Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph
Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph
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发表时间：
2019-07
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通讯作者：
M. Ohue;Ryota;Keisuke Yanagisawa;Y. Akiyama
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作者：
M. Ohue;Ryota;Keisuke Yanagisawa;Y. Akiyama
机器学习经常用于虚拟筛选，以寻找对目标蛋白质具有药理活性的化合物。编织模块是一种图的卷积深度神经网络，它不仅使用了只关注原子的特征(原子特征)，而且还使用了关注原子对的特征(对特征)，从而可以考虑不相邻原子的信息。然而，图形上的距离与三维坐标距离之间的相关性是不确定的。在本文中，我们对编织模块提出了三点改进。首先，修改了图上环原子之间的距离，使图上的距离更接近坐标距离。其次，在特征对的卷积层中，根据图上距离的不同使用不同的权重矩阵。最后，在将配对特征转换为原子特征时，使用了按距离加权和。实验结果表明，该方法的性能略好于编织模型，距离表示的改进对复合活性预测有一定的帮助。
Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features); thus, it can consider information of nonadjacent atoms. However, the correlation between the distance on the graph and the three-dimensional coordinate distance is uncertain. In this paper, we propose three improvements for modifying the weave module. First, the distances between ring atoms on the graph were modified to bring the distances on the graph closer to the coordinate distance. Second, different weight matrices were used depending on the distance on the graph in the convolution layers of the pair features. Finally, a weighted sum, by distance, was used when converting pair features to atom features. The experimental results show that the performance of the proposed method is slightly better than that of the weave module, and the improvement in the distance representation might be useful for compound activity prediction.