AI-Driven Functional Protein Design: Machine Learning Approaches for Screening and Optimization

Recent machine learning advances enable data-driven protein design from prediction to optimization. Key challenges persist, stability-function tradeoffs, and the expense of experimental validation. AI addresses these issues via prediction, screening, and optimization. This review examines strategies like structure-based reverse design and emerging trends including multimodal integration and AI-experiment feedback loops. In conclusion, AI-driven protein design holds promise for surpassing traditional limitations, paving the way for more efficient and precise biotechnological innovations.