Reimagining computational macromolecular modeling: AI-driven approaches

Abstract

Macromolecules, such as proteins, antibodies, nanobodies, and other affinity binders, play essential roles in therapeutic and diagnostic applications due to their high specificity and functionality. Understanding their structure is critical for deciphering their biological activity and drug discovery; however, the inherent complexity of these molecules poses significant challenges. Computational approaches have emerged as powerful tools for modeling macromolecular structures and interactions, offering faster and more cost-effective alternatives to experimental techniques. This review highlights state-of-the-art computational methods used in macromolecule modeling, with a strong focus on artificial intelligence (AI)- and machine learning (ML)-based approaches. Key advanced AI/ML techniques that have revolutionized the field are discussed. We also discuss therapeutic applications of AI/ML approaches and explore how these technologies are transforming drug discovery by accurately predicting macromolecular structures, designing novel therapeutic molecules, modeling protein-protein and protein-drug interactions, estimating binding affinities, and improving cheminformatics analyses. Finally, the review outlines ongoing shortcomings, such as data integration, interpretability, and model validation, and offers perspectives on future directions. We assess the strengths and limitations of each computational approach and present challenges unique to different macromolecule types. By providing a comprehensive overview of current computational strategies, this review serves as a valuable resource for developing innovative approaches in drug development while showcasing the state of the art in computational macromolecular modeling.