The integration of artificial intelligence (AI) into pharmaceutical research has redefined the landscape of drug discovery, enabling unprecedented advances across data integration, molecular design, clinical translation, and therapeutic innovation.
Applied Artificial Intelligence for Drug Discovery is a comprehensive and forward-looking volume that explores how AI, machine learning (ML), and deep learning (DL) are revolutionizing the discovery and development of new drugs. Spanning 27 chapters authored by leading international experts, this book presents state-of-the-art methods and practical applications covering the entire drug discovery pipeline.
Topics include AI-based drug target identification, pathway analysis, structure- and ligand-based drug design, generative models for de novo design, peptide discovery, ADMET prediction, retrosynthesis, drug repurposing, and nanomedicine. Dedicated chapters focus on the implementation of large language models, contrastive and few-shot learning, quantum machine learning, federated and explainable AI, and clinical trial optimization.
With its balance of foundational theory, applied case studies, and emerging perspectives, the book offers a unique resource for computational chemists, pharmaceutical scientists, bioinformaticians, data scientists, and R&D professionals.
This volume serves not only as a scientific reference but also as a strategic guide for those looking to adopt AI in pharmaceutical pipelines and therapeutic development. It is equally suited for academic researchers and industrial innovators seeking to unlock the full potential of AI in healthcare.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
14
113 farbige Abbildungen, 14 s/w Abbildungen
X, 330 p. 127 illus., 113 illus. in color.
ISBN-13
978-3-031-98022-0 (9783031980220)
Schweitzer Klassifikation
Prof. Antonio Lavecchia is Full Professor of Medicinal Chemistry at the University of Naples Federico II (Italy), where he leads the Drug Discovery Laboratory and serves as Scientific Director of the Molecular Modeling Excellence Laboratory (LMM). He received his Ph.D. in Pharmaceutical Sciences from the University of Catania in 1999, completing part of his doctoral research at the University of Minnesota (USA).
With a strong background in both experimental and computational medicinal chemistry, Prof. Lavecchia is internationally recognized for his interdisciplinary expertise in drug design, molecular modeling, and the application of artificial intelligence (AI) in pharmaceutical research. His scientific work spans the development of novel algorithms, AI-based frameworks, and modeling platforms for accelerating the discovery and optimization of bioactive compounds across therapeutic areas such as oncology, metabolic diseases, inflammation, infectious diseases, and rare disorders. His academic output includes over 180 scientific publications in high-impact international journals, five books and book chapters, six patents, and over 280 conference presentations worldwide. He serves on the editorial boards of several international scientific journals and regularly acts as a peer reviewer and expert evaluator for major funding agencies and research institutions worldwide.
Prof. Lavecchia ranks among the world's top 2% of scientists (Stanford University ranking) and is acknowledged as a global expert in PPAR nuclear receptor pharmacology and AI-driven drug discovery. He is co-founder of two biotech spin-offs and has been featured on the covers of J. Chem. Inf. Model. and ACS Omega for his pioneering contributions to AI in drug discovery.
Through this volume, Prof. Lavecchia brings together leading experts in the field to provide a comprehensive, forward-thinking resource that explores the transformative role of AI across the entire drug discovery continuum.
