A universal framework for accurate and efficient geometric deep learning of molecular systems.

ABSTRACT: Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly

SUBMITTER: Zhang S 

PROVIDER: S-EPMC10628308 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature