Springenberg et al. 2014).

The evaluation of model performance is conducted for three GNN backbones: GCN (Kipf 2016), GAT (Veličković et al. 2017), and GIN (Xu et al. 2018). However, the explanation results are reported only for the GIN model due to its strongest performance across tasks, following the recommendations of the B-XAIC benchmark. Hyperparameters for all models, including SEAL, were optimized through random search. The search space and the optimal hyperparameters found are listed in Appendix A.

Figure: Figure 2: Node-level explanation examples for selected synthetic compounds from the B-XAIC dataset. Each column corresponds to a compound drawn from one of the tasks. From top to bottom: Our method (SEAL), the ground-truth explanation, and the other best explanation technique, denoted as Baseline. Highlighted nodes indicate substructures or atoms contributing most to the prediction. Red denotes a strong positive contribution to the predicted class.
Refer to caption: x2.png

Figure: Figure 3: Effect of regularization on explanation quality in the phosphorus detection task. (a) Plot showing the relationship between $\lambda$ and SE. (b) Visual comparison of explanations generated with two different values of $\lambda$. High $\lambda$ values prevent the attribution of high contribution to neighboring fragments.
Refer to caption: x3.png

#### Results.

Table [1](https://arxiv.org/html/2508.15015v1#Sx4.T1) presents the results of the model performance on the synthetic dataset. Our model achieves competitive classification performance with an AUROC score comparable or even exceeding those of existing GNN architectures, while adding the capability of explaining its predictions.
Table [2](https://arxiv.org/html/2508.15015v1#Sx4.T2) shows the results of the explanation evaluation, where our method yields significantly higher SE scores than other explainers on challenging tasks such as PAINS, rings-count, and indole. In the halogens (X) and phosphorus (P) tasks, our performance is on par with that of the strongest baselines, reflecting the relative ease of localizing single-atom substructures.
A significant decline in performance appears in the boron (B) task due to its frequent appearance in complex substructures that our extended BRICS decomposition cannot efficiently segment (Figure [2](https://arxiv.org/html/2508.15015v1#Sx4.F2)). The largest ring pattern, like boron, mostly occurs in larger substructures, but it also presents an additional challenge due to its highly imbalanced nature, with a low percentage of positive samples across the dataset. The evaluation of the negative examples is provided in the Appendix B.

Figure [2](https://arxiv.org/html/2508.15015v1#Sx4.F2) presents an example explanation generated by our model for randomly selected molecules. It includes both correct explanations and failure cases where larger fragments are highlighted. The figure also compares the ground-truth annotations with the outputs of the best-performing baseline method for each task. SEAL effectively highlights chemically meaningful subgraphs, whereas other approaches tend to assign the prediction to only a few atoms, distributing smaller weights across the entire graph. More examples can be found in Appendix E.

#### Regularization

SEAL dynamically adapts its $\lambda$ parameter to maximize interpretability without sacrificing performance. We perform an ablation study by varying the regularization parameter $\lambda$, which determines how much message passing is restricted in our model. We discover that the optimal value of $\lambda$ depends on the specific task. In some cases, limiting message propagation improves explanation by preventing information from leaking across irrelevant parts of the graph. For example, in the phosphorus (P) task, increasing $\lambda$ leads to a notable improvement in subgraph explanation quality, as shown in Figure [3](https://arxiv.org/html/2508.15015v1#Sx4.F3). This indicates that stronger regularization helps the model concentrate on localized substructures without causing over-smoothing. In contrast, other tasks, such as PAINS detection, require the information to flow across distant parts of the graph. In these cases, we find that the best explanation performance occurs when $\lambda=0$. Notably, low $\lambda$ values cause information leakage into adjacent fragments, whereas higher $\lambda$ values provide more focused and faithful explanations.

### Evaluation on real-world datasets

Evaluating explanation performance on real-world molecular datasets remains a challenging task. Unlike synthetic benchmarks, these datasets do not provide ground-truth explanations that identify which atoms or substructures are responsible for the prediction. Additionally, most of the molecular properties relevant for real-world applications are much more complex, often involving long-range interactions between fragments or features based on the spatial distribution of atoms. To benchmark our method with real-world compounds, we follow the same setup as used for the synthetic dataset.

**Table 3: Scores of various GNN architectures on the real-world datasets (Solubility, hERG, and CYP), using MAE and AUROC as metrics. The last row shows the regularization value selected for SEAL on the validation set.**
|  | AqSol | hERG | CYP2C9 |
| --- | --- | --- | --- |
| Model | MAE $\downarrow$ | AUROC $\uparrow$ | AUROC $\uparrow$ |
| GIN | 0.41 $\pm$ 0.01 | 0.86 $\pm$ 0.01 | 0.86 $\pm$ 0.01 |
| GAT | 0.57 $\pm$ 0.01 | 0.70 $\pm$ 0.01 | 0.68 $\pm$ 0.01 |
| GCN | 0.49 $\pm$ 0.02 | 0.81 $\pm$ 0.03 | 0.84 $\pm$ 0.01 |
| SEAL | 0.47 $\pm$ 0.01 | 0.85 $\pm$ 0.01 | 0.81 $\pm$ 0.01 |
|  | $\lambda=10^{-4}$ | $\lambda=10^{-4}$ | $\lambda=2$ |

**Table 4: Performance of model explanations on real-world datasets (hERG and CYP2C9). Explanations are evaluated using Fidelity metrics at 10%, 20%, and 30% masking thresholds, representing the proportion of most important atoms (nodes) either removed or retained during the evaluation.**
|  | Model | $\text{Fidelity}_{10}+\uparrow$ | $\text{Fidelity}_{10}-\downarrow$ | $\text{Fidelity}_{20}+\uparrow$ | $\text{Fidelity}_{20}-\downarrow$ | $\text{Fidelity}_{30}+\uparrow$ | $\text{Fidelity}_{30}-\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| hERG | Deconvolution | 0.33 $\pm$ 0.06 | 0.49 $\pm$ 0.02 | 0.45 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.48 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |
| GuidedBackprop | 0.39 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.44 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.47 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |  |
| IntegratedGradients | 0.47 $\pm$ 0.07 | 0.49 $\pm$ 0.02 | 0.54 $\pm$ 0.10 | 0.47 $\pm$ 0.03 | 0.58 $\pm$ 0.19 | 0.41 $\pm$ 0.16 |  |
| Saliency | 0.35 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.42 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.46 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |  |
| InputXGradient | 0.33 $\pm$ 0.05 | 0.49 $\pm$ 0.02 | 0.40 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.44 $\pm$ 0.04 | 0.50 $\pm$ 0.02 |  |
| GNNExplainer | 0.42 $\pm$ 0.15 | 0.49 $\pm$ 0.02 | 0.46 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.50 $\pm$ 0.05 | 0.47 $\pm$ 0.04 |  |
| SEAL ($\lambda=10^{-4}$) | 0.63 $\pm$ 0.01 | 0.09 $\pm$ 0.03 | 0.71 $\pm$ 0.01 | 0.07 $\pm$ 0.02 | 0.78 $\pm$ 0.01 | 0.05 $\pm$ 0.02 |  |
| CYP2C9 | Deconvolution | 0.24 $\pm$ 0.02 | 0.40 $\pm$ 0.09 | 0.31 $\pm$ 0.03 | 0.37 $\pm$ 0.03 | 0.34 $\pm$ 0.03 | 0.37 $\pm$ 0.03 |
| GuidedBackprop | 0.33 $\pm$ 0.03 | 0.40 $\pm$ 0.10 | 0.34 $\pm$ 0.04 | 0.39 $\pm$ 0.07 | 0.35 $\pm$ 0.03 | 0.38 $\pm$ 0.05 |  |
| IntegratedGradients | 0.45 $\pm$ 0.08 | 0.29 $\pm$ 0.16 | 0.59 $\pm$ 0.14 | 0.29 $\pm$ 0.16 | 0.68 $\pm$ 0.21 | 0.29 $\pm$ 0.16 |  |
| Saliency | 0.32 $\pm$ 0.04 | 0.38 $\pm$ 0.06 | 0.37 $\pm$ 0.03 | 0.36 $\pm$ 0.03 | 0.38 $\pm$ 0.04 | 0.36 $\pm$ 0.03 |  |
| InputXGradient | 0.31 $\pm$ 0.02 | 0.41 $\pm$ 0.12 | 0.34 $\pm$ 0.02 | 0.41 $\pm$ 0.11 | 0.35 $\pm$ 0.03 | 0.41 $\pm$ 0.11 |  |
| GNNExplainer | 0.30 $\pm$ 0.02 | 0.37 $\pm$ 0.03 | 0.43 $\pm$ 0.10 | 0.43 $\pm$ 0.16 | 0.42 $\pm$ 0.08 | 0.33 $\pm$ 0.08 |  |
| SEAL ($\lambda=2$) | 0.52 $\pm$ 0.02 | 0.04 $\pm$ 0.05 | 0.57 $\pm$ 0.03 | 0.03 $\pm$ 0.04 | 0.66 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |

Figure: Figure 4: Node-level explanation examples for selected compounds from the Aqueous Solubility, CYP 2C9, and hERG datasets. Each row corresponds to a compound from one of the datasets. From left to right: Our method (SEAL), a gradient-based method (Integrated Gradients), a perturbation-based method (GNNExplainer). Highlighted nodes indicate substructures or atoms contributing most to the prediction. Red denotes a strong positive contribution, while blue indicates a strong negative influence on the predicted property.
Refer to caption: x4.png

#### Datasets.

We evaluate our method on three real-world molecular property prediction datasets from TDC (Huang et al. 2021). hERG inhibition (Karim et al. 2021) is a binary classification task that includes molecular structures labeled as hERG blockers or non-blockers, a property critical for cardiac safety assessment in drug development. CYP450 2C9 inhibition (Veith et al. 2009) is a binary classification task that focuses on inhibition of the cytochrome P450 2C9 enzyme, which is central to drug metabolism. Aqueous Solubility (AqSol) (Sorkun, Khetan, and Er 2019) is a regression task that contains compounds with measured solubility in water.

#### Metrics.

To evaluate explanations across different methods and fairly compare them with our model, we decided to evaluate on standard Positive and Negative fidelity. For all models, we mask node features at the input level, ensuring a fair comparison. Fidelity definition that we decided to use:

- •
Positive Fidelity: we mask the most important nodes and check how the prediction has changed.
- •
Negative Fidelity: we retain the most important nodes and check if the prediction is the same.

For classification tasks, fidelity is measured by the proportion of times the predicted class changes after masking. We evaluate masking at thresholds of $10\%,20\%$, and $30\%$ of nodes, ensuring that the most relevant atoms are included in explanations without exceeding the specified percentage. The advantage of our model is that the prediction is a sum of contributions, so we can directly mask contributions instead of masking the input graph nodes and features, which leads to out-of-distribution samples. An ablation study on different masking strategies in SEAL is presented in Appendix C.

#### Results.

Table [3](https://arxiv.org/html/2508.15015v1#Sx4.T3) shows our model’s performance on real-world molecular datasets. In terms of predictive AUROC, our model matches or exceeds the GCN and GAT baselines, while also providing the advantage of interpretability. Compared to the more robust GIN baseline, our model remains competitive without a significant loss in performance. F1 score, Accuracy, and RMSE can be found in Appendix B.

For the explanation evaluation, our model consistently achieves strong fidelity scores. On the hERG dataset, it outperforms all other methods in both positive and negative fidelity with a significant margin. On the CYP dataset, our approach again exceeds existing techniques; only Integrated Gradients shows competitive results but experiences a noticeable drop in negative fidelity, demonstrating the strength of our method in capturing relevant structures.

For regression tasks like Solubility, evaluating explanation quality is more difficult, and not all explainers are well-defined in this context. Nevertheless, our method attains reasonable fidelity values compared to other explanation methods. These results are detailed in Appendix B.

#### Qualitative examples.

In Figure [4](https://arxiv.org/html/2508.15015v1#Sx4.F4), we present qualitative visualizations of explanations generated by our model compared to the top-performing baselines for the AqSolDB, CYP2C9, and hERG datasets. While other methods tend to produce scattered or noisy explanations, our model yields more compact and interpretable substructures. These results show that our approach captures chemically plausible explanations that are easier to interpret and often more localized, especially in tasks like solubility, where polarity and solubility driving fragments are correctly emphasized. More examples can be found in Appendix E.

