Applications of Machine Learning in Enzyme Design

Dear Colleagues,

Enzymes are the workhorses of biocatalysis across the fields of food, pharmaceuticals, chemicals, agriculture, and environmental remediation. However, rationally engineering enzymes to improve activity, specificity, stability, and novel functions remains challenging due to the complexity of sequence–structure–function relationships and the cost of iterative experimentation. Recent advances in machine learning (ML)—including protein language models, structure predictors, and generative design—are reshaping this landscape. By leveraging large-scale sequence and structural data, ML enables rapid property prediction, the prioritization of beneficial mutations, the efficient navigation of sequence space, and data-driven design from scratch, thereby accelerating the design–build–test–learn cycle.

Despite these advances, important challenges persist, such as improving data quality and curation and label sparsity and assay bias, the generalization of models to new scaffolds and conditions, and the seamless integration of ML predictions into automated high-throughput experimentation and mechanistic modeling. Addressing these bottlenecks requires interdisciplinary collaboration across the fields of computational biology, protein engineering, structural biology, and synthetic biology.

To further advance this field, we invite the submission of original research articles and comprehensive reviews on the application of machine learning in enzyme design. Topics of interest include, but are not limited to, the following themes:

• Novel ML algorithms and frameworks for enzyme property prediction;
• Data curation, representation, and augmentation for enzyme design;
• Explainable AI and model interpretability in enzyme engineering;
• Structure-guided and generative design of enzymes using ML;
• ML-driven screening and optimization in directed evolution;
• The integration of ML into high-throughput experimental platforms;
• Case studies and practical applications of ML-designed enzymes in the food industry and healthcare.

We look forward to receiving your valuable contributions and advancing the frontiers of enzyme design together.

Prof. Dr. Fufeng Liu
Guest Editor

Dr. Xingmiao Lu
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• biocatalysis
• enzyme design
• computational-aided enzyme design
• AI-driven enzyme design
• AI-driven directed evolution
• enzyme structure prediction
• enzyme function prediction
• large language model