  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.
