AI in biomolecular structural data analysis

Artificial intelligence (AI) is rapidly growing in how researchers study biomolecular structures of proteins and nucleic acids. The use of traditional methods like X-ray crystallography and electron microscopy resolves structural details but these are time-consuming and costly. AI, especially deep learning models, e.g., AlphaFold2 and RoseTTAFold, enables scientists to predict accurate 3D structures quickly from sequence data, opening new ways in understanding molecular functions and drug discovery. This chapter explains key concepts of machine learning and its architectures used in structural biology, including convolutional (CNN) and graph neural networks (GNN), recurrent neural networks (RNN), and transformers. It provides details on how AI helps not only in predicting 3D structures but also in identifying functional sites and interactions, aiding enzyme engineering and therapeutic design towards targets. The experimental data and integration of AI are also discussed, explaining how these techniques complement each other to improve structural determinants. The Challenges faced, like interpretability of the model, data quality, and ethical concerns, are highlighted, along with accessibility for researchers. Overall, this chapter provides a basic overview of the use of AI in biomolecular structural analysis and is designed to equip beginners with the primary knowledge to explore this exciting interdisciplinary field.