#### Discussion.

Across all evaluated tasks, our model consistently demonstrates strong performance, both in terms of the prediction performance and explanation faithfulness, while providing an added benefit of interpretability. We got strong and comparative results compared to the GNN baselines. Furthermore, we also outperform the other explainer techniques, in terms of positive and negative fidelity.

By combining strong quantitative results with interpretability aligned with chemical intuition, SEAL proves to be a reliable tool for understanding model decisions across both real and synthetic molecular data. However, fidelity is not a perfect metric because it compares model predictions for the real molecule and its masked counterpart, which has some nodes or their features removed. This artificial reference point is an out-of-distribution sample for the model, so its prediction should be approached with caution. To further support these findings and assess the practical usefulness of the explanations, also keeping in mind that fidelity is not the most informative metric, we conducted a follow-up user study with expert chemists. This enables us to determine whether the generated explanations are not only mathematically accurate but also chemically meaningful and trustworthy in real-world applications.

Figure: Figure 5: Distribution of votes per explanation method across all 19 compound-based questions. Each bar represents a compound on the y-axis, and the x-axis shows the percentage of user votes received by each method. Different colors correspond to different explanation techniques. The black dashed line denotes the expected value for random guessing.
Refer to caption: x5.png

### User study

While the results from synthetic and real-world benchmarks appear promising, the evaluation metrics used often fail to fully capture the human-aligned interpretability of explanations generated by XAI techniques. These quantitative metrics measure how explanations influence model predictions but do not ensure that the highlighted atoms or substructures are chemically meaningful or align with expert understanding. Explanations that perform well on such metrics can still be useless and poorly understandable to chemistry experts. To assess whether SEAL explanations are interpretable and to evaluate if the explanations produced by different methods align with chemistry knowledge and intuition, we decided to conduct an examination across chemistry experts using various methods and randomly selected compounds.

#### Experimental design.

Our user study consisted of 19 questions featuring various randomly selected compounds. Each question included nine different explanations generated by SEAL and seven explainability methods (the same methods used in previous experiments). In addition, two random explanations were added as separate options, and all answers were shuffled. Each explanation highlights atoms that were predicted to increase the compound’s solubility in water (aqueous solubility). Participants had to choose the explanation they believed best highlights the atoms most responsible for increasing solubility. All participants were experts with at least a master’s degree in chemistry, ensuring a representative sample with relevant knowledge to evaluate factors influencing solubility. Each question included:

- •
a question saying: "Which explanation best highlights the atoms that contribute most to increasing the solubility of this compound?",
- •
molecular structure of a compound,
- •
nine explanation visualizations with important atoms marked in red, as predicted by various explainers.

The study included two randomized control conditions. In the first one, randomly selected atoms are assigned importance. The second one assigns importance to random BRICS structures. To ensure that all methods have the same chances, we fixed the number of highlighted atoms to approximately half of all atoms in the compounds. In case there were two explainers that highlight the same atoms, we replaced redundant ones with randomly generated alternatives. The participants were blinded to the source of each explanation and instructed to focus solely on the plausibility of the provided explanation.

#### Discussion.

Among the methods tested, SEAL was chosen most frequently in 14 out of 19 questions, significantly outperforming all other methods. The remaining votes were distributed as follows: Deconvolution and IntegratedGradients were chosen 5 times, and InputXGradients was selected 3 times. Other methods (Saliency, GNNExplainer, and Guided Backprop) did not win in any of the questions. The distribution of votes between methods in each question is shown in Figure [5](https://arxiv.org/html/2508.15015v1#Sx4.F5). All compounds and visualizations that were used for this user study are listed in the Appendix D.
The user study confirms that our method, SEAL, provides explanations that align more closely with human intuition and chemical understanding. It was favored over other techniques, emphasizing its ability to produce meaningful and understandable atom-level attributions.

## Conclusions

In this study, we presented SEAL, an interpretable GNN model for molecular property prediction. The method breaks down an input molecular graph into fragments that are used to calculate their contributions to the final model prediction, which is a sum of these contributions and the bias term. A regularization term is employed to regulate the exchange of information between fragments in our new SEAL-GCN architecture. Experiments on both synthetic and real-world datasets show that SEAL can accurately identify important molecular fragments. The practical usefulness of our method is further validated by a user study, where SEAL is the most frequently chosen method for explaining solubility in water.

## Acknowledgments

This work of B.Z. and T.D. was funded by the "Interpretable and Interactive Multimodal Retrieval in Drug Discovery" project. The "Interpretable and Interactive Multimodal Retrieval in Drug Discovery” project (FENG.02.02-IP.05-0040/23) is carried out within the First Team programme of the Foundation for Polish Science co-financed by the European Union under the European Funds for Smart Economy 2021-2027 (FENG). The work of S.M. was supported by the National Science Centre, Poland (grant no. 2022/47/B/ST6/03397). We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2025/018272. Some experiments were performed on servers purchased with funds from the flagship project entitled “Artificial Intelligence Computing Core Facility” from the DigiWorld Priority Research Area within the Excellence Initiative – Research University program at Jagiellonian University in Krakow.

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## Appendix A A. Training details

### Hardware and software details

#### Hardware.

All of our experiments were conducted on NVIDIA Grace Hopper GH200 (96 GB memory), NVIDIA Grace CPU 72-Core @ 3.1 GHz, 16GB RAM, CUDA toolkit 12.4.

#### Python environment.

Our experiments were carried out in Python 3.11, with Pytorch 2.5.1, Pytorch Geometric 2.6.1 for training, and RDKit (2024.9.6) for preprocessing molecules.

### Experimental details

We trained the networks with a batch size of 256, using the AdamW optimizer, early stopping after 30 epochs, and a warm-up for the first 50 epochs (10 epochs for tasks that required fewer epochs, like atom-specific tasks from the synthetic dataset). For our model, we used 10-fold cross-validation to select the optimal $\lambda$ using the Wilcoxon signed-rank test. We used MAE and AUROC as target evaluation metrics for hyperparameter searching and the Wilcoxon test. A weight decay of 0.0001 was applied to all models and tasks. Seed was set to 0 during training, while for explanation extraction and evaluation, it was set to 123. All experiment results were obtained using a 5-fold split approach. The B-XAIC benchmark proposed a fixed train-test set, and we followed this recommendation. For the datasets from TDC, we sampled five testing sets using seeds from 0 to 4, following the benchmark recommendation. The ranges of hyperparameters are shown in Table [5](https://arxiv.org/html/2508.15015v1#A1.T5).

**Table 5: Hyperparameter search space used during model optimization.**
| Hyperparameter | Values |
| --- | --- |
| Hidden dimensions | [64, 128, 256, 512, 1024] |
| GNN layers | [2, 3, 4] |
| Learning rate | [0.001, 0.003, 0.0001, 0.0003] |
| Dropout rate | [0.0, 0.1, 0.2, 0.3, 0.4, 0.5] |
| $\lambda$ | $[2,1,0.5,10^{-1},10^{-2},10^{-3},10^{-4},0]$ |

The hyperparameters selected for the synthetic datasets are listed in Table [6](https://arxiv.org/html/2508.15015v1#A2.T6), whereas those for the real-world datasets are presented in Table [7](https://arxiv.org/html/2508.15015v1#A2.T7).

### Data preprocessing

In our experiments, we standardize target values in our regression task (Solubility), but we do not perform any preprocessing in classification tasks. The atom features used for training include one-hot encoded atom types [C, N, O, F, Cl, Br, P, S, B, I, Other]; we do not use any bond features.

## Appendix B B. Extended results

The performance of SEAL with different regularization values $\lambda$ for the synthetic benchmark is presented in Table [8](https://arxiv.org/html/2508.15015v1#A2.T8). Detailed results for the subgraph explanation metric are shown in Table [9](https://arxiv.org/html/2508.15015v1#A2.T9), and for the null explanation metric in Table [10](https://arxiv.org/html/2508.15015v1#A2.T10). For real-world datasets, the evaluation of classification tasks is presented in Table [11](https://arxiv.org/html/2508.15015v1#A2.T11), while for the regression task in Table [12](https://arxiv.org/html/2508.15015v1#A2.T12). The values of the fidelity metric for these datasets are presented in Table [13](https://arxiv.org/html/2508.15015v1#A2.T13).

**Table 6: Hyperparameters found for SEAL, GAT, GCN, and GIN in synthetic dataset evaluation.**
| Model | B | P | PAINS | X | indole | rings-count | rings-max |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SEAL |  |  |  |  |  |  |  |
| Hidden dimensions | 1024 | 1024 | 512 | 1024 | 512 | 1024 | 256 |
| GNN layers | 4 | 4 | 3 | 2 | 4 | 2 | 4 |
| Learning rate | 0.0001 | 0.003 | 0.003 | 0.003 | 0.0003 | 0.003 | 0.003 |
| Dropout | 0.4 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 |
| $\lambda$ | $2$ | $2$ | 0 | $2$ | $10^{-4}$ | $10^{-3}$ | $2$ |
| GAT |  |  |  |  |  |  |  |
| Hidden dimensions | 256 | 1024 | 256 | 1024 | 256 | 1024 | 256 |
| GNN layers | 3 | 4 | 3 | 4 | 3 | 4 | 3 |
| Learning rate | 0.0003 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.003 | 0.0001 |
| Dropout | 0.4 | 0.4 | 0 | 0.4 | 0 | 0.1 | 0 |
| GCN |  |  |  |  |  |  |  |
| Hidden dimensions | 1024 | 1024 | 512 | 1024 | 512 | 512 | 1024 |
| GNN layers | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| Learning rate | 0.0001 | 0.0001 | 0.0003 | 0.0001 | 0.0003 | 0.0003 | 0.0003 |
| Dropout | 0.4 | 0.4 | 0.1 | 0.4 | 0.1 | 0.1 | 0.1 |
| GIN |  |  |  |  |  |  |  |
| Hidden dimensions | 1024 | 1024 | 1024 | 1024 | 512 | 256 | 1024 |
| GNN layers | 4 | 4 | 4 | 4 | 4 | 3 | 4 |
| Learning rate | 0.0001 | 0.0001 | 0.0003 | 0.0001 | 0.0003 | 0.001 | 0.0003 |
| Dropout | 0.4 | 0.4 | 0.1 | 0.4 | 0.1 | 0.5 | 0.1 |

**Table 7: Hyperparameters found for SEAL, GAT, GCN, and GIN in real-world dataset evaluation.**
| Model | CYP | hERG | Solubility |
| --- | --- | --- | --- |
| SEAL |  |  |  |
| Hidden dimensions | 512 | 512 | 1024 |
| GNN layers | 4 | 4 | 4 |
| Learning rate | 0.0003 | 0.0003 | 0.003 |
| Dropout | 0.1 | 0.1 | 0.1 |
| $\lambda$ | $2.0$ | $0.0001$ | $0.0001$ |
| GAT |  |  |  |
| Hidden dimensions | 256 | 256 | 128 |
| GNN layers | 3 | 3 | 3 |
| Learning rate | 0.0001 | 0.0001 | 0.0003 |
| Dropout | 0 | 0 | 0.3 |
| GCN |  |  |  |
| Hidden dimensions | 256 | 1024 | 1024 |
| GNN layers | 4 | 4 | 4 |
| Learning rate | 0.003 | 0.003 | 0.003 |
| Dropout | 0.2 | 0.1 | 0.1 |
| GIN |  |  |  |
| Hidden dimensions | 512 | 512 | 1024 |
| GNN layers | 4 | 4 | 4 |
| Learning rate | 0.0003 | 0.0003 | 0.0003 |
| Dropout | 0.1 | 0.1 | 0.1 |

