图卷积神经网络作为“通用”属性预测器：适用性的普遍性和局限性
Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
摘要：
利用机器学习（ML）技术开发新的功能材料/化合物是当今的一个热门话题，包括几个关键步骤，其中之一是化学结构表示的选择。ML中严格特征工程的经典方法通常提高了预测模型的性能，但同时，它缩小了适用范围，降低了预测结果的物理可解释性。在这项研究中，我们提出了图卷积神经网络（GCNN）作为一种架构，可以使用最小的一组有意义的描述符，成功地预测来自化学空间不同领域的化合物的性质。GCNN模型的适用性已被广泛的化学领域特定的属性所证明。它们的性能可与最先进的技术相媲美;然而，这种架构免除了执行精确特征工程的需要。
英文摘要：
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. Classical approach of rigorous feature engineering in ML typically improves the performance of the predictive model, but at the same time, it narrows down the scope of applicability and decreases the physical interpretability of predicted results. In this study, we present graph convolutional neural networks (GCNN) as an architecture that allows to successfully predict the properties of compounds from diverse domains of chemical space, using a minimal set of meaningful descriptors. The applicability of GCNN models has been demonstrated by a wide range of chemical domain-specific properties. Their performance is comparable to state-of-the-art techniques; however, this architecture exempts from the need to carry out precise feature engineering.
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