This volume surveys cutting-edge machine learning–based approaches for the study of enzyme catalysis, dynamics, and design. It brings together computational and experimental approaches that integrate molecular simulation, machine learning, and high-throughput data to interrogate enzymatic catalysis at multiple scales. The chapters in the volume highlight how data-driven frameworks complement physics-based models to advance mechanistic understanding and enable rational enzyme engineering.

• Discussion of cutting-edge machine learning approaches for both computational and experimental studies of enzymes.
• Integration of physics-based simulations with data-driven models to characterize enzymatic mechanisms.
• Methodological frameworks for enzyme discovery and rational protein engineering.