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.
1. Chapter 1 : Introduction to drug discovery - p. Page x 2. Chapter 2 : Introduction to artificial intelligence and machine learning - p. Page 9 3. Chapter 3 : Data types and resources - p. Page 25 4. Chapter 4 : Target identification and validation - p. Page 50 5. Chapter 5 : Hit discovery - p. Page 75 6. Chapter 6 : Lead optimization - p. Page 99 7. Chapter 7 : Evaluating safety and toxicity - p. Page 113 8. Chapter 8 : Precision medicine - p. Page 133 9. Chapter 9 : Image analysis in drug discovery - p. Page 152 10. Chapter 10 : Clinical trials, real-world evidence, and digital medicine - p. Page 185 11. Chapter 11 : Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment - p. Page 211 12. Index - p. Page 231
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.
1. Chapter 1 : Introduction to drug discovery - p. Page x 2. Chapter 2 : Introduction to artificial intelligence and machine learning - p. Page 9 3. Chapter 3 : Data types and resources - p. Page 25 4. Chapter 4 : Target identification and validation - p. Page 50 5. Chapter 5 : Hit discovery - p. Page 75 6. Chapter 6 : Lead optimization - p. Page 99 7. Chapter 7 : Evaluating safety and toxicity - p. Page 113 8. Chapter 8 : Precision medicine - p. Page 133 9. Chapter 9 : Image analysis in drug discovery - p. Page 152 10. Chapter 10 : Clinical trials, real-world evidence, and digital medicine - p. Page 185 11. Chapter 11 : Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment - p. Page 211 12. Index - p. Page 231
