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

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

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