**Table 8: AUROC, F1, and Accuracy score of various graph neural network architectures on the B-XAIC benchmark.**
| Model | rings-count | rings-max | X | P | B | Indole | PAINS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| AUROC $\uparrow$ |  |  |  |  |  |  |  |
| GIN | 1.00 $\pm$ 0.00 | 0.93 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
| GCN | 1.00 $\pm$ 0.00 | 0.82 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |
| GAT | 0.88 $\pm$ 0.01 | 0.75 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.97 $\pm$ 0.00 | 0.92 $\pm$ 0.01 |
| SEAL ($\lambda=2$) | 0.97 $\pm$ 0.01 | 0.99 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.95 $\pm$ 0.00 |
| SEAL ($\lambda=1$) | 0.97 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.01 |
| SEAL ($\lambda=0.5$) | 0.97 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.00 |
| SEAL ($\lambda=10^{-1}$) | 0.98 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.00 |
| SEAL ($\lambda=10^{-2}$) | 0.98 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-3}$) | 0.98 $\pm$ 0.01 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
| SEAL ($\lambda=10^{-4}$) | 0.99 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
| SEAL ($\lambda=0$) | 0.99 $\pm$ 0.00 | 0.98 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 |
| F1 Score $\uparrow$ |  |  |  |  |  |  |  |
| GIN | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |
| GCN | 0.98 $\pm$ 0.00 | 0.93 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.97 $\pm$ 0.00 | 0.93 $\pm$ 0.00 |
| GAT | 0.79 $\pm$ 0.03 | 0.92 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.92 $\pm$ 0.01 | 0.85 $\pm$ 0.01 |
| SEAL ($\lambda=2$) | 0.90 $\pm$ 0.03 | 0.85 $\pm$ 0.05 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 0.98 $\pm$ 0.00 | 0.86 $\pm$ 0.00 |
| SEAL ($\lambda=1$) | 0.86 $\pm$ 0.02 | 0.87 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 0.98 $\pm$ 0.00 | 0.86 $\pm$ 0.01 |
| SEAL ($\lambda=0.5$) | 0.87 $\pm$ 0.03 | 0.87 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 0.98 $\pm$ 0.00 | 0.86 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-1}$) | 0.88 $\pm$ 0.04 | 0.89 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 0.99 $\pm$ 0.01 | 0.98 $\pm$ 0.00 | 0.86 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-2}$) | 0.92 $\pm$ 0.01 | 0.90 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.98 $\pm$ 0.02 | 0.99 $\pm$ 0.00 | 0.86 $\pm$ 0.02 |
| SEAL ($\lambda=10^{-3}$) | 0.93 $\pm$ 0.02 | 0.91 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.01 | 0.99 $\pm$ 0.00 | 0.93 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-4}$) | 0.94 $\pm$ 0.01 | 0.91 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.98 $\pm$ 0.01 | 0.99 $\pm$ 0.00 | 0.95 $\pm$ 0.00 |
| SEAL ($\lambda=0$) | 0.93 $\pm$ 0.01 | 0.88 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.96 $\pm$ 0.01 |
| Accuracy $\uparrow$ |  |  |  |  |  |  |  |
| GIN | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |
| GCN | 0.98 $\pm$ 0.00 | 0.93 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.97 $\pm$ 0.00 | 0.93 $\pm$ 0.00 |
| GAT | 0.81 $\pm$ 0.02 | 0.91 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.92 $\pm$ 0.01 | 0.86 $\pm$ 0.01 |
| SEAL ($\lambda=2$) | 0.93 $\pm$ 0.02 | 0.98 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.91 $\pm$ 0.00 |
| SEAL ($\lambda=1$) | 0.91 $\pm$ 0.02 | 0.98 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.91 $\pm$ 0.01 |
| SEAL ($\lambda=0.5$) | 0.92 $\pm$ 0.02 | 0.98 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.91 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-1}$) | 0.92 $\pm$ 0.03 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.91 $\pm$ 0.00 |
| SEAL ($\lambda=10^{-2}$) | 0.95 $\pm$ 0.01 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.91 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-3}$) | 0.96 $\pm$ 0.01 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.95 $\pm$ 0.00 |
| SEAL ($\lambda=10^{-4}$) | 0.97 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |
| SEAL ($\lambda=0$) | 0.96 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.97 $\pm$ 0.00 |

**Table 9: Performance of various model explanations on the B-XAIC benchmark. The subgraph explanation (SE) metric is employed for positive examples containing the relevant pattern.**
| Model | rings-count | rings-max | X | P | B | Indole | PAINS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| SE $\uparrow$ |  |  |  |  |  |  |  |
| Deconvolution | 0.55 $\pm$ 0.24 | 0.38 $\pm$ 0.18 | 0.07 $\pm$ 0.00 | 0.90 $\pm$ 0.00 | 0.72 $\pm$ 0.01 | 0.36 $\pm$ 0.21 | 0.33 $\pm$ 0.01 |
| GuidedBackprop | 0.69 $\pm$ 0.05 | 0.67 $\pm$ 0.02 | 0.94 $\pm$ 0.01 | 0.85 $\pm$ 0.11 | 1.00 $\pm$ 0.00 | 0.85 $\pm$ 0.03 | 0.78 $\pm$ 0.02 |
| IntegratedGradients | 0.36 $\pm$ 0.00 | 0.34 $\pm$ 0.03 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.84 $\pm$ 0.06 | 0.76 $\pm$ 0.02 |
| Saliency | 0.51 $\pm$ 0.04 | 0.55 $\pm$ 0.03 | 0.92 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.87 $\pm$ 0.02 | 0.81 $\pm$ 0.01 |
| InputXGradient | 0.49 $\pm$ 0.03 | 0.47 $\pm$ 0.05 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 1.00 $\pm$ 0.00 | 0.74 $\pm$ 0.05 | 0.54 $\pm$ 0.03 |
| GNNExplainer | 0.49 $\pm$ 0.01 | 0.50 $\pm$ 0.00 | 0.50 $\pm$ 0.00 | 0.51 $\pm$ 0.01 | 0.53 $\pm$ 0.05 | 0.53 $\pm$ 0.03 | 0.54 $\pm$ 0.06 |
| SEAL ($\lambda=2$) | 1.00 $\pm$ 0.00 | 0.34 $\pm$ 0.03 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.77 $\pm$ 0.00 |
| SEAL ($\lambda=1$) | 1.00 $\pm$ 0.00 | 0.35 $\pm$ 0.01 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.77 $\pm$ 0.01 |
| SEAL ($\lambda=0.5$) | 1.00 $\pm$ 0.00 | 0.35 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.78 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-1}$) | 1.00 $\pm$ 0.00 | 0.33 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.78 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-2}$) | 1.00 $\pm$ 0.00 | 0.34 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.99 $\pm$ 0.00 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.78 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-3}$) | 0.98 $\pm$ 0.01 | 0.29 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.04 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.80 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-4}$) | 0.96 $\pm$ 0.01 | 0.25 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.96 $\pm$ 0.04 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.83 $\pm$ 0.02 |
| SEAL ($\lambda=0$) | 0.87 $\pm$ 0.04 | 0.23 $\pm$ 0.02 | 1.00 $\pm$ 0.00 | 0.91 $\pm$ 0.04 | 0.88 $\pm$ 0.01 | 0.96 $\pm$ 0.00 | 0.83 $\pm$ 0.01 |

**Table 10: Performance of various model explanations on the B-XAIC benchmark. The null explanation (NE) metric is employed for negative examples, checking uniform distribution.**
| Model | rings-count | rings-max | X | P | B | Indole | PAINS |
| --- | --- | --- | --- | --- | --- | --- | --- |
| NE $\uparrow$ |  |  |  |  |  |  |  |
| Deconvolution | 0.56 $\pm$ 0.06 | 0.46 $\pm$ 0.07 | 0.84 $\pm$ 0.01 | 0.84 $\pm$ 0.01 | 0.82 $\pm$ 0.01 | 0.80 $\pm$ 0.01 | 0.81 $\pm$ 0.01 |
| GuidedBackprop | 0.32 $\pm$ 0.08 | 0.10 $\pm$ 0.06 | 0.35 $\pm$ 0.10 | 0.51 $\pm$ 0.04 | 0.45 $\pm$ 0.03 | 0.33 $\pm$ 0.05 | 0.28 $\pm$ 0.02 |
| IntegratedGradients | 0.81 $\pm$ 0.08 | 0.55 $\pm$ 0.20 | 0.19 $\pm$ 0.10 | 0.36 $\pm$ 0.37 | 0.22 $\pm$ 0.07 | 0.31 $\pm$ 0.13 | 0.42 $\pm$ 0.25 |
| Saliency | 0.48 $\pm$ 0.05 | 0.36 $\pm$ 0.08 | 0.37 $\pm$ 0.05 | 0.55 $\pm$ 0.03 | 0.50 $\pm$ 0.08 | 0.42 $\pm$ 0.03 | 0.35 $\pm$ 0.04 |
| InputXGradient | 0.53 $\pm$ 0.05 | 0.49 $\pm$ 0.14 | 0.23 $\pm$ 0.07 | 0.69 $\pm$ 0.16 | 0.39 $\pm$ 0.14 | 0.49 $\pm$ 0.01 | 0.40 $\pm$ 0.04 |
| GNNExplainer | 0.80 $\pm$ 0.07 | 0.80 $\pm$ 0.05 | 0.65 $\pm$ 0.02 | 0.67 $\pm$ 0.01 | 0.67 $\pm$ 0.00 | 0.73 $\pm$ 0.09 | 0.55 $\pm$ 0.26 |
| SEAL ($\lambda=2$) | 0.44 $\pm$ 0.05 | 0.47 $\pm$ 0.09 | 0.32 $\pm$ 0.09 | 0.16 $\pm$ 0.06 | 0.44 $\pm$ 0.02 | 0.64 $\pm$ 0.04 | 0.61 $\pm$ 0.01 |
| SEAL ($\lambda=1$) | 0.42 $\pm$ 0.05 | 0.47 $\pm$ 0.06 | 0.36 $\pm$ 0.09 | 0.18 $\pm$ 0.18 | 0.45 $\pm$ 0.03 | 0.63 $\pm$ 0.03 | 0.62 $\pm$ 0.01 |
| SEAL ($\lambda=0.5$) | 0.45 $\pm$ 0.04 | 0.52 $\pm$ 0.03 | 0.38 $\pm$ 0.08 | 0.21 $\pm$ 0.16 | 0.46 $\pm$ 0.05 | 0.63 $\pm$ 0.04 | 0.63 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-1}$) | 0.53 $\pm$ 0.06 | 0.44 $\pm$ 0.10 | 0.32 $\pm$ 0.05 | 0.22 $\pm$ 0.07 | 0.48 $\pm$ 0.05 | 0.68 $\pm$ 0.03 | 0.63 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-2}$) | 0.48 $\pm$ 0.04 | 0.48 $\pm$ 0.08 | 0.51 $\pm$ 0.05 | 0.09 $\pm$ 0.05 | 0.49 $\pm$ 0.04 | 0.63 $\pm$ 0.02 | 0.63 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-3}$) | 0.71 $\pm$ 0.10 | 0.56 $\pm$ 0.06 | 0.36 $\pm$ 0.07 | 0.10 $\pm$ 0.01 | 0.43 $\pm$ 0.07 | 0.69 $\pm$ 0.05 | 0.65 $\pm$ 0.01 |
| SEAL ($\lambda=10^{-4}$) | 0.70 $\pm$ 0.05 | 0.62 $\pm$ 0.07 | 0.33 $\pm$ 0.09 | 0.10 $\pm$ 0.02 | 0.26 $\pm$ 0.06 | 0.65 $\pm$ 0.05 | 0.70 $\pm$ 0.03 |
| SEAL ($\lambda=0$) | 0.59 $\pm$ 0.06 | 0.62 $\pm$ 0.07 | 0.60 $\pm$ 0.07 | 0.16 $\pm$ 0.08 | 0.38 $\pm$ 0.12 | 0.57 $\pm$ 0.05 | 0.68 $\pm$ 0.01 |

