# Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications

## TL;DR

This paper reviews how artificial intelligence is revolutionizing protein structure prediction, enabling faster and more accurate results for applications in drug discovery and disease research.

## Contribution

The paper provides a comprehensive overview of recent AI-driven advances in protein structure prediction and their practical applications.

## Key findings

- AI models like AlphaFold3 and RoseTTAFold achieve near-experimental accuracy in protein structure prediction.
- Single-sequence methods such as ESMFold improve speed and scalability of predictions.
- AI-driven methods are being applied to drug discovery, enzyme engineering, and disease research.

## Abstract

The three-dimensional structure of a protein underpins its biological function, making structure determination and prediction central challenges in structural biology. Although experimental techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryo-EM) can yield high-resolution structures, they are limited by low throughput, high cost, and demanding sample preparation. Likewise, traditional computational methods often perform poorly in the absence of homologous templates or under complex folding dynamics. Recent advances in deep learning and large-scale protein language models have transformed protein structure prediction. Models such as AlphaFold3 and RoseTTAFold achieve near-experimental accuracy by integrating evolutionary information, geometric constraints, and end-to-end neural architectures, while single-sequence approaches such as ESMFold offer substantial gains in speed and scalability. This review summarizes the biochemical foundations of protein folding, recent AI-driven methodological advances, and representative applications in drug discovery, enzyme engineering, and disease research, and discusses current challenges and future directions.