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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.
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