Department of Pathology and Immunology, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.
Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.
J Phys Chem A. 2021 Oct 14;125(40):8978-8986. doi: 10.1021/acs.jpca.1c04462. Epub 2021 Oct 5.
Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.
计算小分子和聚合物的量子化学性质可以为物理学家、化学家、生物学家在设计新材料、催化剂、生物探针和药物时提供有价值的见解。深度学习可以在通常使用的方法（如密度泛函理论）所需的时间的一小部分内准确计算量子化学性质。量子化学中深度学习的大多数当前方法都从实验得出的分子结构或预先计算的原子坐标的几何信息开始。这些方法有许多有用的应用，但它们在时间和计算资源方面可能代价高昂。在这项研究中，我们证明了通过在坐标自由域中使用图编码进行深度学习，而无需几何信息，就可以进行准确的量子化学计算。坐标自由方法仅依赖于分子图，即原子和键的连接性，而不依赖于原子坐标或键距离。我们还发现，图编码架构的选择对这些方法的性能有很大影响。这些图编码架构的结构为量子力学中的一个重要而悬而未决的问题提供了一个探索机会：哪些类型的量子化学性质可以用局部变量模型表示？我们发现，局部变量模型 Wave 可以准确地计算量子化学性质，而图卷积架构则需要全局变量。此外，局部变量 Wave 模型在具有大型相关系统的复杂分子上优于全局变量图卷积模型。
