Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization.

中文翻译：

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将药效团概念与图神经网络相结合，用于化学性质预测和解释

最近，图神经网络 (GNN) 彻底改变了化学性质预测领域，并在基准数据集上取得了最先进的结果。与传统的基于描述符和指纹的 QSAR 模型相比，GNN 可以学习任务相关的表示，完全摆脱了专家定义的规则。然而，由于缺乏有用的先验知识，GNN 的预测性能和可解释性可能会受到影响。在这项研究中，我们引入了一种新的 GNN 模型，称为 RG-MPNN，用于化学性质预测，该模型将药效团信息分层集成到消息传递神经网络 (MPNN) 架构中，具体而言，以基于药效团的简化图 (RG) 池化的方式. RG-MPNN 不仅吸收了原子级消息传递阶段的原子和键信息，还有来自 RG 级信息传递阶段的药效团信息。我们在 11 个基准和 10 个激酶数据集上的实验结果表明，我们的模型始终匹配或优于其他现有的 GNN 模型。此外，我们证明了将基于药效团的 RG 池应用到 MPNN 架构通常可以帮助 GNN 模型提高预测能力。RG-MPNN 表示的聚类分析和药效团节点的重要性分析将帮助化学家深入了解命中发现和先导优化。我们证明了将基于药效团的 RG 池应用到 MPNN 架构通常可以帮助 GNN 模型提高预测能力。RG-MPNN 表示的聚类分析和药效团节点的重要性分析将帮助化学家深入了解命中发现和先导优化。我们证明了将基于药效团的 RG 池应用到 MPNN 架构通常可以帮助 GNN 模型提高预测能力。RG-MPNN 表示的聚类分析和药效团节点的重要性分析将帮助化学家深入了解命中发现和先导优化。