It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data. There is currently no universal and widely adopted method for robustly representing chemical reactions. Most existing methods suffer from one or more drawbacks, such as: (1) lacking universality; (2) lacking robustness; (3) lacking interpretability; or (4) requiring excessive manual pre-processing. Here we exploit graph-based representations of molecular structures to develop and test a hypergraph attention neural network approach to solve at once the reaction representation and property-prediction problems, alleviating the aforementioned drawbacks. We evaluate this hypergraph representation in three experiments using three independent data sets of chemical reactions. In all experiments, the hypergraph-based approach matches or outperforms other representations and their corresponding models of chemical reactions while yielding interpretable multi-level representations.
翻译:科学和技术必须能够预测化学反应及其特性。为了实现这些技能,必须发展化学反应的良好表现,或能够自动从数据中了解这种表现的良好深层学习结构。目前没有普遍和广泛采用的有力代表化学反应的方法。大多数现有方法都存在一个或多个缺陷,例如:(1)缺乏普遍性;(2)缺乏可靠性;(3)缺乏解释性;或(4)需要过份的人工预处理。我们在这里利用分子结构的图表表示法来开发和测试高射线神经网络方法,以便立即解决反应说明和财产预测问题,减轻上述缺陷。我们利用三种独立的化学反应数据集在三次实验中评估这种高射线表示法。在所有实验中,高射线方法与其他表示法及其相应的化学反应模型相匹配或优于其他表示法及其相应的化学反应模型,同时产生可解释的多层次表示法。