ArticleJournal of the American Chemical Society2025
Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the S
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.ACS catalysis · 2026Article
- What Determines the Directionality of Kinesins? Studying the Anomalous Directionality of Nonclaret Disjunctional Motor Mutants.Journal of the American Chemical Society · 2026Article
- WatCon: A Python Tool for Analysis of Conserved Water Networks Across Protein Families.JACS Au · 2025Article
- Exploring evolutionary trajectories of drug resistance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- Decoding Enzymatic Dechlorination with Multiscale Modeling: Mechanistic Insights into Native Haloalkane Dehalogenase fromACS catalysis · 2025Article
- Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics.JACS Au · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the S
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.