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Physics-based framework targets high-throughput enzyme design

A bioRxiv preprint describes a physics-based framework that combines machine learning, molecular mechanics and microkinetic modelling to predict enzyme reaction rates and selectivity. In tests on an oxidative amidase, the authors report near-experimental accuracy across substrates, mutations and enzyme homologues.