**Table 11: Comparison of model performance on real-world datasets (hERG and CYP2C9).**
|  | Model | AUROC $\uparrow$ | F1 $\uparrow$ | Accuracy $\uparrow$ |
| --- | --- | --- | --- | --- |
| hERG | GIN | 0.86 $\pm$ 0.01 | 0.78 $\pm$ 0.01 | 0.78 $\pm$ 0.01 |
| GAT | 0.70 $\pm$ 0.01 | 0.64 $\pm$ 0.01 | 0.65 $\pm$ 0.01 |  |
| GCN | 0.81 $\pm$ 0.03 | 0.73 $\pm$ 0.02 | 0.73 $\pm$ 0.02 |  |
| SEAL ($\lambda=2$) | 0.81 $\pm$ 0.01 | 0.71 $\pm$ 0.03 | 0.74 $\pm$ 0.01 |  |
| SEAL ($\lambda=1$) | 0.81 $\pm$ 0.01 | 0.73 $\pm$ 0.02 | 0.75 $\pm$ 0.01 |  |
| SEAL ($\lambda=0.5$) | 0.81 $\pm$ 0.01 | 0.73 $\pm$ 0.02 | 0.75 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-1}$) | 0.79 $\pm$ 0.01 | 0.70 $\pm$ 0.02 | 0.73 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-2}$) | 0.80 $\pm$ 0.00 | 0.70 $\pm$ 0.02 | 0.73 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-3}$) | 0.81 $\pm$ 0.03 | 0.71 $\pm$ 0.03 | 0.74 $\pm$ 0.02 |  |
| SEAL ($\lambda=10^{-4}$) | 0.85 $\pm$ 0.01 | 0.76 $\pm$ 0.01 | 0.77 $\pm$ 0.00 |  |
| SEAL ($\lambda=0$) | 0.85 $\pm$ 0.01 | 0.76 $\pm$ 0.01 | 0.78 $\pm$ 0.01 |  |
| CYP2C9 | GIN | 0.86 $\pm$ 0.01 | 0.79 $\pm$ 0.01 | 0.79 $\pm$ 0.01 |
| GAT | 0.68 $\pm$ 0.01 | 0.67 $\pm$ 0.01 | 0.69 $\pm$ 0.01 |  |
| GCN | 0.84 $\pm$ 0.01 | 0.78 $\pm$ 0.01 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=2$) | 0.81 $\pm$ 0.01 | 0.65 $\pm$ 0.02 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=1$) | 0.81 $\pm$ 0.01 | 0.64 $\pm$ 0.03 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=0.5$) | 0.81 $\pm$ 0.00 | 0.64 $\pm$ 0.02 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-1}$) | 0.79 $\pm$ 0.01 | 0.59 $\pm$ 0.03 | 0.76 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-2}$) | 0.79 $\pm$ 0.01 | 0.58 $\pm$ 0.03 | 0.76 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-3}$) | 0.83 $\pm$ 0.01 | 0.64 $\pm$ 0.03 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-4}$) | 0.83 $\pm$ 0.00 | 0.64 $\pm$ 0.04 | 0.78 $\pm$ 0.01 |  |
| SEAL ($\lambda=0$) | 0.83 $\pm$ 0.01 | 0.64 $\pm$ 0.03 | 0.79 $\pm$ 0.01 |  |

**Table 12: Comparison of model performance on real-world datasets (Solubility).**
|  | Model | MAE $\downarrow$ | RMSE $\downarrow$ |
| --- | --- | --- | --- |
| Solubility | GIN | 0.41 $\pm$ 0.01 | 0.60 $\pm$ 0.02 |
| GAT | 0.57 $\pm$ 0.01 | 0.75 $\pm$ 0.03 |  |
| GCN | 0.49 $\pm$ 0.02 | 0.67 $\pm$ 0.03 |  |
| SEAL ($\lambda=2$) | 0.54 $\pm$ 0.04 | 0.73 $\pm$ 0.05 |  |
| SEAL ($\lambda=1$) | 0.53 $\pm$ 0.05 | 0.73 $\pm$ 0.05 |  |
| SEAL ($\lambda=0.5$) | 0.54 $\pm$ 0.05 | 0.73 $\pm$ 0.05 |  |
| SEAL ($\lambda=10^{-1}$) | 0.54 $\pm$ 0.05 | 0.73 $\pm$ 0.06 |  |
| SEAL ($\lambda=10^{-2}$) | 0.53 $\pm$ 0.05 | 0.73 $\pm$ 0.04 |  |
| SEAL ($\lambda=10^{-3}$) | 0.48 $\pm$ 0.01 | 0.68 $\pm$ 0.04 |  |
| SEAL ($\lambda=10^{-4}$) | 0.47 $\pm$ 0.01 | 0.66 $\pm$ 0.04 |  |
| SEAL ($\lambda=0$) | 0.45 $\pm$ 0.01 | 0.66 $\pm$ 0.03 |  |

**Table 13: Performance of model explanations on real-world datasets (hERG, CYP2C9 and Solubility). Explanations are evaluated using Fidelity metrics at 10%, 20%, and 30% masking thresholds, representing the proportion of most important atoms (nodes) either removed or retained during the evaluation.**
|  | Model | $\text{Fidelity}_{10}+\uparrow$ | $\text{Fidelity}_{10}-\downarrow$ | $\text{Fidelity}_{20}+\uparrow$ | $\text{Fidelity}_{20}-\downarrow$ | $\text{Fidelity}_{30}+\uparrow$ | $\text{Fidelity}_{30}-\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| hERG | Deconvolution | 0.33 $\pm$ 0.06 | 0.49 $\pm$ 0.02 | 0.45 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.48 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |
| GuidedBackprop | 0.39 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.44 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.47 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |  |
| IntegratedGradients | 0.47 $\pm$ 0.07 | 0.49 $\pm$ 0.02 | 0.54 $\pm$ 0.10 | 0.47 $\pm$ 0.03 | 0.58 $\pm$ 0.19 | 0.41 $\pm$ 0.16 |  |
| Saliency | 0.35 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.42 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.46 $\pm$ 0.02 | 0.49 $\pm$ 0.02 |  |
| InputXGradient | 0.33 $\pm$ 0.05 | 0.49 $\pm$ 0.02 | 0.40 $\pm$ 0.04 | 0.49 $\pm$ 0.02 | 0.44 $\pm$ 0.04 | 0.50 $\pm$ 0.02 |  |
| GNNExplainer | 0.42 $\pm$ 0.15 | 0.49 $\pm$ 0.02 | 0.46 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.50 $\pm$ 0.05 | 0.47 $\pm$ 0.04 |  |
| SEAL ($\lambda=2$) | 0.57 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.66 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.76 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=1$) | 0.57 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.65 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.75 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=0.5$) | 0.57 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.66 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.76 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=10^{-1}$) | 0.59 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.68 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.77 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=10^{-2}$) | 0.59 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.68 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.78 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=10^{-3}$) | 0.63 $\pm$ 0.03 | 0.02 $\pm$ 0.01 | 0.72 $\pm$ 0.03 | 0.01 $\pm$ 0.01 | 0.80 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-4}$) | 0.63 $\pm$ 0.01 | 0.09 $\pm$ 0.03 | 0.71 $\pm$ 0.01 | 0.07 $\pm$ 0.02 | 0.78 $\pm$ 0.01 | 0.05 $\pm$ 0.02 |  |
| SEAL ($\lambda=0$) | 0.67 $\pm$ 0.02 | 0.15 $\pm$ 0.02 | 0.74 $\pm$ 0.02 | 0.15 $\pm$ 0.01 | 0.78 $\pm$ 0.03 | 0.14 $\pm$ 0.01 |  |
| CYP2C9 | Deconvolution | 0.24 $\pm$ 0.02 | 0.40 $\pm$ 0.09 | 0.31 $\pm$ 0.03 | 0.37 $\pm$ 0.03 | 0.34 $\pm$ 0.03 | 0.37 $\pm$ 0.03 |
| GuidedBackprop | 0.33 $\pm$ 0.03 | 0.40 $\pm$ 0.10 | 0.34 $\pm$ 0.04 | 0.39 $\pm$ 0.07 | 0.35 $\pm$ 0.03 | 0.38 $\pm$ 0.05 |  |
| IntegratedGradients | 0.45 $\pm$ 0.08 | 0.29 $\pm$ 0.16 | 0.59 $\pm$ 0.14 | 0.29 $\pm$ 0.16 | 0.68 $\pm$ 0.21 | 0.29 $\pm$ 0.16 |  |
| Saliency | 0.32 $\pm$ 0.04 | 0.38 $\pm$ 0.06 | 0.37 $\pm$ 0.03 | 0.36 $\pm$ 0.03 | 0.38 $\pm$ 0.04 | 0.36 $\pm$ 0.03 |  |
| InputXGradient | 0.31 $\pm$ 0.02 | 0.41 $\pm$ 0.12 | 0.34 $\pm$ 0.02 | 0.41 $\pm$ 0.11 | 0.35 $\pm$ 0.03 | 0.41 $\pm$ 0.11 |  |
| GNNExplainer | 0.30 $\pm$ 0.02 | 0.37 $\pm$ 0.03 | 0.43 $\pm$ 0.10 | 0.43 $\pm$ 0.16 | 0.42 $\pm$ 0.08 | 0.33 $\pm$ 0.08 |  |
| SEAL ($\lambda=2$) | 0.52 $\pm$ 0.02 | 0.04 $\pm$ 0.05 | 0.57 $\pm$ 0.03 | 0.03 $\pm$ 0.04 | 0.66 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL ($\lambda=1$) | 0.50 $\pm$ 0.02 | 0.05 $\pm$ 0.06 | 0.55 $\pm$ 0.02 | 0.04 $\pm$ 0.05 | 0.64 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL ($\lambda=0.5$) | 0.50 $\pm$ 0.02 | 0.05 $\pm$ 0.04 | 0.56 $\pm$ 0.01 | 0.04 $\pm$ 0.03 | 0.64 $\pm$ 0.02 | 0.01 $\pm$ 0.01 |  |
| SEAL ($\lambda=10^{-1}$) | 0.57 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.63 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.72 $\pm$ 0.02 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=10^{-2}$) | 0.53 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.60 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.69 $\pm$ 0.02 | 0.00 $\pm$ 0.00 |  |
| SEAL ($\lambda=10^{-3}$) | 0.53 $\pm$ 0.02 | 0.06 $\pm$ 0.04 | 0.58 $\pm$ 0.02 | 0.05 $\pm$ 0.04 | 0.65 $\pm$ 0.02 | 0.04 $\pm$ 0.03 |  |
| SEAL ($\lambda=10^{-4}$) | 0.52 $\pm$ 0.02 | 0.13 $\pm$ 0.05 | 0.58 $\pm$ 0.02 | 0.11 $\pm$ 0.03 | 0.63 $\pm$ 0.03 | 0.10 $\pm$ 0.04 |  |
| SEAL ($\lambda=0$) | 0.52 $\pm$ 0.00 | 0.11 $\pm$ 0.02 | 0.59 $\pm$ 0.01 | 0.09 $\pm$ 0.01 | 0.65 $\pm$ 0.01 | 0.07 $\pm$ 0.01 |  |
| Solubility | Deconvolution | 0.41 $\pm$ 0.08 | 5.24 $\pm$ 1.60 | 0.95 $\pm$ 0.23 | 5.02 $\pm$ 1.52 | 1.54 $\pm$ 0.42 | 4.78 $\pm$ 1.43 |
| GuidedBackprop | 1.11 $\pm$ 0.17 | 5.08 $\pm$ 1.67 | 2.59 $\pm$ 0.54 | 4.70 $\pm$ 1.65 | 3.37 $\pm$ 0.94 | 4.31 $\pm$ 1.56 |  |
| IntegratedGradients | 0.42 $\pm$ 0.05 | 5.47 $\pm$ 1.84 | 0.84 $\pm$ 0.20 | 5.45 $\pm$ 1.96 | 1.30 $\pm$ 0.42 | 5.33 $\pm$ 1.98 |  |
| Saliency | 0.95 $\pm$ 0.15 | 5.18 $\pm$ 1.71 | 1.90 $\pm$ 0.46 | 4.94 $\pm$ 1.74 | 2.56 $\pm$ 0.74 | 4.64 $\pm$ 1.66 |  |
| InputXGradient | 0.73 $\pm$ 0.11 | 5.13 $\pm$ 1.62 | 1.71 $\pm$ 0.36 | 4.81 $\pm$ 1.56 | 2.44 $\pm$ 0.61 | 4.45 $\pm$ 1.45 |  |
| GNNExplainer | 0.71 $\pm$ 0.13 | 5.12 $\pm$ 1.60 | 1.81 $\pm$ 0.49 | 4.80 $\pm$ 1.53 | 2.82 $\pm$ 0.86 | 4.47 $\pm$ 1.43 |  |
| SEAL ($\lambda=2$) | 1.12 $\pm$ 0.25 | 1.04 $\pm$ 0.42 | 1.20 $\pm$ 0.28 | 0.96 $\pm$ 0.38 | 1.34 $\pm$ 0.32 | 0.84 $\pm$ 0.33 |  |
| SEAL ($\lambda=1$) | 1.04 $\pm$ 0.31 | 0.83 $\pm$ 0.39 | 1.11 $\pm$ 0.34 | 0.78 $\pm$ 0.36 | 1.22 $\pm$ 0.38 | 0.69 $\pm$ 0.31 |  |
| SEAL ($\lambda=0.5$) | 1.08 $\pm$ 0.31 | 0.89 $\pm$ 0.41 | 1.15 $\pm$ 0.33 | 0.83 $\pm$ 0.38 | 1.26 $\pm$ 0.37 | 0.74 $\pm$ 0.34 |  |
| SEAL ($\lambda=10^{-1}$) | 0.77 $\pm$ 0.20 | 0.67 $\pm$ 0.29 | 0.83 $\pm$ 0.23 | 0.62 $\pm$ 0.26 | 0.92 $\pm$ 0.27 | 0.55 $\pm$ 0.23 |  |
| SEAL ($\lambda=10^{-2}$) | 0.64 $\pm$ 0.12 | 0.50 $\pm$ 0.13 | 0.68 $\pm$ 0.13 | 0.49 $\pm$ 0.13 | 0.73 $\pm$ 0.14 | 0.46 $\pm$ 0.13 |  |
| SEAL ($\lambda=10^{-3}$) | 1.24 $\pm$ 0.20 | 1.26 $\pm$ 0.33 | 1.37 $\pm$ 0.21 | 1.14 $\pm$ 0.30 | 1.55 $\pm$ 0.25 | 0.98 $\pm$ 0.25 |  |
| SEAL ($\lambda=10^{-4}$) | 0.78 $\pm$ 0.14 | 0.64 $\pm$ 0.16 | 0.84 $\pm$ 0.15 | 0.58 $\pm$ 0.13 | 0.91 $\pm$ 0.17 | 0.52 $\pm$ 0.09 |  |
| SEAL ($\lambda=0$) | 0.49 $\pm$ 0.03 | 0.55 $\pm$ 0.07 | 0.54 $\pm$ 0.04 | 0.53 $\pm$ 0.08 | 0.58 $\pm$ 0.05 | 0.48 $\pm$ 0.07 |  |

