  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 model local and non-local interactions
and their combined effects. As a result, PAMNet can reduce expensive
operations, making it time and memory efficient. In extensive benchmark
studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy
and efficiency in three diverse learning tasks: small molecule properties, RNA
3D structures, and protein-ligand binding affinities. Our results highlight the
potential for PAMNet in a broad range of molecular science applications.
