Applied Artificial Intelligence for Drug Discovery

Editor

Antonio Lavecchia (Editor)

ISBN: 9783031980213

Published: January 2026

Format: Hardcover

Language: English

Publisher: Springer Cham

Description

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.

Key Features

Covers the full drug discovery pipeline, from target identification to clinical development

Includes a dedicated chapter on quantum machine learning applied to drug design

Addresses explainability and interpretability of AI models in pharmaceutical research

Includes a real-world case study on antiviral drug discovery during health emergencies

Brings together an international team of specialists in computational and medicinal chemistry

Coverage

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; AI-Driven Approaches in Health Emergencies; Explainable AI in Drug Discovery; Generative AI in Clinical Studies; Challenges and Future Directions

About the Authors

Antonio Lavecchia is a professor specializing in computational and medicinal chemistry, editing contributions from an international team of researchers working at the intersection of artificial intelligence and pharmaceutical science.

Table of Contents

1. History of Artificial Intelligence and Drug Discovery; 2. AI for Drug Target and Pathway Identification; 3. AI-Driven Discovery of microRNA Targets; 4. Drug Discovery with Quantum Machine Learning; 5. AI-Based Platforms for Drug Discovery; 6. AI-Driven Approaches in Health Emergencies; 7. Explainable AI in Drug Discovery; 8. Leveraging Generative AI in Clinical Studies; 9. Challenges and Future Directions in AI for Drug Discovery.

Why buy this book?

For medical and pharmaceutical science libraries, this volume is the most current, comprehensive reference connecting AI methods to every stage of drug discovery. For researchers and R&D professionals, it provides applied frameworks — not just theory — for target identification, molecular design and generative AI in clinical development.

Keywords

AI drug discovery, generative AI pharma, quantum machine learning drug design, explainable AI medicine, target identification AI, biotech R&D, pharmaceutical artificial intelligence

Target Audience

Pharmaceutical scientists, computational chemists, drug discovery researchers, biotech R&D professionals, academics

Genre

Medicine, Pharmaceutical Science, Artificial Intelligence

Q&A

How is artificial intelligence used in drug discovery?
AI supports target identification, molecular design, quantum machine learning-based screening, and generative AI applications throughout clinical development.

What is explainable AI in the context of drug discovery?
It refers to AI methods designed to be interpretable, helping researchers understand why a model predicts a given molecule's activity or safety profile.

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

What is quantum machine learning's role in drug design?
It's an emerging computational approach explored in a dedicated chapter for accelerating molecular property prediction and screening.

Who edited this book?
Antonio Lavecchia, bringing together an international team of specialists in computational and medicinal chemistry.

Where can I buy Applied Artificial Intelligence for Drug Discovery?
You can order this title directly from CLNZ Books, with worldwide delivery via international courier.

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