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

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

Academic and industrial researchers interested in drug discovery, chemical biology, computational chemistry, medicinal chemistry, and bioinformatics

1. Drug discovery in the era of AI and ML
   2. The druggable targets (target identification and validation)
   3. Finding the sweet spot of compound properties – (lead identification – lead optimisation) - Compounds, Chemistry and Cheminformatics
   4. Drug safety and predicting toxicology
   5. Image analytics
   6. Biology and Bioinformatics
   7. Bayesian Approaches to problem solving
   8. Natural Language Processing
   9. Case studies