AI-Driven Protein Discovery: Bridging Computational Innovation and Therapeutic Advancements

Artificial intelligence (AI) is revolutionizing the science of protein discovery dramatically, making tremendous progress in drug discovery, protein structure prediction, and precision medicine. The current study examines the crucial role of AI, i.e., via machine learning (ML) and deep learning (DL), in transforming our knowledge, modeling, and use of proteins in therapeutic design. Such breakthrough frameworks as AlphaFold and RosettaFold have shown AI's unmatched ability to predict protein structures at experimental accuracy, minimizing time and expense associated with conventional means. Furthermore, AI's assistance in the discovery of protein-protein interactions (PPIs) and drug repurposing has presented novel avenues to boost drug discovery and enhance current drugs. The combination of multi- omics data has augmented the contribution of AI to personalized medicine to facilitate the design of protein-based therapies for individual patients to optimize therapeutic effects. Nevertheless, issues such as data quality, model interpretability, and constraints on computing power continue to hinder the extensive use of AI in protein discovery. Ethical considerations, such as data protection and transparency of AI models in clinical environments, need to be addressed with caution. Future work needs to focus on enhancing model interpretability, enriching dataset quality, and promoting the use of AI models with conventional experimental frameworks to unlock AI's transformative potential in the battle against disease and the expansion of precision medicine.