MolNet-3D: Deep Learning of Molecular Representations and Properties from 3D Topography

A new paradigm combining quantum-mechanical calculations with machine learning (ML) to rationally design compounds with specific properties from extremely large chemical space is emerging and developing at a rapid pace. In this context, appropriately describing molecules and efficiently extracting patterns from electronic-structure calculations are the core challenges for the success of the quantum-mechanics-based ML approaches. Here, MolNet-3D is introduced, a strong deep learning architecture capable of mapping from a flexible and universal 3D topography descriptor to quantum-mechanical observables of molecules of arbitrary shape. The model can learn an invariant representation without the need for the transformation of atom coordinates into interatomic distances, thus preserving the intrinsic 3D topography information of molecules. The capabilities of MolNet-3D are shown by accurately predicting the various density functional theory calculated properties for molecules, including energetic, electronic, and thermodynamic properties. Compared with the previously proposed ML methods in the MoleculeNet benchmarks, their model generally offers the best performance in those quantum-mechanical tasks, elucidating the importance of intrinsic topography information in molecular representation learning. This work may provide new insight into the construction of molecular ML models from 3D topography recognition perspectives.