## Appendix C C. Ablation study on masking strategy

In our fidelity evaluation, we analyze how masking different types of contributions affects the model’s interpretability. For each fidelity type (positive, negative), we evaluate the impact of masking the top $10\%$, $20\%$, and $30\%$ of nodes or contributions. This allows us to compare how well explanations identify the most influential substructures without exceeding a predefined threshold.

Unlike standard explainers that only operate on node masks, our model allows for masking specific contribution scores directly at the level of the model’s architecture by setting $c_{i}=0$ for a given fragment. However, a challenge with this approach is that sometimes, even at the beginning of the ranking, a single large important substructure can surpass the 10% node threshold. Consequently, the masking process might include more atoms than planned, violating the target percentage and possibly impacting the fidelity assessment. Also, we need to carefully select the masking strategy: whether to focus on absolute contributions or to selectively mask only positive or negative influences. However, the optimal strategy may vary depending on the task and model sensitivity, whether one chooses to use or omit masking of contributions, and whether masking is guided by absolute, positive only, or negative only importance scores. The results comparing these masking strategies are reported in Table [14](https://arxiv.org/html/2508.15015v1#A4.T14) for hERG, in Table [15](https://arxiv.org/html/2508.15015v1#A4.T15) for CYP2C9, and in Table [16](https://arxiv.org/html/2508.15015v1#A4.T16) for Solubility. These results contain the following naming convention:

- •
mask-abs: zeroing features, mask contributions - based on maximum absolute value,
- •
mask: zeroing features, mask contributions - based on maximum or minimum value,
- •
abs: zeroing features - based on maximum absolute value,
- •
zero: zeroing features - based on maximum or minimum value.

## Appendix D D. User study

All of the molecules that were included in our user study are presented in Figures [6](https://arxiv.org/html/2508.15015v1#A4.F6)-[12](https://arxiv.org/html/2508.15015v1#A4.F12). Each explanation is annotated with the name of the method that produced this explanation (the names were not included in the survey, and the order of the explanations was randomized). Some methods resulted in the same explanation, which is why some of the figures have multiple method names. In these situations, we had to generate more random explanations to maintain a consistent number of options across questions. Methods that took part in these experiments: SEAL, GuidedBackprop, GNNExplainer, InputXGradient, Saliency, Deconvolution, IntegratedGradients, two Random methods, the first where we sample from nodes, the second where we sample for substructures generated by BRICS.

**Table 14: Model explanations performance using different type of masking strategy in SEAL architecture for hERG dataset. Evaluating using Fidelity metrics at 10%, 20%, and 30% masking thresholds.**
|  | Model | $\text{Fidelity}_{10}+\uparrow$ | $\text{Fidelity}_{10}-\downarrow$ | $\text{Fidelity}_{20}+\uparrow$ | $\text{Fidelity}_{20}-\downarrow$ | $\text{Fidelity}_{30}+\uparrow$ | $\text{Fidelity}_{30}-\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| hERG |  |  |  |  |  |  |  |
| $\lambda=2$ | SEAL-mask-abs | 0.36 $\pm$ 0.01 | 0.18 $\pm$ 0.00 | 0.37 $\pm$ 0.02 | 0.18 $\pm$ 0.01 | 0.38 $\pm$ 0.02 | 0.15 $\pm$ 0.01 |
| SEAL-mask | 0.57 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.66 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.76 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.49 $\pm$ 0.04 | 0.46 $\pm$ 0.04 | 0.49 $\pm$ 0.04 | 0.46 $\pm$ 0.04 | 0.49 $\pm$ 0.04 | 0.44 $\pm$ 0.05 |  |
| SEAL-zero | 0.59 $\pm$ 0.05 | 0.45 $\pm$ 0.04 | 0.58 $\pm$ 0.10 | 0.43 $\pm$ 0.06 | 0.58 $\pm$ 0.12 | 0.40 $\pm$ 0.09 |  |
| $\lambda=1$ | SEAL-mask-abs | 0.36 $\pm$ 0.01 | 0.18 $\pm$ 0.01 | 0.37 $\pm$ 0.02 | 0.18 $\pm$ 0.01 | 0.39 $\pm$ 0.02 | 0.15 $\pm$ 0.01 |
| SEAL-mask | 0.57 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.65 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.75 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.54 $\pm$ 0.04 | 0.48 $\pm$ 0.10 | 0.55 $\pm$ 0.03 | 0.47 $\pm$ 0.11 | 0.55 $\pm$ 0.03 | 0.45 $\pm$ 0.12 |  |
| SEAL-zero | 0.64 $\pm$ 0.03 | 0.44 $\pm$ 0.15 | 0.66 $\pm$ 0.06 | 0.41 $\pm$ 0.18 | 0.67 $\pm$ 0.10 | 0.38 $\pm$ 0.20 |  |
| $\lambda=0.5$ | SEAL-mask-abs | 0.37 $\pm$ 0.01 | 0.18 $\pm$ 0.01 | 0.37 $\pm$ 0.01 | 0.17 $\pm$ 0.00 | 0.39 $\pm$ 0.02 | 0.15 $\pm$ 0.01 |
| SEAL-mask | 0.57 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.66 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.76 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.52 $\pm$ 0.04 | 0.49 $\pm$ 0.05 | 0.52 $\pm$ 0.04 | 0.48 $\pm$ 0.06 | 0.52 $\pm$ 0.04 | 0.47 $\pm$ 0.07 |  |
| SEAL-zero | 0.62 $\pm$ 0.03 | 0.47 $\pm$ 0.08 | 0.62 $\pm$ 0.07 | 0.45 $\pm$ 0.10 | 0.61 $\pm$ 0.09 | 0.42 $\pm$ 0.11 |  |
| $\lambda=10^{-1}$ | SEAL-mask-abs | 0.37 $\pm$ 0.01 | 0.20 $\pm$ 0.02 | 0.38 $\pm$ 0.01 | 0.19 $\pm$ 0.01 | 0.40 $\pm$ 0.01 | 0.16 $\pm$ 0.01 |
| SEAL-mask | 0.59 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.68 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.77 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.50 $\pm$ 0.03 | 0.50 $\pm$ 0.03 | 0.51 $\pm$ 0.02 | 0.49 $\pm$ 0.04 | 0.51 $\pm$ 0.02 | 0.48 $\pm$ 0.04 |  |
| SEAL-zero | 0.59 $\pm$ 0.05 | 0.49 $\pm$ 0.04 | 0.60 $\pm$ 0.07 | 0.48 $\pm$ 0.05 | 0.58 $\pm$ 0.07 | 0.45 $\pm$ 0.07 |  |
| $\lambda=10^{-2}$ | SEAL-mask-abs | 0.37 $\pm$ 0.01 | 0.19 $\pm$ 0.02 | 0.38 $\pm$ 0.01 | 0.18 $\pm$ 0.01 | 0.39 $\pm$ 0.01 | 0.15 $\pm$ 0.01 |
| SEAL-mask | 0.59 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.68 $\pm$ 0.01 | 0.00 $\pm$ 0.00 | 0.78 $\pm$ 0.01 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.47 $\pm$ 0.03 | 0.48 $\pm$ 0.03 | 0.48 $\pm$ 0.02 | 0.47 $\pm$ 0.03 | 0.49 $\pm$ 0.02 | 0.46 $\pm$ 0.04 |  |
| SEAL-zero | 0.59 $\pm$ 0.03 | 0.47 $\pm$ 0.05 | 0.58 $\pm$ 0.07 | 0.45 $\pm$ 0.07 | 0.56 $\pm$ 0.09 | 0.43 $\pm$ 0.09 |  |
| $\lambda=10^{-3}$ | SEAL-mask-abs | 0.38 $\pm$ 0.03 | 0.24 $\pm$ 0.01 | 0.39 $\pm$ 0.02 | 0.22 $\pm$ 0.01 | 0.41 $\pm$ 0.02 | 0.20 $\pm$ 0.02 |
| SEAL-mask | 0.63 $\pm$ 0.03 | 0.02 $\pm$ 0.01 | 0.72 $\pm$ 0.03 | 0.01 $\pm$ 0.01 | 0.80 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL-abs | 0.43 $\pm$ 0.04 | 0.48 $\pm$ 0.01 | 0.45 $\pm$ 0.04 | 0.48 $\pm$ 0.01 | 0.47 $\pm$ 0.03 | 0.47 $\pm$ 0.02 |  |
| SEAL-zero | 0.58 $\pm$ 0.04 | 0.48 $\pm$ 0.01 | 0.59 $\pm$ 0.05 | 0.47 $\pm$ 0.02 | 0.57 $\pm$ 0.05 | 0.44 $\pm$ 0.04 |  |
| $\lambda=10^{-4}$ | SEAL-mask-abs | 0.42 $\pm$ 0.02 | 0.28 $\pm$ 0.04 | 0.44 $\pm$ 0.02 | 0.28 $\pm$ 0.04 | 0.46 $\pm$ 0.02 | 0.27 $\pm$ 0.03 |
| SEAL-mask | 0.63 $\pm$ 0.01 | 0.09 $\pm$ 0.03 | 0.71 $\pm$ 0.01 | 0.07 $\pm$ 0.02 | 0.78 $\pm$ 0.01 | 0.05 $\pm$ 0.02 |  |
| SEAL-abs | 0.46 $\pm$ 0.02 | 0.49 $\pm$ 0.01 | 0.49 $\pm$ 0.01 | 0.49 $\pm$ 0.01 | 0.50 $\pm$ 0.02 | 0.49 $\pm$ 0.01 |  |
| SEAL-zero | 0.56 $\pm$ 0.05 | 0.49 $\pm$ 0.01 | 0.57 $\pm$ 0.05 | 0.48 $\pm$ 0.01 | 0.55 $\pm$ 0.06 | 0.47 $\pm$ 0.02 |  |
| $\lambda=0$ | SEAL-mask-abs | 0.46 $\pm$ 0.02 | 0.32 $\pm$ 0.02 | 0.47 $\pm$ 0.01 | 0.32 $\pm$ 0.02 | 0.48 $\pm$ 0.01 | 0.31 $\pm$ 0.02 |
| SEAL-mask | 0.67 $\pm$ 0.02 | 0.15 $\pm$ 0.02 | 0.74 $\pm$ 0.02 | 0.15 $\pm$ 0.01 | 0.78 $\pm$ 0.03 | 0.14 $\pm$ 0.01 |  |
| SEAL-abs | 0.43 $\pm$ 0.02 | 0.49 $\pm$ 0.02 | 0.48 $\pm$ 0.02 | 0.49 $\pm$ 0.02 | 0.49 $\pm$ 0.02 | 0.48 $\pm$ 0.02 |  |
| SEAL-zero | 0.52 $\pm$ 0.02 | 0.49 $\pm$ 0.02 | 0.53 $\pm$ 0.02 | 0.48 $\pm$ 0.02 | 0.51 $\pm$ 0.02 | 0.48 $\pm$ 0.02 |  |

