The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry
- 1st Edition - April 23, 2021
- Imprint: Academic Press
- Editor: Stephanie K. Ashenden
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 0 4 5 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 4 4 9 - 8
The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies h… Read more
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Request a sales quoteThe 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgments and conflicts of interest
- Chapter 1: Introduction to drug discovery
- Abstract
- The drug discovery process
- Chapter 2: Introduction to artificial intelligence and machine learning
- Abstract
- Supervised learning
- Unsupervised learning
- Semisupervised learning
- Model selection
- Types of data
- Other key considerations
- Deep learning
- Uncertainty quantification
- Chapter 3: Data types and resources
- Abstract
- Notes on data
- Omics data
- Chemical compounds
- QSAR with regards to safety
- Data resources
- Chapter 4: Target identification and validation
- Abstract
- Introduction
- Target identification predictions
- Gene prioritization methods
- Machine learning and knowledge graphs in drug discovery
- Data, data mining, and natural language processing for information extraction
- Chapter 5: Hit discovery
- Abstract
- Chemical space
- Screening methods
- High-throughput screening
- Computer-aided drug discovery
- Virtual screening
- Representing compounds to machine learning algorithms
- Candidate learning algorithms
- Future directions: Learned descriptors and proteochemometric models
- Evaluating virtual screening models
- Clustering in hit discovery
- Chapter 6: Lead optimization
- Abstract
- What is lead optimization
- Applications of machine learning in lead optimization
- Assessing ADMET and biological activities properties
- Matched molecular pairs
- Chapter 7: Evaluating safety and toxicity
- Abstract
- Introduction to computational approaches for evaluating safety and toxicity
- In silico nonclinical drug safety
- Machine learning approaches to toxicity prediction
- Pharmacovigilance and drug safety
- Conclusions
- Chapter 8: Precision medicine
- Abstract
- Cancer-targeted therapy and precision oncology
- Personalized medicine and patient stratification
- Finding the “right patient”: Data-driven identification of disease subtypes
- Key advances in healthcare AI driving precision medicine
- Chapter 9: Image analysis in drug discovery
- Abstract
- Cells
- Spheroids
- Microphysiological systems
- Ex vivo tissue culture
- Animal models
- Aims and tasks in image analysis
- Region segmentation in digital pathology
- Feature extraction
- The status of imaging and artificial intelligence in human clinical trials for oncology drug development
- Future directions
- Chapter 10: Clinical trials, real-world evidence, and digital medicine
- Abstract
- Introduction
- Clinical trials
- Real-world data: Challenges and applications in drug development
- Sensors and wearable devices
- Conclusions
- Chapter 11: Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment
- Abstract
- Overview
- Background
- Current European and US legislation for environmental assessment of pharmaceuticals
- Animal testing for protecting the environment
- Issues for database creation
- Opportunities to refine animal testing for protecting the environment
- Current approaches to predicting uptake of pharmaceuticals
- What makes pharmaceuticals special?
- Why do pharmaceuticals effect wildlife?
- What happens in the environment?
- Predicting uptake using ML
- Regional issues and the focus of concern
- Intelligent regulation—A future state of automated AI assessment of chemicals
- Key points for future development
- Index
- Edition: 1
- Published: April 23, 2021
- No. of pages (Paperback): 264
- No. of pages (eBook): 264
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128200452
- eBook ISBN: 9780128204498
SA
Stephanie K. Ashenden
Dr. Ashenden is Senior Artificial Intelligence and Machine Learning Data Scientist at AstraZeneca, working in the Discovery Sciences, IMed-Biotech Unit. She received her PhD in 2018 from the Department of Chemistry, Cambridge University. Dr. Ashenden has three publications, but in very high impact resources (Methods in Enzymology, Journal of Chemical Information and Modeling, and Journal of Medicinal Chemistry).
Dr. Ashenden is a very early career researcher, but has an extensive research network, academic and industrial experience, and a drive to conduct and report high quality research. She will be working with more experienced researchers on the project to help guide and offer experience.
Affiliations and expertise
Senior Artificial Intelligence and Machine Learning Data Scientist, AstraZenecaRead The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry on ScienceDirect