With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanisms have been generally employed to explain the importance of molecular substructures that contribute to molecular properties, their interpretability remains limited. In this work, we introduce a versatile segmentation method and develop an interpretable subgraph attention (ISA) network with positive and negative streams (ISA-PN) to enhance the understanding of molecular structure-property relationships. The predictive performance of the ISA models was validated using data sets for aqueous solubility, lipophilicity, and melting temperature, with a particular focus on evaluating interpretability for the aqueous solubility data set. The ISA-PN model enables the quantification of the contributions of molecular substructures through positive and negative attention scores. Comparative analyses of the ISA, ISA-PN, and GC-Net (group contribution network) models demonstrate that the ISA-PN model significantly improves interpretability while maintaining similar accuracy levels. This study highlights the efficacy of the ISA-PN model in providing meaningful insights into the contributions of molecular substructures to molecular properties, thereby enhancing the interpretability of DL models in chemical applications.

中文翻译：

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具有积极和消极关注以提高可解释性的分层图注意力网络：ISA-PN。


随着深度学习 （DL） 方法在化学和材料科学领域的进步，DL 模型的可解释性已成为阐明定量（分子）结构-性质关系的关键问题。尽管注意力机制通常被用来解释有助于分子特性的分子亚结构的重要性，但它们的可解释性仍然有限。在这项工作中，我们引入了一种通用的分割方法，并开发了一个具有正负流 （ISA-PN） 的可解释子图注意力 （ISA） 网络，以增强对分子结构-性质关系的理解。使用水溶性、亲脂性和熔融温度数据集验证了 ISA 模型的预测性能，特别注重评估水溶性数据集的可解释性。ISA-PN 模型能够通过积极和消极的注意力分数来量化分子亚结构的贡献。对 ISA、ISA-PN 和 GC-Net（组贡献网络）模型的比较分析表明，ISA-PN 模型显著提高了可解释性，同时保持了相似的准确性水平。本研究强调了 ISA-PN 模型在为分子亚结构对分子特性的贡献提供有意义的见解方面的有效性，从而增强了 DL 模型在化学应用中的可解释性。