IDEA #09I8IM PaxNet: Physics-Informed Multiplex Graph Neural Network for Molecular Structures

The software contains a novel Graph Neural Network (GNN) algorithm to predict the properties of molecules from their 3D structures. As inspired by molecular mechanics, the software decouples the modeling of local and non-local interactions in 3D molecules with multiplex graphs to reduce the expensive angle-related computations. As a result, the software is efficient regarding time and space when applied on both small molecules and macromolecules (e.g. proteins or RNAs). Besides, the software can predict vectorial properties (e.g. dipole moment) besides scalar properties (e.g. energies) by addressing geometric vectors and quantum mechanics. The performance on benchmark datasets demonstrates the state-of-the-art performance of the software as compared to other baselines. The software can be directly used to predict the properties (either scalar or vectorial) of molecules (either micro or macro) given their 3D structures. Since the prediction of molecular properties is fundamental and crucial in molecular discovery process, the software can be used as a building block to learn atom-level or molecule-level embeddings in solving material design and drug discovery problems such as molecule generation, protein design, reaction prediction, retrosynthesis prediction and so on.