Abstract
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has 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 human expert knowledge and statistical approaches, 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 predictive models to some pharmaceutical molecules.
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Acknowledgments
The research for this chapter was financially supported by the LIFE VERMEER project (LIFE16 ENV/IT/OOO167).
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Golbamaki, A., Benfenati, E., Roncaglioni, A. (2022). In Silico Methods for Carcinogenicity Assessment. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_9
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DOI: https://doi.org/10.1007/978-1-0716-1960-5_9
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