Chapter title |
Computational Toxicology and Drug Discovery
|
---|---|
Chapter number | 11 |
Book title |
Computational Toxicology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7899-1_11 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7898-4, 978-1-4939-7899-1
|
Authors |
Catrin Hasselgren, Glenn J. Myatt, Hasselgren, Catrin, Myatt, Glenn J. |
Abstract |
The use of computational toxicology methods within drug discovery began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been continuously expanding ever since and the tasks at hand have become more complex. These approaches are now strategically integrated into the risk assessment process, as a complement to in vitro and in vivo methods. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life-cycle management. This chapter provides an overview of the field and describes the application of computational toxicology throughout the entire discovery and development process. |
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Unknown | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 55 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 10 | 18% |
Researcher | 8 | 15% |
Student > Ph. D. Student | 7 | 13% |
Other | 4 | 7% |
Student > Postgraduate | 4 | 7% |
Other | 9 | 16% |
Unknown | 13 | 24% |
Readers by discipline | Count | As % |
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Chemistry | 18 | 33% |
Agricultural and Biological Sciences | 8 | 15% |
Biochemistry, Genetics and Molecular Biology | 4 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 5% |
Computer Science | 3 | 5% |
Other | 4 | 7% |
Unknown | 15 | 27% |