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[null,null,"Autore",null,"Lavecchia Antonio"]
[null,null,"Titolo",null,"Applied Artificial Intelligence for Drug Discovery : From Data-Driven Insights to Therapeutic Innovation / / edited by Antonio Lavecchia"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026"]
[null,null,"ISBN",null,"3-031-98022-0"]
[null,null,"Edizione",null,"[1st ed. 2026.]"]
[null,null,"Descrizione fisica",null,"1 online resource (1128 pages)"]
[null,null,"Collana",null,"Biomedical and Life Sciences Series"]
[null,null,"Disciplina",null,"615"]
[null,null,"Soggetti",null,"Pharmacology"]
[{"$nulls":4},"Pharmacy"]
[{"$nulls":4},"Drug delivery systems"]
[{"$nulls":4},"Drug Delivery"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"The History of Artificial Intelligence and Drug Discovery -- Data Mining and Integration Approaches in AI-driven Drug Discovery -- Artificial Intelligence for Drug Target and Pathway Identification, Assessment, Validation, and Indication Expansion -- Artificial Intelligence in Structure-Based Drug Design -- Artificial intelligence in Ligand-Based drug design -- Artificial Intelligence in De Novo Drug Design -- Artificial Intelligence in Peptide Drug Discovery -- Deep Learning for In Silico ADMET Prediction -- Harnessing Artificial Intelligence to Revolutionize Molecular Modelling and Simulations -- Drug discovery with quantum machine learning -- AI-Driven Discovery of MicroRNA Targets for Disease Therapy and Drug Development -- AI in Retrosynthesis: Introduction, Methods, Evaluation, and Future Directions -- Active Learning in Drug Discovery: Revolutionizing Chemical Space Exploration -- Large Language Models in Drug Discovery -- Contrastive Learning Approaches for Drug Discovery -- Few-shot Learning in Drug Discovery -- Explainable Artificial Intelligence in Drug Discovery -- Federated Learning in Drug Discovery: Challenges, Innovations and Future Directions -- Revolutionizing drug delivery: the role of artificial intelligence in nanomedicine and precision pharma -- Artificial Intelligence-Driven and In Silico Approaches in Health Emergencies: A Case Study on Antiviral Drug Discovery -- Practical and Reproducible AI-driven Modeling Protocols in Drug Discovery -- AI-based Platforms for Drug Discovery: Current Tools and "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","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."]