QBI presents a seminar with Arun Prasad Pandurangan, Assistant Research Professor in the Department of Medicine and a Teaching Fellow at Gonville and Caius College, University of Cambridge.

Dr. Pandurangan's research focuses on computational methods development that integrates structural bioinformatics, molecular modeling, and computational genomics to study how genetic variation influences protein structure, stability, and function. He has developed freely available computational tools and a large-scale database for proteome-wide structural domain annotation and leads interdisciplinary collaborations applying structure-informed and emerging AI/ML frameworks to advance understanding of health and disease.

Talk Title: From Knowledge-Based Modeling to AI-Guided Discovery: Structure-Informed Insights into Health and Disease

Understanding how molecular structure shapes biological function and dysfunction lies at the heart of biomedical science. Dr. Pandurangan's research explores this relationship through the lens of structural bioinformatics - developing and applying computational frameworks to interpret, predict, and design molecular behavior. In his talk, he will highlight examples from his work on structural modeling, mutation impact prediction, and large-scale genomic annotation, illustrating how structure-guided, knowledge-based approaches have advanced our understanding of molecular mechanisms and informed collaborative experimental studies.

Looking ahead, he is developing strategies that integrate structural and biophysical principles with multi-omics and emerging AI and machine learning methods to enable predictive and design-oriented frameworks for studying health and disease. By combining the interpretability of structure-informed models with the adaptability of AI-driven inference, this approach aims to bridge basic and translational research - fostering collaboration across bioinformatics, biology, chemistry, and medicine.

Hosted by Andrej Sali