ABSTRACT Today, Artificial Intelligence (AI) has emerged as a transformative force in the field of macromolecular research, fundamentally changing our approach for understanding complex biological structures. This comprehensive review examines the diverse applications of AI in macromolecular studies, ranging from machine learning algorithms to deep learning architectures. It aims to explore how AI facilitates data acquisition, processing, predictive modeling, and structural analysis. By investigating the intersection of AI and macromolecular research, this review highlights significant contributions, challenges, and future opportunities for enhancing our understanding of biological macromolecules. Finally, we provide insights into future research directions and the potential of AI to drive innovation in macromolecular science.
Rani et al. (2026) studied this question.