CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations

TL;DR

This paper introduces a tool that automates hybrid machine learning and molecular mechanical simulations for studying protein-ligand interactions with high accuracy and efficiency.

Contribution

The novel contribution is the CHARMM-GUI Hybrid ML/MM Builder, which automates setup for hybrid ML/MM simulations using neural network potentials.

Findings

The builder supports TorchANI-AMBER and OpenMM-ML for simulating protein-ligand systems in solution or membrane.

Supported neural network potentials include MACE and ANI, enabling near-quantum mechanical accuracy for ligands.

Application examples demonstrate the tool's usability and effectiveness in hybrid ML/MM modeling.

Abstract

Recent advances in machine learning (ML) have enabled
new developments
in molecular dynamics simulation. Neural network potentials (NNPs)
trained on quantum mechanical (QM) data provide highly accurate descriptions
of drug-like molecules. Analogous to a QM and molecular mechanical
(QM/MM) approach, hybrid ML/MM simulations employ NNPs to describe
a localized region of the system, such as a ligand, while the rest
of the system is treated using classical MM force fields. This hybrid
framework enables simulations of protein–ligand complexes with
near-QM accuracy for the ligand at a substantially reduced computational
cost. CHARMM-GUI Hybrid ML/MM Builder automates the
preparation of system and input files required for hybrid ML/MM modeling
and simulation. This new module generates all necessary files to simulate
protein–ligand complexes in solution or membrane using TorchANI-AMBER
and OpenMM-ML. Currently supported NNPs include MACE and ANI. In this
paper, we present Hybrid ML/MM Builder and representative
application systems that demonstrate its usage and capabilities.

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