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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.
List of contents
The History of Artificial Intelligence and Drug Discovery.- Data Mining and Integration Approaches in AI-driven Drug Discovery.- Artificial Intelligence for Drug Target and Pathway Identification, Assessment, Validation, and Indication Expansion.- Artificial Intelligence in Structure-Based Drug Design.- Artificial intelligence in Ligand-Based drug design.- Artificial Intelligence in De Novo Drug Design.- Artificial Intelligence in Peptide Drug Discovery.- Deep Learning for In Silico ADMET Prediction.- Harnessing Artificial Intelligence to Revolutionize Molecular Modelling and Simulations.- Drug discovery with quantum machine learning.- AI-Driven Discovery of MicroRNA Targets for Disease Therapy and Drug Development.- AI in Retrosynthesis: Introduction, Methods, Evaluation, and Future Directions.- Active Learning in Drug Discovery: Revolutionizing Chemical Space Exploration.- Large Language Models in Drug Discovery.- Contrastive Learning Approaches for Drug Discovery.- Few-shot Learning in Drug Discovery.- Explainable Artificial Intelligence in Drug Discovery.- Federated Learning in Drug Discovery: Challenges, Innovations and Future Directions.- Revolutionizing drug delivery: the role of artificial intelligence in nanomedicine and precision pharma.- Artificial Intelligence-Driven and In Silico Approaches in Health Emergencies: A Case Study on Antiviral Drug Discovery.- Practical and Reproducible AI-driven Modeling Protocols in Drug Discovery.- AI-based Platforms for Drug Discovery: Current Tools and Human-Centered Design Strategies.- AI and ML-Driven Strategies for Drug Repurposing: Tech-niques, Applications, and Challenges.- Artificial intelligence in clinical trials: from protocol design to pharmacovigilance.- Leveraging Generative AI in Clinical Studies to Improve Efficiency and Quality of Drug Development.- AI-Driven Advances in Personalized Therapeutic Strategies for Precision Medicine.- Challenges and Future Directions in Al for Drug Discovery.
About the author
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.
Summary
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.
