Jun 2026· Catalysts· Vol 16, pp. 598· 0 citations· 143 references
TL;DR
This review provides a comprehensive overview of ML-guided strategies to improve key enzymatic parameters, including the turnover number, substrate affinity, and catalytic efficiency, with a focus on mechanistic insights and performance outcomes.
Abstract
Biocatalysis has emerged as a mainstay in the field of sustainable chemical synthesis owing to its high selectivity, mild reaction conditions, and reduced environmental impact. Traditional enzyme engineering approaches, such as rational design and directed evolution, are often associated with limited throughput and a limited understanding of sequence–structure–function relationships, despite high experimental costs. In recent years, the integration of machine learning (ML) into enzyme engineering has emerged as a transformative approach, enabling data-driven prediction, design, and optimization of biocatalysts, thereby enhancing performance and applications. This review provides a comprehensive overview of ML-guided strategies to improve key enzymatic parameters, including the turnover number (kcat), substrate affinity (Km), and catalytic efficiency (kcat/Km), with a focus on mechanistic insights and performance outcomes. The integration of ML models into design–build–test–learn (DBTL) cycles accelerated directed evolution, reduced screening efforts, and enabled targeted mutagenesis. Beyond applications, this review also discusses the current limitations of ML-guided approaches, including data scarcity, model interpretability, and challenges in predicting complex mutations and allosteric effects. The gap between computational predictions and experimental outcomes is identified, and the role of ML integration with enzyme kinetics, molecular dynamics, and high-throughput experimentation is emphasized. Future directions, such as generative AI, explainable ML, and autonomous laboratories, are discussed for next-generation biocatalytic applications.
This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.
Mati Ullah, Muhammad Rizwan, Vivian Andoh et al.· Journal of Agricultural and...· 0 citations
Enzyme enantioselectivity remains a central challenge in the biocatalytic synthesis of chiral pharmaceuticals and fine chemicals. Conventional evolution methods, constrained by prolonged experimental cycles and computationally intensive processes, struggle to comprehensively explore complex protein sequences. Although machine learning (ML) has been successfully applied to optimize enzymatic properties, such as catalytic efficiency and stability, its potential in enantioselectivity engineering remains underexploited. This review systematically evaluates how machine learning integrates multimodal datasets, including sequence, structural, and functional performance data, to advance the discovery and engineering of naturally enantioselective enzymes, and enable the de novo design of artificial enzymes for reaction-relevant applications. Furthermore, key algorithmic frameworks are reviewed. Persistent challenges such as data heterogeneity and limited model generalization capabilities are also critically examined. Moreover, machine learning is increasingly expected to bridge molecular-level enantioselective design with pathway optimization and reactor-scale process engineering, enabling a more integrated and efficient chiral biomanufacturing pipeline. Overall, these advancements open new avenues for efficient chiral biosynthesis and provide theoretical foundations and technical blueprints for a paradigm shift toward intelligence-driven biocatalysis engineering.
Jie Gu, Yan Xu, Xiaoyan Sun et al.· ACS Synthetic Biology· 0 citations
Results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope, and suggest that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope.
Ravi G. Lal, Jason Yang, Ziyan Zhang et al.· bioRxiv· 0 citations
Bioelectrochemical systems (BES) represent an interdisciplinary convergence of biology, electrochemistry, materials science, environmental engineering and mechanical engineering, offering transformative potential for renewable energy generation, wastewater treatment and resource valorization. However, the inherent structural intricacy and mechanistic complexity of BES pose significant challenges to system understanding and optimization. With robust capabilities in pattern recognition and nonlinear system modeling, machine learning (ML) appears to be a good approach to decipher the complex mechanisms of BES. A systematic literature review reveals that ML applications in BES date back to 2006, with a marked surge around 2021, reflecting the growing research interest in this interdisciplinary field. The application domains primarily fall into four categories: (1) analysis and prediction of microbial communities, (2) intelligent design of system components, (3) performance prediction and system optimization, and (4) real-time monitoring and assisted intelligent control. Among these, performance prediction and system optimization constitute the dominant application area, and model interpretability and generalization are cross-cutting requirements for reliable and transferable ML deployment in BES. Among the BES subtypes that have employed ML, microbial fuel cells (MFC) account for the largest share (42.4%), followed by electrochemical biosensor (EB, 37.6%) and microbial electrolysis cells (MEC, 10.9%), with other types occupying smaller proportions. Regarding algorithmic choices, artificial neural networks are the most frequently used method (30.9%), followed by support vector machines or support vector regression (17.3%), principal component analysis (14.5%), and regression trees (13.1%). ML has exhibited significant potential in elevating BES design, manufacture, operation and application. However, constrained by data scarcity and heterogeneity, present models are with limited transferability across scales. Further efforts are warranted to promote the application of ML in BES by expanding data accumulation, diversifying datasets, and developing targeted models. This will ultimately enable a deeper understanding, enhanced optimization and broader deployment of BES.
The Oxygen Evolution Reaction (OER) is a fundamental process in electrochemical water splitting, playing a crucial role in sustainable hydrogen production. However, its intrinsically sluggish kinetics, involving complex four-electron transfer steps, remain a major bottleneck for efficient energy conversion. In recent years, Machine Learning (ML) has emerged as a powerful approach to accelerate catalyst discovery by enabling data-driven prediction of OER activity and reducing reliance on costly experimental and density functional theory (DFT) calculations. This review systematically summarizes recent advances in ML-assisted OER research, focusing on key aspects including dataset construction, descriptor engineering, model development, and performance evaluation. Various ML techniques, ranging from traditional algorithms such as Random Forest and Support Vector Machines to advanced deep learning approaches, are critically discussed in the context of catalyst screening and activity prediction. Particular attention is given to the role of physicochemical descriptors, including adsorption energies and electronic structure parameters, in governing model performance and interpretability. Furthermore, this review highlights current challenges, such as data scarcity, lack of standardization, and limited model generalization, while discussing emerging trends including active learning, explainable AI, and integration with high-throughput simulations. By providing a comprehensive overview, this work aims to guide future research toward the development of robust, interpretable, and scalable ML frameworks for accelerating the discovery of efficient OER catalysts.
Wise Herowati, Muhamad Akrom, T. Sutojo et al.· Journal of Multiscale Materi...· 0 citations
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations