AI-Enabled Discovery of Novel Enzymes: Applications in Biotechnology, Food Systems and Green Manufacturing

Timothy Patrick Jenkins

AI-Enabled Discovery of Novel Enzymes: Applications in Biotechnology, Food Systems and Green Manufacturing

Author(s)	Timothy Patrick Jenkins
Country	United States
Abstract	Enzymes provide selective and comparatively mild routes for transforming biological and chemical materials. Their industrial use, however, is limited by the difficulty of identifying catalysts that remain active, selective, stable, and economically viable under realistic processing conditions. Conventional enzyme discovery depends heavily on cultivable microorganisms, sequence similarity, manual screening, and iterative experimentation. These approaches have generated valuable biocatalysts, but they examine only a small proportion of natural sequence diversity and can become expensive when candidate libraries are large. Artificial intelligence offers a complementary strategy by learning relationships among protein sequence, structure, catalytic function, substrate preference, and process conditions. This paper examines an AI-enabled enzyme-discovery framework integrating metagenomic exploration, sequence representation, function prediction, structural modeling, candidate ranking, automated screening, and experimental validation. A structured integrative review is combined with a transparent conceptual simulation. The analysis considers applications in industrial biotechnology, food processing, biomass conversion, polymer degradation, low-temperature manufacturing, and environmentally preferable chemical synthesis. It also examines challenges involving incomplete annotations, biased databases, model interpretability, enzyme–substrate interactions, scale-up, biosafety, intellectual property, and responsible use of environmental genetic resources. A simulated screening portfolio of 20 enzyme candidates is used to illustrate how predicted thermal stability and catalytic activity could support candidate prioritization. The modeled relationship is illustrative and does not represent laboratory measurements. The study finds that AI can reduce an initially vast search space, but computational predictions cannot establish enzyme novelty, activity, safety, or industrial performance without biochemical verification. Reliable discovery therefore requires a closed-loop system in which experimental results continually refine computational models. The paper concludes that the strongest contribution of AI lies not in replacing enzymology but in directing limited experimental resources toward scientifically plausible and industrially relevant candidates while maintaining transparent validation, sustainability assessment, and human oversight.
Keywords	artificial intelligence; biocatalysis; enzyme discovery; enzyme engineering; food biotechnology; green manufacturing; machine learning; metagenomics
Field	Engineering
Published In	Volume 2, Issue 9, September 2026
Published On	2026-09-02

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AI-Enabled Discovery of Novel Enzymes: Applications in Biotechnology, Food Systems and Green Manufacturing

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