Pseudodata-Guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction
DENet provides a general and effective strategy to enhance the stability and deployability of data-driven enzyme kinetics predictors for real-world enzyme engineering applications.
Authors
- Zhiwei Nie (ORCID: https://orcid.org/0000-0002-2781-5248)
- Zhixiang Ren (ORCID: https://orcid.org/0000-0002-4104-3790)
- Haitao Zhang (ORCID: https://orcid.org/0000-0002-1639-4089)
- Haomin Wu (ORCID: https://orcid.org/0009-0005-4001-4348)
Institutions
- Peking University (CN)
- Southern University of Science and Technology (CN)
- Peng Cheng Laboratory (CN)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2026-04-27
- DOI
- https://doi.org/10.1021/acs.jcim.5c03204
- Primary Topic
- Protein Structure and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00