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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

Table of Contents

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