Applied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation

Cover
Antonio Lavecchia
Springer Nature, 09.01.2026 - 842 Seiten

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.

 

Ausgewählte Seiten

Inhalt

History of Artificial Intelligence and Drug Discovery
1
Data Mining and Integration Approaches in AIDriven DrugDiscovery
21
Artificial Intelligence for Drug Target and Pathway Identification Assessment Validation and Indication Expansion
73
Artificial Intelligence in StructureBased Drug Design
105
Artificial Intelligence in LigandBased Drug Design
130
Artificial Intelligence in De Novo Drug Design
157
Artificial Intelligence in Peptide Drug Discovery
187
Deep Learning for In Silico ADMET Prediction
213
FewShot Learning in Drug Discovery
497
Explainable Artificial Intelligence in Drug Discovery
525
Challenges Innovations and Future Directions
556
The Role of Artificial Intelligence in Nanomedicine and Precision Pharma
583
A Case Study on Antiviral Drug Discovery
602
Practical and Reproducible AIDriven Modeling Protocols in Drug Discovery
623
Current Tools and HumanCentered Design Strategies
655
Techniques Applications and Challenges
689

Harnessing Artificial Intelligence to Revolutionise MolecularModelling and Simulations
239
Drug Discovery with Quantum Machine Learning
289
AIDriven Discovery of microRNA Targets for Disease Therapy and Drug Development
344
Introduction Methods Evaluation and Future Directions
379
Revolutionizing Chemical Space Exploration
409
Large Language Models in Drug Discovery
436
Contrastive Learning Approaches for Drug Discovery
469
From Protocol Design to Pharmacovigilance
737
Leveraging Generative AI in Clinical Studies to Improve Efficiency and Quality of Drug Development
761
AIDriven Advances in Personalized Therapeutic Strategies for Precision Medicine
788
Challenges and Future Directions in Al for Drug Discovery
817
Index
839
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Applied Artificial Intelligence for Drug Discovery: From Data-Driven ...
Antonio Lavecchia
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accuracy activity ADMET advancements AI-driven algorithms analysis applications approach artificial intelligence arXiv autoencoders bioactivity Bioinformatics biological biomedical cancer challenges Chem Chem Inf Model ChEMBL chemical cheminformatics Chen classification clinical trials complex compounds database datasets deep learning descriptors disease drug design drug development Drug Discov drug discovery drug repurposing drug-target effects efficiency embeddings enabling encoding enhance ensuring evaluation experimental extraction few-shot framework function gene genomic GNNs https://doi.org identify improve inhibitors input integration interactions language models Lavecchia learning models leveraging ligands LLMs machine learning methods miRNAs molecular docking molecular structures molecules multiple novel optimization pathways patient peptides performance pharmaceutical platform potential pre-training protein QSAR quantum circuit quantum computing qubits random forest retrosynthetic samples sequences simulations SMILES specific strategies target tasks techniques text mining therapeutic toxicity transformer users validation vector virtual screening Wang Zhang

Autoren-Profil (2026)

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.

Bibliografische Informationen

TitelApplied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation
Biomedical and Life Sciences
Biomedical and Life Sciences (R0)
HerausgeberAntonio Lavecchia
VerlagSpringer Nature, 2026
ISBN3031980220, 9783031980220
Länge842 Seiten
  
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