Navigating Molecular Networks

N. Sukumar is an Adjunct Professor at the School of Artificial Intelligence, Amrita Vishwa Vidyapeetham University, Coimbatore, India. He was the Founding Head and retired as Professor, Department of Chemistry, and Founding Director, Center for Informatics, at Shiv Nadar University, India. He earned his M.Sc. in Chemistry from the Indian Institute of Technology, Kanpur, and his Ph.D. from the State University of Chemistry at Stony Brook. He completed postdoctoral appointments at the University of Southern California, the University of New Orleans, and Marquette University. Sukumar was also an Alexander von Humboldt Fellow at the University of Bonn, Germany, and served as a visiting scientist at the Wadsworth Institute of the New York State Department of Health. Additionally, he worked as an Associate Research Professor at the Rensselaer Polytechnic Institute in Troy, NY.His research spans quantum chemistry, density functional theory, computational and cheminformatic methods for discovering molecules and materials with specific chemical and biological properties. He specializes in developing novel molecular descriptors and robust property modelling methods for predicting and interpreting protein-ligand binding and protein similarity classification.Currently, Sukumar's active research programs involve drug, polymer, and nanomaterials design through QSAR/QSPR modelling and machine learning. He also explores protein and DNA bioinformatics using structure-based methods and molecular descriptors. His work includes chemical and biological networks, employing graph and network properties to study and design molecular libraries, along with materials design using machine learning and first-principles computations to unveil complex relationships between structure and properties of materials.Sukumar is the editor of A Matter of Density (Wiley, 2012) and co-author of Computational Drug Discovery: A Primer (IonCure Press, 2023), among other book chapters and research papers.

• Molecular Networks.- Transformations of Chemical Space.- Spectral Graph Theory.- Universality and Random Matrix Theory.- Mapping and Navigating Chemical Space Networks.- Generative AI – Growing the Network.- Discovery and Creativity.