ArticleNature communications2026
Multiscale machine learning molecular mechanics for mechanism and stereoselectivity of Diels-Alderase catalysis.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis.Journal of chemical theory and computation · 2026Article
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6 authors.
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Abstract
Enzymes catalyze complex chemical transformations with remarkable efficiency and selectivity, yet their atomistic mechanisms remain challenging to capture because conventional simulations trade accuracy for efficiency. Here we introduce a reactive machine learning/molecular mechanics (ML/MM) framework that bridges quantum chemistry with long-timescale sampling, enabling direct exploration of enzymatic transition states and free-energy landscapes. Coupled with metadynamics, this approach achieves nanosecond sampling of bond-forming reactions and quantitatively predicts activation barriers, mutational effects, and stereoselectivity. Applied to Diels-Alderases, the framework not only reproduces experimental activity and endo/exo preferences with sub-kcal mol
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Registered trials
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