College of Information Science and Engineering , Ocean University of China , Qingdao 266100 , China.
J Chem Inf Model. 2019 Sep 23;59(9):3817-3828. doi: 10.1021/acs.jcim.9b00410. Epub 2019 Aug 30.
Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets.
准确预测分子性质对于新化合物的设计至关重要，这是药物发现的关键步骤。在本文中，我们利用分子图数据基于图卷积神经网络进行性质预测。此外，我们还引入了卷积空间图嵌入层（C-SGEL）来保留分子的空间连接信息。通过堆叠多个 C-SGEL 来构建卷积空间图嵌入网络（C-SGEN），以实现端到端的表示学习。为了增强网络的鲁棒性，我们还将分子指纹与 C-SGEN 相结合，构建了一个用于预测分子性质的组合模型。我们的对比实验表明，我们的方法具有较高的准确性，并且在一些公开的基准数据集上取得了最佳结果。
