The interpretability and accuracy of deep-learning-based predictive models play a pivotal role in accelerating computer-aided green product design considering environmental, health, and safety (EH&S) impacts. Recently, molecular graph-based hybrid representation methods have demonstrated comparable or superior abilities to other molecular representations. However, existing molecular graph-based hybrid representation methods incorporate only 2D-based atom-level, bond-level, or molecule-level features while neglecting the molecular geometry, also known as 3D spatial structure information, which is crucial for determining molecular properties. Moreover, existing molecular graph-based hybrid representations lack consideration of knowledge in the chemistry domain, which can improve the interpretability of the predictive models. To this end, a 3D multi-hierarchical representation-based deep neural network (3D-MrDNN) architecture, simultaneously integrating directed message passing neural network learned representation, chemically synthesizable fragment features, and molecular 3D spatial information, is established for the prediction of EH&S properties. The results of predictive performance and ablation studies indicate that the proposed model exhibits decent predictive ability for EH&S properties. Chemically synthesizable fragments are utilized to integrate chemical knowledge into the proposed 3D-MrDNN architecture, the interpretability of which enables chemists to find the key molecular fragments for designing target products with better EH&S performance.

中文翻译：

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基于可解释的 3D 多层次表示的深度神经网络，用于有机溶剂的环境、健康和安全特性预测

基于深度学习的预测模型的可解释性和准确性在加速考虑环境、健康和安全 (EH&S) 影响的计算机辅助绿色产品设计方面发挥着关键作用。最近，基于分子图的混合表示方法已经证明了与其他分子表示相当或更好的能力。然而，现有的基于分子图的混合表示方法仅包含基于 2D 的原子级、键级或分子级特征，而忽略了分子几何形状，也称为 3D 空间结构信息，这对于确定分子特性至关重要。此外，现有的基于分子图的混合表示缺乏对化学领域知识的考虑，这可以提高预测模型的可解释性。为此，建立了基于 3D 多层次表示的深度神经网络 (3D-MrDNN) 架构，同时集成定向消息传递神经网络学习表示、化学合成片段特征和分子 3D 空间信息，用于 EH&S 预测特性。预测性能和消融研究的结果表明，所提出的模型对 EH&S 特性表现出良好的预测能力。利用化学合成片段将化学知识整合到所提出的 3D-MrDNN 架构中，其可解释性使化学家能够找到关键分子片段，以设计具有更好 EH&S 性能的目标产品。