The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry
eBook - ePub

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

  1. 264 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

About this book

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

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Yes, you can access The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry by Stephanie K. Ashenden in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. Acknowledgments and conflicts of interest
  8. List of Illustrations
  9. List of Tables
  10. Chapter 1 : Introduction to drug discovery
  11. Chapter 2 : Introduction to artificial intelligence and machine learning
  12. Chapter 3 : Data types and resources
  13. Chapter 4 : Target identification and validation
  14. Chapter 5 : Hit discovery
  15. Chapter 6 : Lead optimization
  16. Chapter 7 : Evaluating safety and toxicity
  17. Chapter 8 : Precision medicine
  18. Chapter 9 : Image analysis in drug discovery
  19. Chapter 10 : Clinical trials, real-world evidence, and digital medicine
  20. Chapter 11 : Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment
  21. Index
  22. A