Navigating Molecular Networks

N. Sukumar

ISBN 10: 3031762894 / ISBN 13: 9783031762895

Editore: Springer Nature Switzerland, Springer Nature Switzerland, 2025

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Druck auf Anfrage Neuware - Printed after ordering - This book delves into the foundational principles governing the treatment of molecular networks and 'chemical space'-the comprehensive domain encompassing all physically achievable molecules-from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein.Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.

Codice articolo 9783031762895

Dati bibliografici

• Titolo: Navigating Molecular Networks
• Editore: Springer Nature Switzerland, Springer Nature Switzerland
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031762894
• ISBN 13: 9783031762895
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 8 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 8 millimetri di profondità
• Peso: 213 grammi

Descrizione articolo

Riassunto

This book delves into the foundational principles governing the treatment of molecular networks and "chemical space"—the comprehensive domain encompassing all physically achievable molecules—from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein.

Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.

Informazioni sull?autore

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.