A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy.

ABSTRACT: Quantifying intermolecular interactions with quantum chemistry (QC) is useful for many chemical problems, including understanding the nature of protein-ligand interactions. Unfortunately, QC computations on protein-ligand systems are too computationally expensive for most use cases. The flourishing field of machine-learned (ML) potentials is a promising solution, but it is limited by an inability to easily capture long range, non-local interactions. In this work we develop an atomic-pairwise neural network (AP-Net) specialized for modeling intermolecular interactions. This model benefits from a number of physical constraints, including a two-component equivariant message passing neural network architecture that predicts interaction energies via an intermediate prediction of monomer

SUBMITTER: Glick ZL 

PROVIDER: S-EPMC11339967 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature