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Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry [Mīkstie vāki]

Edited by (Senior Artificial Intelligence and Machine Learning Data Scientist, AstraZeneca)
  • Formāts: Paperback / softback, 264 pages, height x width: 235x191 mm, weight: 450 g, 60 illustrations (20 in full color); Illustrations, unspecified
  • Izdošanas datums: 28-Apr-2021
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128200456
  • ISBN-13: 9780128200452
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  • Cena: 130,13 €
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Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical IndustryPalielināt
  • Formāts: Paperback / softback, 264 pages, height x width: 235x191 mm, weight: 450 g, 60 illustrations (20 in full color); Illustrations, unspecified
  • Izdošanas datums: 28-Apr-2021
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128200456
  • ISBN-13: 9780128200452
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780128200452.html
Keywords:

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
Contributors xi
Preface xv
Acknowledgments and conflicts of interest xvii
Chapter 1 Introduction to drug discovery
1(14)
Stephanie Kay Ashenden
The drug discovery process
1(8)
Target identification
1(1)
Target validation
2(1)
Hit identification and lead discovery
3(3)
Lead optimization
6(2)
Precision medicine
8(1)
Clinical testing and beyond
8(1)
References
9(6)
Chapter 2 Introduction to artificial intelligence and machine learning
15(12)
Stephanie Kay Ashenden
Aleksandra Bartosik
Paul-Michael Agapow
Elizaveta Semenova
Supervised learning
17(1)
Unsupervised learning
17(1)
Semisupervised learning
18(1)
Model selection
18(2)
Types of data
20(1)
Other key considerations
20(2)
Feature generation and selection
20(1)
Censored and missing data
21(1)
Dependencies in the data: Time series or sequences, spatial dependence
21(1)
Deep learning
22(2)
Uncertainty quantification
24(1)
Bayesian inference
24(1)
References
25(2)
Chapter 3 Data types and resources
27(34)
Stephanie Kay Ashenden
Sumit Deswal
Krishna C. Bulusu
Aleksandra Bartosik
Khader Shameer
Notes on data
27(1)
Omics data
28(2)
Genomics
28(1)
Transcriptomics
29(1)
Metabolomics and lipomics
29(1)
Proteomics
30(1)
Chemical compounds
30(5)
SDF format
31(1)
InChI and InChI Key format
31(1)
SMILES and SMARTS format
31(1)
Fingerprint format
32(1)
Other descriptors
33(1)
Similarity measures
34(1)
QSAR with regards to safety
35(1)
Data resources
36(14)
Toxicity related databases
36(5)
Drug safety databases
41(2)
Key public data-resources for precision medicine
43(7)
References
50(11)
Chapter 4 Target identification and validation
61(20)
Stephanie Kay Ashenden
Natalie Kurbatova
Aleksandra Bartosik
Introduction
61(2)
Target identification predictions
63(2)
Gene prioritization methods
65(1)
Machine learning and knowledge graphs in drug discovery
66(6)
Introduction
66(1)
Graph theory algorithms
67(2)
Graph-oriented machine learning approaches
69(3)
Drug discovery knowledge graph challenges
72(1)
Data, data mining, and natural language processing for information extraction
72(3)
What is natural language processing
72(1)
How is it used for drug discovery and development
73(1)
Where is it used in drug discovery and development (and thoughts on where it is going at the end)
74(1)
References
75(6)
Chapter 5 Hit discovery
81(22)
Hannes Whittingham
Stephanie Kay Ashenden
Chemical space
81(1)
Screening methods
82(1)
High-throughput screening
82(1)
Computer-aided drug discovery
83(2)
De novo design
83(2)
Virtual screening
85(4)
Data collection and curation
86(1)
Databases and access
87(1)
Compounds
87(1)
Targets
88(1)
Activity measurement
88(1)
Cleaning collected data---Best practices
89(1)
Representing compounds to machine learning algorithms
89(1)
Candidate learning algorithms
89(3)
Naive Bayes
90(1)
K-Nearest neighbors
90(1)
Support vector machines
90(1)
Random forests
91(1)
Artificial neural networks
91(1)
Multitask deep neural networks
92(1)
Future directions: Learned descriptors and proteochemometric models
92(2)
Graph convolutional and message passing neural networks
93(1)
Proteochemometric models
93(1)
Evaluating virtual screening models
94(2)
Train-test splits: Random, temporal, or cluster-based?
