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

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.