**Table 15: Model explanations performance using different type of masking strategy in SEAL architecture for CYP2C9 dataset. Evaluating using Fidelity metrics at 10%, 20%, and 30% masking thresholds.**
|  | Model | $\text{Fidelity}_{10}+\uparrow$ | $\text{Fidelity}_{10}-\downarrow$ | $\text{Fidelity}_{20}+\uparrow$ | $\text{Fidelity}_{20}-\downarrow$ | $\text{Fidelity}_{30}+\uparrow$ | $\text{Fidelity}_{30}-\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CYP2C9 |  |  |  |  |  |  |  |
| $\lambda=2$ | SEAL-mask-abs | 0.36 $\pm$ 0.01 | 0.19 $\pm$ 0.01 | 0.37 $\pm$ 0.01 | 0.18 $\pm$ 0.01 | 0.39 $\pm$ 0.02 | 0.15 $\pm$ 0.02 |
| SEAL-mask | 0.52 $\pm$ 0.02 | 0.04 $\pm$ 0.05 | 0.57 $\pm$ 0.03 | 0.03 $\pm$ 0.04 | 0.66 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL-abs | 0.41 $\pm$ 0.11 | 0.40 $\pm$ 0.11 | 0.42 $\pm$ 0.11 | 0.39 $\pm$ 0.11 | 0.45 $\pm$ 0.10 | 0.38 $\pm$ 0.12 |  |
| SEAL-zero | 0.49 $\pm$ 0.08 | 0.37 $\pm$ 0.15 | 0.51 $\pm$ 0.08 | 0.35 $\pm$ 0.15 | 0.54 $\pm$ 0.07 | 0.32 $\pm$ 0.16 |  |
| $\lambda=1$ | SEAL-mask-abs | 0.34 $\pm$ 0.02 | 0.20 $\pm$ 0.02 | 0.35 $\pm$ 0.01 | 0.19 $\pm$ 0.02 | 0.36 $\pm$ 0.02 | 0.16 $\pm$ 0.02 |
| SEAL-mask | 0.50 $\pm$ 0.02 | 0.05 $\pm$ 0.06 | 0.55 $\pm$ 0.02 | 0.04 $\pm$ 0.05 | 0.64 $\pm$ 0.03 | 0.01 $\pm$ 0.01 |  |
| SEAL-abs | 0.40 $\pm$ 0.13 | 0.40 $\pm$ 0.12 | 0.41 $\pm$ 0.13 | 0.40 $\pm$ 0.12 | 0.42 $\pm$ 0.13 | 0.38 $\pm$ 0.12 |  |
| SEAL-zero | 0.45 $\pm$ 0.10 | 0.39 $\pm$ 0.13 | 0.46 $\pm$ 0.11 | 0.38 $\pm$ 0.13 | 0.47 $\pm$ 0.11 | 0.35 $\pm$ 0.14 |  |
| $\lambda=0.5$ | SEAL-mask-abs | 0.35 $\pm$ 0.01 | 0.20 $\pm$ 0.02 | 0.36 $\pm$ 0.01 | 0.19 $\pm$ 0.02 | 0.37 $\pm$ 0.01 | 0.15 $\pm$ 0.01 |
| SEAL-mask | 0.50 $\pm$ 0.02 | 0.05 $\pm$ 0.04 | 0.56 $\pm$ 0.01 | 0.04 $\pm$ 0.03 | 0.64 $\pm$ 0.02 | 0.01 $\pm$ 0.01 |  |
| SEAL-abs | 0.35 $\pm$ 0.02 | 0.36 $\pm$ 0.04 | 0.37 $\pm$ 0.02 | 0.35 $\pm$ 0.04 | 0.40 $\pm$ 0.05 | 0.33 $\pm$ 0.05 |  |
| SEAL-zero | 0.44 $\pm$ 0.05 | 0.34 $\pm$ 0.05 | 0.46 $\pm$ 0.05 | 0.32 $\pm$ 0.05 | 0.48 $\pm$ 0.05 | 0.28 $\pm$ 0.05 |  |
| $\lambda=10^{-1}$ | SEAL-mask-abs | 0.38 $\pm$ 0.02 | 0.19 $\pm$ 0.02 | 0.38 $\pm$ 0.02 | 0.18 $\pm$ 0.02 | 0.38 $\pm$ 0.02 | 0.16 $\pm$ 0.01 |
| SEAL-mask | 0.57 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.63 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.72 $\pm$ 0.02 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.53 $\pm$ 0.12 | 0.52 $\pm$ 0.10 | 0.55 $\pm$ 0.12 | 0.50 $\pm$ 0.10 | 0.57 $\pm$ 0.11 | 0.48 $\pm$ 0.10 |  |
| SEAL-zero | 0.56 $\pm$ 0.08 | 0.51 $\pm$ 0.11 | 0.59 $\pm$ 0.08 | 0.49 $\pm$ 0.10 | 0.60 $\pm$ 0.08 | 0.45 $\pm$ 0.12 |  |
| $\lambda=10^{-2}$ | SEAL-mask-abs | 0.35 $\pm$ 0.02 | 0.20 $\pm$ 0.02 | 0.36 $\pm$ 0.02 | 0.20 $\pm$ 0.01 | 0.36 $\pm$ 0.02 | 0.18 $\pm$ 0.01 |
| SEAL-mask | 0.53 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.60 $\pm$ 0.02 | 0.00 $\pm$ 0.00 | 0.69 $\pm$ 0.02 | 0.00 $\pm$ 0.00 |  |
| SEAL-abs | 0.47 $\pm$ 0.14 | 0.40 $\pm$ 0.08 | 0.48 $\pm$ 0.15 | 0.40 $\pm$ 0.08 | 0.50 $\pm$ 0.15 | 0.38 $\pm$ 0.08 |  |
| SEAL-zero | 0.50 $\pm$ 0.09 | 0.39 $\pm$ 0.12 | 0.52 $\pm$ 0.10 | 0.38 $\pm$ 0.12 | 0.53 $\pm$ 0.11 | 0.36 $\pm$ 0.14 |  |
| $\lambda=10^{-3}$ | SEAL-mask-abs | 0.38 $\pm$ 0.02 | 0.22 $\pm$ 0.03 | 0.39 $\pm$ 0.01 | 0.21 $\pm$ 0.02 | 0.39 $\pm$ 0.02 | 0.20 $\pm$ 0.02 |
| SEAL-mask | 0.53 $\pm$ 0.02 | 0.06 $\pm$ 0.04 | 0.58 $\pm$ 0.02 | 0.05 $\pm$ 0.04 | 0.65 $\pm$ 0.02 | 0.04 $\pm$ 0.03 |  |
| SEAL-abs | 0.41 $\pm$ 0.09 | 0.38 $\pm$ 0.09 | 0.42 $\pm$ 0.10 | 0.37 $\pm$ 0.08 | 0.43 $\pm$ 0.09 | 0.36 $\pm$ 0.08 |  |
| SEAL-zero | 0.47 $\pm$ 0.06 | 0.36 $\pm$ 0.09 | 0.49 $\pm$ 0.08 | 0.35 $\pm$ 0.10 | 0.51 $\pm$ 0.09 | 0.31 $\pm$ 0.10 |  |
| $\lambda=10^{-4}$ | SEAL-mask-abs | 0.38 $\pm$ 0.03 | 0.26 $\pm$ 0.04 | 0.41 $\pm$ 0.04 | 0.25 $\pm$ 0.03 | 0.43 $\pm$ 0.04 | 0.24 $\pm$ 0.03 |
| SEAL-mask | 0.52 $\pm$ 0.02 | 0.13 $\pm$ 0.05 | 0.58 $\pm$ 0.02 | 0.11 $\pm$ 0.03 | 0.63 $\pm$ 0.03 | 0.10 $\pm$ 0.04 |  |
| SEAL-abs | 0.38 $\pm$ 0.07 | 0.41 $\pm$ 0.11 | 0.41 $\pm$ 0.09 | 0.39 $\pm$ 0.09 | 0.44 $\pm$ 0.10 | 0.36 $\pm$ 0.07 |  |
| SEAL-zero | 0.41 $\pm$ 0.05 | 0.40 $\pm$ 0.13 | 0.45 $\pm$ 0.06 | 0.38 $\pm$ 0.12 | 0.47 $\pm$ 0.07 | 0.34 $\pm$ 0.11 |  |
| $\lambda=0$ | SEAL-mask-abs | 0.37 $\pm$ 0.01 | 0.25 $\pm$ 0.02 | 0.41 $\pm$ 0.02 | 0.23 $\pm$ 0.02 | 0.43 $\pm$ 0.03 | 0.22 $\pm$ 0.01 |
| SEAL-mask | 0.52 $\pm$ 0.00 | 0.11 $\pm$ 0.02 | 0.59 $\pm$ 0.01 | 0.09 $\pm$ 0.01 | 0.65 $\pm$ 0.01 | 0.07 $\pm$ 0.01 |  |
| SEAL-abs | 0.30 $\pm$ 0.01 | 0.32 $\pm$ 0.02 | 0.33 $\pm$ 0.01 | 0.31 $\pm$ 0.02 | 0.35 $\pm$ 0.02 | 0.29 $\pm$ 0.02 |  |
| SEAL-zero | 0.39 $\pm$ 0.02 | 0.28 $\pm$ 0.02 | 0.43 $\pm$ 0.02 | 0.26 $\pm$ 0.02 | 0.47 $\pm$ 0.03 | 0.21 $\pm$ 0.02 |  |

