The article "Navigating the Landscape of Enzyme Design: From Molecular Simulations to Machine Learning" by Jiahui Zhou and Meilan Huang reviews the advancements in enzyme design, focusing on structure-based and machine-learning guided approaches. The authors highlight the challenges in navigating the vast sequence space to identify proteins with desirable biocatalytic functions and discuss how deep-learning-based structure prediction methods, such as AlphaFold2, have expanded the 3D structure databases and enabled structure-based design. However, these methods are not suitable for large-scale screening of potential biocatalysts. Machine learning techniques, particularly in big data utilization, offer a new era for accelerated predictions. The review also covers traditional modeling methods, molecular dynamics simulations, and quantum mechanics and multiscale simulations, detailing their applications in enzyme design. The authors provide insights into the challenges and perspectives of integrating traditional molecular simulations with machine learning to effectively employ enzyme design approaches, emphasizing the importance of database construction and algorithm development in achieving predictive ML models for exploring the sequence fitness landscape.The article "Navigating the Landscape of Enzyme Design: From Molecular Simulations to Machine Learning" by Jiahui Zhou and Meilan Huang reviews the advancements in enzyme design, focusing on structure-based and machine-learning guided approaches. The authors highlight the challenges in navigating the vast sequence space to identify proteins with desirable biocatalytic functions and discuss how deep-learning-based structure prediction methods, such as AlphaFold2, have expanded the 3D structure databases and enabled structure-based design. However, these methods are not suitable for large-scale screening of potential biocatalysts. Machine learning techniques, particularly in big data utilization, offer a new era for accelerated predictions. The review also covers traditional modeling methods, molecular dynamics simulations, and quantum mechanics and multiscale simulations, detailing their applications in enzyme design. The authors provide insights into the challenges and perspectives of integrating traditional molecular simulations with machine learning to effectively employ enzyme design approaches, emphasizing the importance of database construction and algorithm development in achieving predictive ML models for exploring the sequence fitness landscape.