A Comprehensive Review on AI Applications in Enzyme Catalysis: Databases, Models, and Future Prospects

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool in enzyme catalysis, revolutionizing the way researchers design, predict, and optimize enzymatic reactions. Enzymes play a crucial role in biological systems and industrial processes due to their specificity, efficiency, and eco-friendly nature. However, traditional methods of enzyme discovery and engineering are often time-consuming, labor-intensive, and expensive. The integration of AI technologies, including machine learning, deep learning, and data-driven modeling, has accelerated advancements in enzyme catalysis by enabling rapid prediction, design, and optimization of enzyme performance.
AI-driven approaches facilitate enzyme discovery by analyzing vast biological datasets such as protein sequences, structural information, and functional annotations. Machine learning algorithms can identify patterns and relationships between enzyme structure and function, allowing researchers to predict catalytic activity, substrate specificity, and stability. These predictive models significantly reduce experimental efforts by narrowing down potential enzyme candidates for laboratory validation. Additionally, AI tools like protein structure prediction and molecular docking simulations enhance understanding of enzyme-substrate interactions, further improving catalytic efficiency. It also plays a key role in reaction optimization and process development. Machine learning algorithms can analyze experimental data to determine optimal conditions such as temperature, pH, solvent composition, and substrate concentration. This data-driven optimization enhances catalytic performance while minimizing waste and energy consumption. Furthermore, AI-enabled automation and robotics have enabled high-throughput experimentation, allowing rapid screening of enzyme variants and reaction conditions. Recent advancements in deep learning and computational biology have further expanded AI applications in enzyme catalysis. Tools such as generative models and neural networks enable the design of entirely new enzymes with desired catalytic functions. These innovations open new possibilities for synthetic biology and green chemistry by creating sustainable and efficient biocatalysts. AI-driven enzyme design also contributes to solving global challenges, including climate change, plastic degradation, and renewable energy production.
This paper reviews AI-driven approaches like CNNs, GNNs, and transformers for protein structure prediction, catalytic activity estimation, and pathway optimization. We discuss datasets, model architectures, and case studies in pharma, biofuel, and green chemistry. Challenges like data scarcity and model interpretability are also addressed.