Chapter title |
Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation
|
---|---|
Chapter number | 13 |
Book title |
Computational Toxicology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7899-1_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7898-4, 978-1-4939-7899-1
|
Authors |
Kirk E. Hevener, Hevener, Kirk E. |
Abstract |
The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns. |
X Demographics
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United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 26 | 100% |
Demographic breakdown
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Student > Master | 4 | 15% |
Student > Postgraduate | 3 | 12% |
Student > Ph. D. Student | 3 | 12% |
Student > Doctoral Student | 2 | 8% |
Student > Bachelor | 2 | 8% |
Other | 3 | 12% |
Unknown | 9 | 35% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 3 | 12% |
Biochemistry, Genetics and Molecular Biology | 2 | 8% |
Unspecified | 1 | 4% |
Computer Science | 1 | 4% |
Other | 3 | 12% |
Unknown | 11 | 42% |