8d ago
Beyond benchmark accuracy: machine-learning turnover-number predictors require system-level validation
Rimon Martinez · M. J. · Lottermoser · J. · Bouillon · A. T. C. · Vranken · W. F. · +4 more
Abstract
Enzyme turnover numbers (kcat) are essential for kinetic models and enzyme-constrained genome-scale metabolic models (ecGEMs), but measured values are sparse and therefore increasingly estimated using machine learning (ML). Although these predictors are commonly evaluated by global regression metrics, their practical utility depends on how errors propagate through downstream models. We benchmarked six current kcat predictors on a curated BRENDA-derived dataset and five of them on EnzyExtract. To
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