Applied Artificial Intelligence for Drug Discovery

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Editor: Antonio Lavecchia

ISBN: 9783031980213
Published: January 2026
Format: Hardback
Language: English
Publisher: Springer Cham

Applied Artificial Intelligence for Drug Discovery: From Data-Driven Insights to Therapeutic Innovation brings together leading researchers to map how AI is reshaping every stage of the drug discovery pipeline — from target identification and validation through molecular design, explainability, quantum machine learning, and generative AI in clinical development.

Contents: History of AI and Drug Discovery; AI for Drug Target and Pathway Identification, Assessment, Validation and Indication Expansion; AI-Driven Discovery of microRNA Targets; Drug Discovery with Quantum Machine Learning; AI-Based Platforms for Drug Discovery: Human-Centered Design Strategies; AI-Driven Approaches in Health Emergencies (antiviral drug discovery case study); Explainable AI in Drug Discovery; Leveraging Generative AI in Clinical Studies; Challenges and Future Directions in AI for Drug Discovery.

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Q&A

Who is this book for?
Pharmaceutical scientists, computational chemists, drug discovery researchers and biotech R&D professionals.

What stages of drug discovery does it cover?
The full pipeline — target identification, molecular design, quantum machine learning, explainability and generative AI in clinical development.

Does it include real-world case studies?
Yes, including an antiviral drug discovery case study using AI-driven and in silico approaches during health emergencies.

Does it address explainability?
Yes, a dedicated chapter on explainable AI methods for interpretable drug discovery models.

Who is the editor?
Antonio Lavecchia, bringing together an international team of specialists across computational and medicinal chemistry.

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