发表机构
National Economics University; VNU University of Science（国民经济大学; 越南国立大学自然科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SMILESGNN通过SMILES与图编码器的交叉注意力融合，在保持高预测性能的同时支持基于图的可解释毒性分析，并在ClinTox和Tox21基准上验证了其有效性。
AI 中文摘要
药物毒性预测对于减少药物发现后期的失败至关重要，但由于严重的类别不平衡、基于支架的泛化问题以及临床对可解释预测的需求，这一任务仍然具有挑战性。单模态方法——SMILES Transformer或图神经网络——能够捕捉分子结构的互补方面，而仅基于序列的模型无法直接提供基于图的归因解释。我们提出了SMILESGNN，一种多模态架构，通过交叉注意力融合SMILES Transformer编码器和GATv2图编码器，以及SMILESGNN-PT，一种使用ChemBERTa-2预训练骨干网络的变体。该设计在预测流程中保留了显式的图分支，支持基于GNNExplainer的与毒性预测相关的子结构分析。在ClinTox上，SMILESGNN仅用0.4M参数便实现了AUC-ROC 0.987和F1 0.906，与强大的SMILESTransformer和更大的ChemBERTa-2/GATv2拼接融合基线相比具有竞争力。在Tox21（12个任务）上，SMILESGNN-PT获得了平均AUC-ROC 0.750，与单独的ChemBERTa-2以及相同骨干网络的拼接融合基线相当。总体而言，结果表明交叉注意力是一种实用的融合替代方案，在保持竞争性预测性能的同时，支持基于图的可解释性。
英文摘要
Drug toxicity prediction is critical for reducing late-stage attrition in drug discovery, yet remains challenging due to severe class imbalance, scaffold-based generalization, and the clinical need for interpretable predictions. Single-modality approaches-SMILES Transformers or graph neural networks capture complementary aspects of molecular structure, while sequence-only models cannot directly provide graph-attributed explanations. We present SMILESGNN, a multimodal architecture that fuses a SMILES Transformer encoder and a GATv2 graph encoder via cross-attention, and SMILESGNN-PT, a variant using a ChemBERTa-2 pretrained backbone. The design retains an explicit graph branch within the predictive pipeline, supporting GNNExplainer-based analysis of substructures associated with toxic predictions. On ClinTox, SMILESGNN achieves AUC-ROC 0.987 and F1 0.906 with only 0.4M parameters, performing competitively with a strong SMILESTransformer and a larger ChemBERTa-2/GATv2 concat-fusion baseline. On Tox21 (12 tasks), SMILESGNN-PT obtains mean AUC-ROC 0.750, comparable to ChemBERTa-2 alone and the same-backbone concat-fusion baseline. Overall, the results suggest that cross-attention is a practical fusion alternative that preserves competitive predictive performance while enabling graph-based interpretability support.
Journal ref2026 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)
DOI：10.1109/MAPR72750.2026.11685822
