Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical–chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

中文翻译：

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建立注意力和边缘消息传递神经网络，以进行生物活性和理化性质预测

图形的神经信息传递是一种将机器学习应用于网络数据的有前途且相对较新的方法。由于分子可以本质上描述为分子图，因此应用这些技术来改善化学信息学领域的分子特性预测是有意义的。我们在现有的消息传递神经网络框架中引入了注意力和边缘记忆方案，并针对文献中的八个不同的理化和生物活性数据集对我们的方法进行了基准测试。我们不再需要仅使用基本的图派生特性来介绍任务和化学描述符计算的先验知识。我们的结果与其他最新的机器学习方法一致地达到了标准，并为稀疏的多任务虚拟筛选目标设定了新的标准。