Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
importance 6/10
ai assisted
Machine Learning in Bioinformatics enzyme discovery stage: research
Problem
Finding useful enzymes with desired functions among vast numbers of uncharacterized proteins is slow and difficult.
Why it matters
Biocatalysts underpin medicine and industry, so slow discovery limits new applications.
What the AI did
Machine learning models enable high-throughput prediction of enzyme functions (EC numbers, GO terms, substrate specificity) and catalytic properties such as kinetics, optimal temperature and pH.
Results
not stated in source
Impact
ML is accelerating enzyme discovery, but data scarcity, generalizability and interpretability limits remain.
Record
| Venue | ACS Catalysis |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Felix Moorhoff, Yanzi Zhang, Sizhe Qiu, Wenjuan Dong, David Medina-Ortiz, Jing Bo Zhao, Mehdi D. Davari |
| Citations | 35 (OpenAlex, at import time) |
| References | 180 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1021/acscatal.5c04814 |
| Open access | hybrid |
| Keywords | machine learning, enzyme mining, biocatalysis, protein function prediction |
Importance rating
6
significant — thousands to millions affected
Enzyme discovery spans biotechnology, biocatalysis, and synthetic biology; abstract claims ML accelerates discovery but gives no scale, metrics, or quantified impact.