Simulating enzyme catalysis with electrostatically embedded machine learning potentials

Key Points

• The aim is to accurately simulate enzyme catalysis through advanced machine learning techniques.
• Coupling machine-learned potentials trained on gas-phase data with the environment using the ML/MM framework.
• Applying electrostatic machine-learning embedding to enhance simulation accuracy and efficiency.
• Achieved accurate simulations of enzyme catalysis that outperform traditional methods.
• Enhanced efficiency in computational resources while maintaining precision.