Artificial Intelligence in Predicting Protein Structures

Abstract

Accurately predicting protein structures is a cornerstone of molecular biology, essential for drug discovery, disease modeling, and synthetic biology. Traditional computational approaches often fall short due to the complexity of protein folding and the vast conformational space involved. Recent advances in artificial intelligence (AI), particularly deep learning models like AlphaFold and RoseTTAFold, have revolutionized protein structure prediction by achieving near-experimental accuracy. This article explores the evolution, methodologies, and implications of AI-driven protein modeling. We highlight the role of convolutional neural networks (CNNs), transformers, and attention mechanisms, and review current applications, challenges, and future directions in integrating AI into structural bioinformatics