多特性融合图卷积方法的分子生物活性预测

药物开发周期长且耗资大，使用计算机药物筛选方法辅助筛选先导化合物的方式可有效提升其效率。该文基于注意力机制提出一种新的特征融合方案——多特性融合方案，并结合现有的基于边注意的图卷积网络，对从公共化学数据库PubChem中筛选的不同种类的生物活性数据集进行生物活性预测。通过直接学习分子图特征，避免了人工计算特征带来的不稳定性及不可靠性；并且基于注意力的多特性融合方案使得模型可以自适应融合多个边属性特征。经验证，该方法比其他机器学习方法能更准确地预测分子的生物活性。

Prediction of Molecular Biological Activity Based on Graph Convolution Method of Multi-Characteristic Fusion

The development cycle of drugs is long and the cost is huge. The method of computerized virtual drug screening can effectively improve the efficiency of the pilot compounds. This paper proposes a new feature fusion scheme based on attention mechanism, called multi-feature fusion scheme. Combined with the existing graph convolution network based on edge attention, the biological activity prediction task is carried out by using this method for different kinds of bioactive data sets selected from PubChem, the public chemical database. The instability and unreliability caused by manual calculation can be avoided by learning the molecular graph features directly, and multi-feature fusion scheme based on attention makes the model adaptive to fuse multiple edge attribute features. The results show that the method can predict the biological activity of molecules more accurately than other machine learning methods.