AI-Driven Protein Design @natrevbioeng 1. This review article explores the transformative impact of artificial intelligence on protein engineering. It highlights how AI is revolutionizing the field by enabling unprecedented precision and speed in designing novel proteins with tailored functions. 2. The article presents a comprehensive roadmap for integrating AI tools into protein design workflows. It categorizes these tools into seven distinct toolkits, each supporting different tasks throughout the design process, from initial concept to experimental validation. 3. One of the key innovations discussed is the use of deep learning models like AlphaFold 2, which can predict protein structures with near-experimental accuracy. This breakthrough significantly accelerates the design process by reducing the need for extensive experimental trials. 4. The review also delves into the application of AI in both directed evolution and rational design strategies. Directed evolution mimics natural selection through iterative cycles of mutagenesis and screening, while rational design leverages detailed structural and mechanistic knowledge to guide modifications. 5. Case studies are provided to illustrate the practical application of AI-driven protein design. These examples range from engineering therapeutic proteins to unlocking novel enzyme functions and reprogramming biomolecular systems, showcasing the vast potential of AI in synthetic biology. 6. Looking ahead, the article outlines future directions, emphasizing the potential of AI to expedite drug development, drive sustainable biotechnology, and expand the frontiers of synthetic biology. It positions AI as a transformative force at the forefront of protein engineering. 7. The review underscores the importance of robust data foundations and interpretability in AI tools. It calls for the development of explainable AI methods to elucidate the decision-making processes behind in silico designs, fostering greater adoption and trust in the field. 📜Paper: nature.com/articles/s44222-0… #AIDrivenProteinDesign #Bioengineering #ProteinEngineering #SyntheticBiology #DrugDiscovery #DeepLearning