Machine Learning Approaches in Predicting Protein Structure

Abstract

Predicting protein structure is a fundamental problem in computational biology with wide-ranging implications for drug discovery, disease research, and biotechnology. Machine learning has emerged as a powerful tool to address this challenge by enabling the prediction of protein structures with unprecedented accuracy. This article reviews the key machine learning techniques employed in protein structure prediction, including deep learning, convolutional neural networks (CNNs), and reinforcement learning. It explores how these approaches are applied to different aspects of protein structure prediction, such as secondary structure prediction, 3D folding, and protein-ligand interactions. The article also discusses the current challenges in the field and the future directions for improving protein structure prediction using machine learning methods.