**Table 16: Model explanations performance using different type of masking strategy in SEAL architecture for Solubility dataset. Evaluating using Fidelity metrics at 10%, 20%, and 30% masking thresholds.**
|  | Model | $\text{Fidelity}_{10}+\uparrow$ | $\text{Fidelity}_{10}-\downarrow$ | $\text{Fidelity}_{20}+\uparrow$ | $\text{Fidelity}_{20}-\downarrow$ | $\text{Fidelity}_{30}+\uparrow$ | $\text{Fidelity}_{30}-\downarrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Solubility |  |  |  |  |  |  |  |
| $\lambda=2$ | SEAL-mask-abs | 0.45 $\pm$ 0.03 | 0.26 $\pm$ 0.10 | 0.46 $\pm$ 0.03 | 0.25 $\pm$ 0.10 | 0.49 $\pm$ 0.04 | 0.22 $\pm$ 0.09 |
| SEAL-mask | 0.42 $\pm$ 0.03 | 0.30 $\pm$ 0.10 | 0.45 $\pm$ 0.03 | 0.29 $\pm$ 0.09 | 0.49 $\pm$ 0.03 | 0.27 $\pm$ 0.08 |  |
| SEAL-abs | 1.12 $\pm$ 0.25 | 1.04 $\pm$ 0.42 | 1.20 $\pm$ 0.28 | 0.96 $\pm$ 0.38 | 1.34 $\pm$ 0.32 | 0.84 $\pm$ 0.33 |  |
| SEAL-zero | 0.98 $\pm$ 0.23 | 1.20 $\pm$ 0.47 | 1.07 $\pm$ 0.26 | 1.11 $\pm$ 0.42 | 1.21 $\pm$ 0.30 | 0.98 $\pm$ 0.37 |  |
| $\lambda=1$ | SEAL-mask-abs | 0.44 $\pm$ 0.04 | 0.22 $\pm$ 0.04 | 0.46 $\pm$ 0.04 | 0.21 $\pm$ 0.04 | 0.48 $\pm$ 0.05 | 0.19 $\pm$ 0.04 |
| SEAL-mask | 0.41 $\pm$ 0.03 | 0.27 $\pm$ 0.04 | 0.44 $\pm$ 0.03 | 0.27 $\pm$ 0.04 | 0.47 $\pm$ 0.04 | 0.24 $\pm$ 0.04 |  |
| SEAL-abs | 1.04 $\pm$ 0.31 | 0.83 $\pm$ 0.39 | 1.11 $\pm$ 0.34 | 0.78 $\pm$ 0.36 | 1.22 $\pm$ 0.38 | 0.69 $\pm$ 0.31 |  |
| SEAL-zero | 0.90 $\pm$ 0.30 | 0.99 $\pm$ 0.42 | 0.98 $\pm$ 0.33 | 0.93 $\pm$ 0.38 | 1.10 $\pm$ 0.37 | 0.82 $\pm$ 0.33 |  |
| $\lambda=0.5$ | SEAL-mask-abs | 0.44 $\pm$ 0.02 | 0.22 $\pm$ 0.03 | 0.46 $\pm$ 0.02 | 0.21 $\pm$ 0.03 | 0.48 $\pm$ 0.02 | 0.19 $\pm$ 0.03 |
| SEAL-mask | 0.41 $\pm$ 0.03 | 0.26 $\pm$ 0.03 | 0.44 $\pm$ 0.02 | 0.26 $\pm$ 0.03 | 0.48 $\pm$ 0.03 | 0.24 $\pm$ 0.03 |  |
| SEAL-abs | 1.08 $\pm$ 0.31 | 0.89 $\pm$ 0.41 | 1.15 $\pm$ 0.33 | 0.83 $\pm$ 0.38 | 1.26 $\pm$ 0.37 | 0.74 $\pm$ 0.34 |  |
| SEAL-zero | 0.91 $\pm$ 0.26 | 1.08 $\pm$ 0.50 | 1.01 $\pm$ 0.28 | 1.00 $\pm$ 0.46 | 1.13 $\pm$ 0.33 | 0.89 $\pm$ 0.40 |  |
| $\lambda=10^{-1}$ | SEAL-mask-abs | 0.44 $\pm$ 0.05 | 0.27 $\pm$ 0.03 | 0.45 $\pm$ 0.06 | 0.25 $\pm$ 0.03 | 0.47 $\pm$ 0.06 | 0.23 $\pm$ 0.02 |
| SEAL-mask | 0.45 $\pm$ 0.04 | 0.24 $\pm$ 0.04 | 0.48 $\pm$ 0.04 | 0.24 $\pm$ 0.03 | 0.52 $\pm$ 0.04 | 0.23 $\pm$ 0.02 |  |
| SEAL-abs | 0.77 $\pm$ 0.20 | 0.67 $\pm$ 0.29 | 0.83 $\pm$ 0.23 | 0.62 $\pm$ 0.26 | 0.92 $\pm$ 0.27 | 0.55 $\pm$ 0.23 |  |
| SEAL-zero | 0.69 $\pm$ 0.20 | 0.78 $\pm$ 0.33 | 0.75 $\pm$ 0.23 | 0.73 $\pm$ 0.30 | 0.83 $\pm$ 0.25 | 0.65 $\pm$ 0.26 |  |
| $\lambda=10^{-2}$ | SEAL-mask-abs | 0.42 $\pm$ 0.02 | 0.23 $\pm$ 0.03 | 0.43 $\pm$ 0.02 | 0.22 $\pm$ 0.03 | 0.44 $\pm$ 0.02 | 0.20 $\pm$ 0.03 |
| SEAL-mask | 0.42 $\pm$ 0.01 | 0.21 $\pm$ 0.02 | 0.45 $\pm$ 0.02 | 0.21 $\pm$ 0.02 | 0.48 $\pm$ 0.01 | 0.21 $\pm$ 0.02 |  |
| SEAL-abs | 0.64 $\pm$ 0.12 | 0.50 $\pm$ 0.13 | 0.68 $\pm$ 0.13 | 0.49 $\pm$ 0.13 | 0.73 $\pm$ 0.14 | 0.46 $\pm$ 0.13 |  |
| SEAL-zero | 0.59 $\pm$ 0.11 | 0.48 $\pm$ 0.11 | 0.64 $\pm$ 0.12 | 0.47 $\pm$ 0.11 | 0.72 $\pm$ 0.14 | 0.44 $\pm$ 0.11 |  |
| $\lambda=10^{-3}$ | SEAL-mask-abs | 0.48 $\pm$ 0.04 | 0.29 $\pm$ 0.03 | 0.51 $\pm$ 0.04 | 0.25 $\pm$ 0.03 | 0.55 $\pm$ 0.05 | 0.22 $\pm$ 0.03 |
| SEAL-mask | 0.45 $\pm$ 0.04 | 0.33 $\pm$ 0.03 | 0.50 $\pm$ 0.05 | 0.31 $\pm$ 0.03 | 0.54 $\pm$ 0.05 | 0.27 $\pm$ 0.03 |  |
| SEAL-abs | 1.24 $\pm$ 0.20 | 1.26 $\pm$ 0.33 | 1.37 $\pm$ 0.21 | 1.14 $\pm$ 0.30 | 1.55 $\pm$ 0.25 | 0.98 $\pm$ 0.25 |  |
| SEAL-zero | 1.10 $\pm$ 0.23 | 1.43 $\pm$ 0.32 | 1.23 $\pm$ 0.25 | 1.29 $\pm$ 0.29 | 1.40 $\pm$ 0.27 | 1.11 $\pm$ 0.24 |  |
| $\lambda=10^{-4}$ | SEAL-mask-abs | 0.59 $\pm$ 0.06 | 0.41 $\pm$ 0.04 | 0.62 $\pm$ 0.06 | 0.39 $\pm$ 0.04 | 0.66 $\pm$ 0.06 | 0.36 $\pm$ 0.03 |
| SEAL-mask | 0.56 $\pm$ 0.06 | 0.47 $\pm$ 0.04 | 0.60 $\pm$ 0.06 | 0.45 $\pm$ 0.04 | 0.64 $\pm$ 0.07 | 0.42 $\pm$ 0.03 |  |
| SEAL-abs | 0.78 $\pm$ 0.14 | 0.64 $\pm$ 0.16 | 0.84 $\pm$ 0.15 | 0.58 $\pm$ 0.13 | 0.91 $\pm$ 0.17 | 0.52 $\pm$ 0.09 |  |
| SEAL-zero | 0.72 $\pm$ 0.10 | 0.73 $\pm$ 0.22 | 0.79 $\pm$ 0.10 | 0.66 $\pm$ 0.19 | 0.88 $\pm$ 0.12 | 0.58 $\pm$ 0.15 |  |
| $\lambda=0$ | SEAL-mask-abs | 0.53 $\pm$ 0.06 | 0.46 $\pm$ 0.04 | 0.59 $\pm$ 0.06 | 0.42 $\pm$ 0.04 | 0.63 $\pm$ 0.07 | 0.37 $\pm$ 0.04 |
| SEAL-mask | 0.54 $\pm$ 0.05 | 0.47 $\pm$ 0.04 | 0.61 $\pm$ 0.05 | 0.43 $\pm$ 0.04 | 0.65 $\pm$ 0.06 | 0.38 $\pm$ 0.04 |  |
| SEAL-abs | 0.49 $\pm$ 0.03 | 0.55 $\pm$ 0.07 | 0.54 $\pm$ 0.04 | 0.53 $\pm$ 0.08 | 0.58 $\pm$ 0.05 | 0.48 $\pm$ 0.07 |  |
| SEAL-zero | 0.53 $\pm$ 0.03 | 0.49 $\pm$ 0.07 | 0.60 $\pm$ 0.03 | 0.46 $\pm$ 0.06 | 0.65 $\pm$ 0.04 | 0.41 $\pm$ 0.05 |  |

Figure: Figure 6: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x6.png

Figure: Figure 7: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x7.png

Figure: Figure 8: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x8.png

Figure: Figure 9: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x9.png

Figure: Figure 10: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x10.png

Figure: Figure 11: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x11.png

Figure: Figure 12: Node-level explanation examples from user study, The red color indicates that the highlighted atoms had a positive contribution to the compound’s solubility.
Refer to caption: x12.png

## Appendix E E. Visualizations

Figures [13](https://arxiv.org/html/2508.15015v1#A5.F13), [15](https://arxiv.org/html/2508.15015v1#A5.F15), [17](https://arxiv.org/html/2508.15015v1#A5.F17), [19](https://arxiv.org/html/2508.15015v1#A5.F19), [21](https://arxiv.org/html/2508.15015v1#A5.F21), [23](https://arxiv.org/html/2508.15015v1#A5.F23), and [25](https://arxiv.org/html/2508.15015v1#A5.F25) display examples of explanations generated by the SEAL model for the tasks in the synthetic dataset for the positive target class. The explanations for the negative class, where the substructure is not present in the compound, are illustrated in Figures [14](https://arxiv.org/html/2508.15015v1#A5.F14), [16](https://arxiv.org/html/2508.15015v1#A5.F16), [18](https://arxiv.org/html/2508.15015v1#A5.F18), [20](https://arxiv.org/html/2508.15015v1#A5.F20), [22](https://arxiv.org/html/2508.15015v1#A5.F22), [24](https://arxiv.org/html/2508.15015v1#A5.F24), and [26](https://arxiv.org/html/2508.15015v1#A5.F26). The explanations for the real-world datasets are available in Figures [27](https://arxiv.org/html/2508.15015v1#A5.F27)-[29](https://arxiv.org/html/2508.15015v1#A5.F29).

Figure: Figure 13: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the Boron (B) task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x13.png

Figure: Figure 14: Node-level explanation examples of the SEAL method evaluated on the Boron (B) task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x14.png

Figure: Figure 15: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on Halogens (X) task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x15.png

Figure: Figure 16: Node-level explanation examples of the SEAL method evaluated on the Halogens (X) task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x16.png

Figure: Figure 17: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the rings-max task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x17.png

Figure: Figure 18: Node-level explanation examples of the SEAL method evaluated on the rings-max task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x18.png

Figure: Figure 19: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the rings-count task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x19.png

Figure: Figure 20: Node-level explanation examples of the SEAL method evaluated on the rings-count task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x20.png

Figure: Figure 21: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the PAINS task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x21.png

Figure: Figure 22: Node-level explanation examples of the SEAL method evaluated on the PAINS task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x22.png

Figure: Figure 23: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the indole task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x23.png

Figure: Figure 24: Node-level explanation examples of the SEAL method evaluated on the indole task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x24.png

Figure: Figure 25: Node-level explanation examples of the SEAL method and Ground-Truth evaluated on the Phosphorus (P) task for the positive target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x25.png

Figure: Figure 26: Node-level explanation examples of the SEAL method evaluated on the Phosphorus (P) task for the negative target class. The red color indicates that the highlighted atoms had a positive contribution to the compound’s positive prediction. Blue as a negative contribution.
Refer to caption: x26.png

Figure: Figure 27: Node-level explanation examples of the SEAL method evaluated on the hERG dataset. The red color indicates that the highlighted atoms had a positive contribution to the positive prediction. Blue as a negative contribution.
Refer to caption: x27.png

Figure: Figure 28: Node-level explanation examples of the SEAL method evaluated on the CYP2C9 dataset. The red color indicates that the highlighted atoms had a positive contribution to the positive prediction. Blue as a negative contribution.
Refer to caption: x28.png

Figure: Figure 29: Node-level explanation examples of the SEAL method evaluated on the aqueous solubility dataset. The red color indicates that the highlighted atoms had a positive contribution to the positive prediction. Blue as a negative contribution.
Refer to caption: x29.png

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 4
presentation: 3
questions: see weakness
soundness: 3
strengths: - Addressing a known limitation of message passing: attribution entanglement across the whole graph.

- Comprehensive results on both synthetic and real-world datasets.
summary: The manuscript introduces SEAL (Substructure Explanation via Attribution Learning), an interpretable GNN for molecular property prediction. SEAL partitions a molecular graph into chemically meaningful fragments and constrains inter-fragment information flow, aiming to disentangle local representations from global message-passing effects so that attributions on substructures better reflect true causal influence. Across synthetic and real benchmarks, SEAL reportedly surpasses prior explainability methods and yields explanations rated more intuitive and trustworthy by chemists.
weaknesses: 1. Limited novelty. The core idea is not particularly new; many prior works have proposed similar frameworks and pipelines.


