Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology. In this work, we propose a deep neural network architecture for atom type embeddings in its molecular context and interatomic potential that follows fundamental physical laws. The architecture is applied to predict physicochemical properties in heterogeneous systems including solvation in diverse solvents, 1-octanol-water partitioning, and PAMPA with a single set of network weights. We show that our architecture is generalized well to the physicochemical properties and outperforms state-of-the-art approaches based on quantum mechanics and neural networks in the task of solvation free energy prediction. The interatomic potentials at each atom in a solute obtained from the model allow quantitative analysis of the physicochemical properties at atomic resolution consistent with chemical and physical reasoning. The software is available at https://github.com/SehanLee/C3Net.

翻译：理解溶质与其周围环境的相互作用是化学与生物学中的基本问题。本文提出一种深度神经网络架构，用于在分子语境中嵌入原子类型及其遵循基本物理定律的原子间势。该架构通过单一网络权重集，应用于异质系统的物理化学性质预测，包括多种溶剂中的溶剂化、1-辛醇-水分配系数及PAMPA。结果表明，本架构对物理化学性质具有良好的泛化能力，并在溶剂化自由能预测任务中优于基于量子力学和神经网络的最新方法。模型获得的溶质各原子原子间势，可定量分析原子尺度的物理化学性质，且结果与化学和物理推理一致。软件开源地址：https://github.com/SehanLee/C3Net。