Original title: A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
Authors: Shuo Zhang, Yang Liu, Lei Xie
The world of molecular science deals with a vast array of molecules and their complexes. Recently, Graph Neural Networks showed promise in understanding these molecules. But existing methods often favor specific molecular types and struggle with larger tasks, limiting their use. To overcome this, researchers introduce PAMNet, a versatile framework. Inspired by molecular mechanics, PAMNet uses physics-informed insights to understand interactions in molecules of all sizes and types. This approach reduces computational demands, making it faster and more memory-efficient. In tests across various tasks—evaluating small molecule properties, RNA 3D structures, and protein-ligand binding—PAMNet outperforms current methods in both accuracy and speed. This breakthrough suggests PAMNet could revolutionize molecular science, offering a powerful tool applicable to a wide range of real-world problems.
Original article: https://arxiv.org/abs/2311.11228