Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neural-network framework called self-attention-based message-passing neural network (SAMPN) to study the relationship between chemical properties and structures in an interpretable way. The main advantages of SAMPN are that it directly uses chemical graphs and breaks the black-box mold of many machine/deep learning methods. Specifically, its attention mechanism indicates the degree to which each atom of the molecule contributes to the property of interest, and these results are easily visualized. Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem. In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical property. Moreover, SAMPN can generate chemically visible and interpretable results, which can help researchers discover new pharmaceuticals and materials. The source code of the SAMPN prediction pipeline is freely available at Github (https://github.com/tbwxmu/SAMPN).

中文翻译：

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基于自注意力的消息传递神经网络，用于预测分子亲脂性和水溶性


高效、准确地预测分子特性（例如亲脂性和溶解度）对于化学和制药行业的合理化合物设计非常重要。为此，我们构建并应用称为基于自注意力的消息传递神经网络（SAMPN）的图神经网络框架，以可解释的方式研究化学性质和结构之间的关系。 SAMPN 的主要优点是它直接使用化学图，打破了许多机器/深度学习方法的黑盒模式。具体来说，它的注意力机制表明分子的每个原子对感兴趣的属性的贡献程度，并且这些结果很容易可视化。此外，SAMPN 的性能优于随机森林和 Deepchem 的深度学习框架 MPN。此外，SAMPN（Multi-SAMPN）的另一种形式可以同时预测多种化学性质，比预测一种特定化学性质的其他模型具有更高的准确性和效率。此外，SAMPN 可以生成化学上可见且可解释的结果，这可以帮助研究人员发现新的药物和材料。 SAMPN 预测管道的源代码可在 Github (https://github.com/tbwxmu/SAMPN) 上免费获取。