Ai-Driven Protein Design

AI-driven protein design is revolutionizing the way scientists understand, engineer, and create proteins for a wide range of applications in biotechnology, medicine, and beyond. Advances in artificial intelligence—including deep learning, generative models, and data-driven optimization—have enabled researchers to move beyond traditional trial-and-error methods, instead allowing for the rational and accelerated design of proteins with specific structural, catalytic, or therapeutic properties.

This research topic seeks to showcase pioneering work at the intersection of computational intelligence and protein engineering. Submissions are encouraged that present novel AI algorithms for predicting protein structure and function, generative approaches for de novo design of protein sequences, and innovative models for understanding and engineering protein-ligand and protein-protein interactions. We are also interested in studies leveraging large-scale biological datasets, integrating omics data streams, and utilizing reinforcement learning or unsupervised methods to discover new protein functionalities.

Papers addressing practical applications—such as AI-driven antibody engineering, enzyme optimization for industry or sustainability, and protein-based therapeutics—are especially welcome, as are works that discuss challenges in the field, such as data curation, interpretability of AI models, and the ethical implications of widespread use of AI in protein design.

By bringing together research articles, reviews, methods papers, and perspectives, this research topic aims to highlight state-of-the-art advances, identify current limitations, and foster collaboration across diverse scientific communities. Our goal is to illuminate how AI-driven protein design is shaping the future of bioinformatics, accelerating discovery, and translating computational innovations into real-world solutions.