Chapter 1 Transforming Pharma with Data Science, AI and Machine LearningChapter 2 Regulatory Perspective on Big Data, AI, and Machining LearningChapter 3 Building an Agile and Scalable Data Science OrganizationChapter 4 AI and Machine Learning in Drug DiscoveryChapter 5 Predicting Anti-Cancer Synergistic Activity Through Machine Learning and Natural Language ProcessingChapter 6 AI-Enabled Clinical TrialsChapter 7 Machine Learning for Precision MedicineChapter 8 Reinforcement Learning in Personalized MedicineChapter 9 Leveraging Machine Learning, Natural Language Processing, and Deep Learning in Drug Safety and PharmacovigilanceChapter 10 Intelligent Manufacturing and Supply of BiopharmaceuticalsChapter 11 Reinventing Medical Affairs in the Era of Big Data and AnalyticsChapter 12 Deep Learning with Electronic Health RecordChapter 13 Real-World Evidence for Treatment Access and Payment Decisions

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

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Harry Yang, Ph.D., is the Vice President and Head of Biometrics at Fate Therapeutics. He has 27 years of experience across all aspects of drug R & D, from early target discovery, through pre-clinical, clinical, translational science, and CMC programs to regulatory approval and post-approval lifecycle management. He played a pivotal role in the successful submissions of 5 biologics license appllications (BLAs) that ultimately led to marketing approvals of five biological products. He has published 8 statistical and data science books, 28 book chapters, over 100 peer-reviewed articles, and 3 industry white papers on diverse scientific, statistical, and data science subjects. He is a frequent invited speaker at national and international conferences. He has also developed statistical courses and conducted trainings at the United States Food and Drug Administration (FDA) and United States Pharmacopeia (USP).

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