94(1)
External validation
95(1)
Prospective experimental validation
96(1)
Clustering in hit discovery
96(3)
Butina clustering
96(1)
K-means clustering
97(1)
Hierarchical clustering
98(1)
References
99(4)
Chapter 6 Lead optimization
103(16)
Stephanie Kay Ashenden
What is lead optimization
103(1)
Applications of machine learning in lead optimization
103(2)
Assessing ADMET and biological activities properties
105(5)
Matched molecular pairs
110(3)
Machine learning with matched molecular pairs
112(1)
References
113(6)
Chapter 7 Evaluating safety and toxicity
119(20)
Aleksandra Bartosik
Hannes Whittingham
Introduction to computational approaches for evaluating safety and toxicity
119(1)
In silico nonclinical drug safety
120(2)
Machine learning approaches to toxicity prediction
122(6)
K-nearest neighbors
122(1)
Logistic regression
123(1)
SVM
124(1)
Decision tree
125(1)
Random forest and other ensemble methods
126(1)
Naive Bayes classifier
126(1)
Clustering and primary component analysis
127(1)
Deep learning
127(1)
Pharmacovigilance and drug safety
128(4)
Data sources
128(2)
Disproportionality analysis
130(1)
Mining medical records
130(1)
Electronic health records
130(1)
Social media signal detection
131(1)
Knowledge-based systems, association rules, and pattern recognition
131(1)
Conclusions
132(1)
References
133(6)
Chapter 8 Precision medicine
139(20)
Sumit Deswal
Krishna C. Bulusii
Paul-Michael Agapow
Faisal M. Khan
Cancer-targeted therapy and precision oncology
140(1)
Personalized medicine and patient stratification
141(2)
Methods for survival analysis
142(1)
Finding the "right patient": Data-driven identification of disease subtypes
143(7)
Subtypes are the currency of precision medicine
143(1)
The nature of clusters and clustering
144(1)
Selection and preparation of data
144(1)
Approaches to clustering and classification
145(3)
Validation and interpretation
148(2)
Key advances in healthcare AI driving precision medicine
150(2)
Key challenges for AI in precision medicine
152(1)
References
152(7)
Chapter 9 Image analysis in drug discovery
159(32)
Adam M. Corrigan
Daniel Sutton
Johannes Zimmermann
Laura A.L. Dillon
Kaustav Bera
Armin Meier
Fabiola Cecchi
Anant Madabhushi
Gunter Schmidt
Jason Hipp
Cells
160(1)
Spheroids
161(1)
Microphysiological systems
161(1)
Ex vivo tissue culture
161(1)
Animal models
161(2)
Tissue pathology
162(1)
Aims and tasks in image analysis
163(5)
Image enhancement
164(1)
Image segmentation
165(3)
Region segmentation in digital pathology
168(4)
Why is it used?
170(2)
Feature extraction
172(2)
Image classification
173(1)
The status of imaging and artificial intelligence in human clinical trials for oncology drug development
174(9)
Computational pathology image analysis
175(3)
Radiology image analysis
178(5)
Conclusion
183(1)
Future directions
183(2)
Imaging for drug screening
183(1)
Computational pathology and radiomics
184(1)
References
185(6)
Chapter 10 Clinical trials, real-world evidence, and digital medicine
191(26)
Jim Weatherall
Faisal M. Khan
Mishal Patel
Richard Dearden
Khader Shameer
Glynn Dennis
Gabriela Feldberg
Thomas White
Sajan Khosla
Introduction
191(1)
The importance of ethical AI
192(1)
Clinical trials
192(10)
Site selection
193(3)
Recruitment modeling for clinical trials
196(2)
Applications of recruitment modeling in the clinical supply chain
198(1)
Clinical event adjudication and classification
199(2)
Identifying predictors of treatment response using clinical trial data
201(1)
Real-world data: Challenges and applications in drug development
202(5)
The RWD landscape
203(1)
Barriers for adoption of RWD for clinical research
203(1)
Use of RWE/RWD in clinical drug development and research
204(3)
Concluding thoughts on RWD
207(1)
Sensors and wearable devices
207(4)
Sample case study: Parkinson's disease
209(1)
Standards and regulations and concluding thoughts
210(1)
Conclusions
211(1)
References
211(6)
Chapter 11 Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment
217(20)
Stewart F. Owen
Jason R. Snape
Overview
217(1)
Background
218(2)
Current European and US legislation for environmental assessment of pharmaceuticals
220(1)
Animal testing for protecting the environment
221(1)
Issues for database creation
222(1)
Opportunities to refine animal testing for protecting the environment
223(1)
Current approaches to predicting uptake of pharmaceuticals
224(1)
What makes pharmaceuticals special?
225(1)
Why do pharmaceuticals effect wildlife?
226(1)
What happens in the environment?
227(1)
Predicting uptake using ML
228(1)
Regional issues and the focus of concern
229(1)
Intelligent regulation---A future state of automated AI assessment of chemicals
230(1)
Key points for future development
231(1)
References
231(6)
Index 237
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.