"Artificial Intelligence in Oncology Drug Discovery and Development" ed. by John Cassidy, Belle Taylor

This book, authors discusses the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. Authors consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable.


Contents
1.Introduction: An Overview of AI in Oncology Drug Discovery and Development
2.Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design
3.Dimensionality and Structure in Cancer Genomics: A Statistical Learning Perspective
4.Electronic Medical Records and Machine Learning in Approaches to Drug Development
5.Applications of Machine Learning in Drug Discovery II: Biomarker Discovery, Patient Stratification and Pharmacoeconomics
6.Efficacy Evaluation in the Era of Precision Medicine: The Scope for AI
7.AI Enabled Precision Medicine: Patient Stratification, Drug Repurposing and Combination Therapies
8.Toward the Clinic: Understanding Patient Perspectives on AI and Data-Sharing for AI-Driven Oncology Drug Development