Enzyme property prediction using artificial intelligence
Year: 2025
Type: review
Abstract: Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.
Authors: Le Yuan, Saman Shafaei, Huimin Zhao
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,250
Awards: Molecule Maker Lab Institute (MMLI): An AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing, BioFoundry: NSF iBioFoundry for Basic and Applied Biology, Global Centers: Reliable and Scalable Biofoundries for Biomanufacturing and Global Bioeconomy, CENTER FOR ADVANCED BIOENERGY AND BIOPRODUCTS INNOVATION, AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing: Molecule Maker Lab Institute (MMLI)
OpenAlex ID: W7116675129