2.  The baseline set appears dated (e.g., ProtGNN from 2022). Please include more recent and stronger competitors.

3.  Report performance under Bemis–Murcko scaffold splits and strict OOD scaffold tests.

4.  Provide runtime/memory profiles vs. popular explainers: train/inference time per molecule, scaling with fragment count, and large-scale throughput.

5.   Missing ablations on key design choices.

---

confidence: 5
contribution: 1
initial_rating: 2
presentation: 3
questions: Please refer to the weaknesses.
soundness: 1
strengths: 1. The prediction head is explicitly the sum of fragment MLP contributions, so each fragment’s scalar contribution is directly interpretable.
2. The paper is well-written and easy to follow.
summary: The paper proposes SEAL, an interpretable GNN for molecular property prediction that first decomposes a molecule into chemically meaningful fragments, aggregates node features within each fragment, and predicts as a sum of fragment contributions.
weaknesses: 1. The paper would benefit from a fuller survey of (i) fragment/motif/scaffold-based explanation methods and (ii) models that explicitly handle intra- vs. inter-fragment interactions in molecular representation learning. Please clarify how your design choices (fragmentation strategy, message-passing split, and regularization) differ from prior work, what problems they solve, and where the contribution sits in the existing taxonomy. A small comparison table contrasting assumptions, granularity, learning signals, and interpretability guarantees would clearly highlight the novelty and scope of the approach.
2. To support the interpretability claims, the paper needs to include head-to-head evaluations against widely used subgraph-level explanation approaches under a shared protocol: same trained backbone, matched sparsity/size budgets, and standard metrics (fidelity/accuracy drop, sufficiency/necessity, stability/consistency across seeds), plus runtime/overhead.
3. Beyond post-hoc explainers, it is also crucial to compare with representation learning methods that build on subgraphs/motifs/hierarchies.

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: - What is the guarantee that the initial fragments are well-decided?
- Can you summarize what novelty this paper brings to the field? 
- Can you explain what SEALAtom actually offers compared to the original graph? If every atom is a fragment, then that is already the original graph.
- Can you explain your GNN layer in more detailed with the size of each elements (i.e., trainable weight matrices, embedding size etc.)?
soundness: 2
strengths: - Good motivation and introduction to the problem. 
- Code sharing for reproducibility.
- User study to show the effectiveness of the model compared to the baselines.
summary: The paper propose a new algorithm called SEAL that identifies fragments in molecule and constraints message passing architecture. It measures the influence of the fragments in the molecule to the prediction rather than focusing on node or edge level. This would increase the interpretability of GNN and generate more intuitive explanations.
weaknesses: - It is not clear if fragmentation algorithm is unique in the model, or just borrowed from the available methods.
- It is not clear how SEALAtom differs than the original graph without any fragmentation.
- Equation 3 requires more explanation.
- No theory behind the method. One can claim that the model should be able to learn the influential edges without separating intra and inter edges. Besides, what is the guarantee that inter-edges are not influential?

---

confidence: 5
contribution: 3
initial_rating: 2
presentation: 2
questions: Can the authors address why evaluation against other self-explaining GNN methods was omitted?

Can the authors address why other real world datasets were omitted?

Why wasn't fidelity calculated on B-XAIC datasets?

How do the authors ensure that user studies accurately evaluate faithfulness of explanation with respect to the model (rather than human chemistry bias), and provide more details about study participants?

Why do the authors only do Figure 6 on Fidelity with 30% of masking?
soundness: 3
strengths: The work addresses an important gap in interpretable molecular property prediction by focusing on fragment-level explanations, which have strong chemical relevance.

The adoption of BRICS and fragment aggregation is conceptually appealing and could aid chemists in understanding model decisions in familiar terms.

The paper is well-motivated and offers thoughtful discussion about interpretability, with the potential to advance explainable AI in the molecular domain.
summary: The paper proposes a novel fragment-wise approach to molecular property prediction, utilizing BRICS fragmentation and aggregating fragment-level contributions to enhance interpretability of Graph Neural Network models. The rationale is grounded in pharmaceutical chemistry, where molecular properties often relate to functional groups, and the model aims to mirror this intuition in predicting outcomes.
weaknesses: The main evaluation relies on synthetic datasets that are often trivial and do not sufficiently challenge the model’s capacity for real-world tasks. Only three real-world datasets are used for more robust assessment, limiting generalizability. There exist many other datasets which are not used in this work. For instance, in the paper of HiGNN (Zhu et al., 2022) there are 11 real world datasets and none of them is investigated in this paper.

A big part of the evaluation checks alignment with ground-truth substructures but does not verify faithfulness with respect to the model’s decision process, which is instead done only with 2 datasets. An explanation should first be evaluated for the faithfulness using standard metrics (Fidelity, Inv Fidelity): if a model learns a spurious bias, the correct explanation should reflect that bias. Only after confirming model alignment, comparisons to chemical ground truth are meaningful. Let me cite Agarwal et al, 2023: "trained GNN model may only capture one or an entirely different rationale. In such cases, evaluating the explanation output by a state-of-the-art method using the ground-truth explanation is incorrect because the underlying GNN model does not rely on that ground-truth explanation. In addition, even if a unique ground-truth explanation generates the correct class label, the GNN model trained on the data could be a weak predictor using an entirely different rationale for prediction. Therefore, the ground-truth explanation cannot be used to assess post hoc explanations of such models."

Fragment extraction is presented as a generalizable mechanism, but the experiments rely exclusively on pre-defined methods (BRICS), which fail to pinpoint atomic-level contributions (e.g., Boron atom in Figure 4), and do not capture long-range interactions crucial for complex properties.

Benchmarking against relevant self-explainable GNNs (GNAN, GIB, VIB, PiGNN, KerGNN) is scarce as only one method is used as comparison, weakening the claims of superiority or novelty in interpretability.

Comparison to leading molecular property predictors (e.g., Grover, MultiChem, HyperMol) is absent.

The user study is incorrectly posed and totally useless, as the question asks which part of the molecule is thought to better impact the solubility. However, the quality of explanations should be primarily checked against the model. The user cannot know whether the highlighted part is truly the one impacting the model’s prediction. Additionally, no information is provided on the number and specialty of the study participants. The reviewer would assume that they are chemists. However, it is obvious that a chemist would be biased toward using functional groups, as that is exactly how they reason, but there are no other methods among the techniques that actually use fragments.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: 1.	In Figure 1, is the claim that “the most polar groups contribute positively and the hydrophobic groups contribute negatively” supported by existing scientific evidence? Does the contribution of each fragment align with known chemical findings?

2.	Could the authors provide a comparison of computational efficiency or runtime between the proposed method and the baselines?

3.	In Line 133, can the embeddings before the graph readout stage be analyzed to evaluate atom-level contributions?

4.	For the synthetic dataset, how are the ground-truth explanations obtained? In the user study, did participants provide what they believe the true explanations should be based on their expertise? Why are the ground-truth explanations not presented in the user study?

5.	The paper states that a high $\lambda$ leads to more interpretable results, but in some experiments, the chosen values are very small. Could the authors explain this inconsistency?

6.	Why is HiGNN used both as the explanation model and as a baseline model?

7.	How is the budget for the final explanation results determined? How do the authors decide how many fragments are selected for the final explanation? Additionally, could the authors explain the color-coding scheme used in Figure 4?

8.	SEALAtom treats a single atom as a functional group. Why does it outperform baselines such as GNNExplainer and PGExplainer?

9.	In Line 676, the paper states that “we decide to mask the same amount of atoms for each molecule among all methods.” Is this assumption reasonable for the budget settings?
soundness: 2
strengths: 1.	The paper addresses an important problem — ensuring that graph explanations are consistent with established scientific understanding. This is a valuable and meaningful research direction, and the paper makes a concrete step toward it.

2.	The paper conducts experiments on both synthetic and real-world datasets to demonstrate effectiveness. The results are clearly presented through figures and tables.

3.	A user study is included to examine the differences between human understanding and graph explanation techniques, providing an interesting perspective and direction for future research.
summary: This paper aims to build an interpretable GNN model that quantifies the contribution of chemically meaningful fragments, ensuring that the explanations better align with domain knowledge. The proposed method decomposes molecular graphs into sets of fragments using a decomposition procedure similar to the BRICS algorithm and evaluates each fragment’s contribution. To isolate the effects of different fragments, the authors introduce a message-passing layer with separate parameters to control information flow within and between fragments. Experiments on synthetic datasets, real-world datasets, and a user study demonstrate the effectiveness of the proposed approach.
weaknesses: 1. Limited novelty. The paper lacks significant novelty. Explaining graphs at the fragment level and using message-passing schemes have been studied in several prior works [1–4]. The proposed method does not appear to introduce a new concept or contribution beyond these approaches.

2. Questionable methodological claim. The authors claim that “a final embedding of each node reflects not only its own properties but also the cumulative properties of distant atoms.” However, Section 3.2 adopts a similar approach to existing methods, making this claim of methodological uniqueness questionable.

3. Lack of detailed explanations. Some statements lack sufficient detail, making the paper difficult to follow. For example, in Line 95, the authors state that in both Cao et al. (2024) and Wang et al. (2025), fragments may contain significant signals from other parts of the molecule, thereby reducing local interpretability. However, the proposed method still allows information flow between fragments, so the distinction between approaches is not clearly explained. Similarly, in Line 105, the authors claim that Wu et al. (2023) use the BRICS method for fragment-level explanations; however, that method is not included in the comparisons.

4. Unclear selection of baselines. The choice of baseline models is questionable. HiGNN is primarily designed for property prediction, yet it is used here for an explanation task. The authors do not compare their method with other interpretable GNN models but instead focus on post-hoc methods and a property prediction model, limiting the fairness of the evaluation.

5. Simplistic evaluation and overly strong results. The model assumes linear independence in the final step (Section 3.1) and aggregates via summation. The evaluation tasks appear overly simple: in Table 4 (AUROC) and Table 5 (F1), the classification models achieve scores of 1.0 or very close to it. This raises concerns about potential data leakage or task simplicity and whether the proposed method can generalize to more complex benchmarks [5].

6. Unsupported statements. Some claims lack experimental or theoretical justification. For instance, in Line 155, the authors state that “the lack of bias would cause an equal distribution of contributions across all fragments, diminishing interpretability.” It is unclear how the label distribution affects this behavior or why the absence of $b$ would reduce interpretability.

7. Inconsistent or unclear visualizations. Several visualizations conflict with or are difficult to reconcile with the fragment decomposition method. For example, in the right panel of Figure 1, it is unclear how the shown functional groups are derived from the decomposition process. Similar issues appear in other figures as well.

8. Questionable benchmark selection. The chosen benchmarks are not well justified. The synthetic dataset, B-XAIC, is not a widely recognized benchmark. For real-world datasets, explanation datasets such as Mutagenicity are available but not used. The rationale for benchmark selection and fairness of comparison should be clarified.

9. Minor errors and missing details.There are some minor issues in the manuscript. For instance, in Line 256, “Hierarchical GNN” should be “HiGNN.” Some figures lack column labels or sufficient annotation of important nodes or subgraphs in the appendix.

10. Inconsistent interpretation granularity. While the paper claims to provide fragment-level explanations, it also proposes atom-level explanations. In some cases, the atom-level model (SEALAtom) performs better than the fragment-level model (SEAL), which raises concerns about the claimed benefits of fragment-level reasoning.


1, Yu, Z. and Gao, H., 2024. MAGE: Model-level graph neural networks explanations via motif-based graph generation. arXiv preprint arXiv:2405.12519.

2, Yu, Z. and Gao, H., 2022. Motifexplainer: a motif-based graph neural network explainer. arXiv preprint arXiv:2202.00519.

3, Liu, X., Luo, D., Gao, W. and Liu, Y., 2025, July. 3dgraphx: Explaining 3d molecular graph models via incorporating chemical priors. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1 (pp. 859-870).

4, Gui, S., Yuan, H., Wang, J., Lao, Q., Li, K. and Ji, S., 2023. Flowx: Towards explainable graph neural networks via message flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7), pp.4567-4578.

5, Bui, N., Nguyen, H.T., Nguyen, V.A. and Ying, R., 2024. Explaining graph neural networks via structure-aware interaction index. arXiv preprint arXiv:2405.14352.


Decision: Reject