Enerzyme framework trains electrostatics-aware NNPs on under 1,000 system-specific points to reproduce MTase reaction energetics and transition states for clusters up to 545 atoms.
We combined automated QM-cluster construction, reactive dataset generation, electrostatics-aware NNP modules, and iterative reaction-path exploration
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Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
Enerzyme framework trains electrostatics-aware NNPs on under 1,000 system-specific points to reproduce MTase reaction energetics and transition states for clusters up to 545 atoms.