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QSAR Methods

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In Silico Methods for Predicting Drug Toxicity

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1425))

Abstract

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

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Acknowledgements

We kindly acknowledge the EU Life + projects CALEIDOS and PROSIL.

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Authors and Affiliations

  1. DEIB, Politecnico di Milano, Milano, Italy

    Giuseppina Gini

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  1. Giuseppina Gini
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Correspondence to Giuseppina Gini .

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Editors and Affiliations

  1. Mario Negri Inst for Pharm Rsrch, IRCCS, Milan, Italy

    Emilio Benfenati

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Gini, G. (2016). QSAR Methods. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3609-0_1

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  • DOI: https://doi.org/10.1007/978-1-4939-3609-0_1

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3607-6

  • Online ISBN: 978-1-4939-3609-0

  • eBook Packages: Springer Protocols

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