Chapter title |
In Silico Methods for Carcinogenicity Assessment.
|
---|---|
Chapter number | 6 |
Book title |
In Silico Methods for Predicting Drug Toxicity
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3609-0_6 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3607-6, 978-1-4939-3609-0
|
Authors |
Azadi Golbamaki, Emilio Benfenati |
Editors |
Emilio Benfenati |
Abstract |
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of alternative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models, have been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on the human expert knowledge and statistically approach, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated and the results are interpreted in details by applying these prediction models to some pharmaceutical molecules. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Bulgaria | 1 | 7% |
Unknown | 14 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Unspecified | 2 | 13% |
Other | 2 | 13% |
Student > Bachelor | 2 | 13% |
Researcher | 2 | 13% |
Student > Ph. D. Student | 1 | 7% |
Other | 1 | 7% |
Unknown | 5 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Unspecified | 2 | 13% |
Agricultural and Biological Sciences | 2 | 13% |
Computer Science | 1 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
Unknown | 9 | 60% |