ReviewMolecules (Basel, Switzerland)2025
AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.
Review in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
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Who cites it
20 citing papers in PubMed.
- Alkaliphilic microbial cellulases: sources, structural insights, and bioengineering approaches for industrial applications.Archives of microbiology · 2026Review
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Titanium Dioxide Nanoparticle-Driven Metabolic and Molecular Reprogramming in Cyanobacteria.Molecules (Basel, Switzerland) · 2026Review
- Microbial lipases: advances in metagenomics and artificial intelligence for enzyme discovery and engineering.Archives of microbiology · 2026Review
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- Recombinant expression of natural Arabidopsis PRLIP1 variants reveals temperature-dependent differences in fluorescence and functional protein recovery in Escherichia coli.World journal of microbiology & biotechnology · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- The expanding role of protease therapeutics (2012-2026): from replacement therapies to immune system modulation and beyond.The Biochemical journal · 2026Review
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Microbial Enzyme Production: Critical Bottlenecks and Integrated Engineering Solutions.Journal of basic microbiology · 2026Review
- An Archaeal Cyclodextrin Glycosyltransferase fromInternational journal of molecular sciences · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Probiotic and Microbial Enzymatic Mechanisms for PFAS Detoxification.Probiotics and antimicrobial proteins · 2026Review
- Protein engineering and high-throughput screening of lytic polysaccharide monooxygenases: strategies, challenges, and prospects for industrial applications.World journal of microbiology & biotechnology · 2026Review
- Fungal cellulases: a comprehensive review on production, innovations, and applications.Archives of microbiology · 2026Review
- Current Research Advances and Future Prospects on Microbial Consortia for Sustainable PFAS Remediation.International journal of molecular sciences · 2026Review
- Nature-Inspired Enzymatic Cascades: Emerging Strategies for Sustainable Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Smart nanobiocatalysts for waste-to-biofuel conversion: integrating Nano-Bio interfaces and AI-driven design.Frontiers in bioengineering and biotechnology · 2026Review
- Programmable saponin biosynthesis from gene networks to predictive biomanufacturing.Frontiers in plant science · 2026Review
- EnzyDiff: Sequence-based classification of mutation-induced enzyme activity direction using latent diffusion denoising.Science progressArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and optimisation. Generative models, including ProteinGAN and variational autoencoders, enable de novo sequence creation with customised activity, while reinforcement learning enhances mutation selection and functional prediction. Hybrid AI-experimental workflows combine predictive modelling with high-throughput screening, accelerating discovery and reducing experimental demand. These strategies have led to the development of synthetic "synzymes" capable of catalysing non-natural reactions, broadening applications in pharmaceuticals, biofuels, and environmental remediation. The integration of AI-based retrosynthesis and pathway modelling further advances metabolic and process optimisation. Together, these innovations signify a shift from empirical, trial-and-error methods to predictive, computationally guided design. The novelty of this work lies in presenting a unified synthesis of emerging AI methodologies that collectively define the next generation of enzyme engineering, enabling the creation of sustainable, efficient, and functionally versatile biocatalysts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.