Literature DB >> 35188626

QSAR Methods.

Giuseppina Gini1.   

Abstract

This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties, and toxicology in particular, modeling. Since about three decades, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Animal and wet experiments, aimed at providing a standardized result about a biological property, can be mimicked by modeling methods, globally called in silico methods, all characterized by deducing properties starting from the 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 check 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. Virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Chemometrics; Computer models; Model interpretation; QSAR; SAR; Toxicity prediction

Mesh:

Year:  2022        PMID: 35188626     DOI: 10.1007/978-1-0716-1960-5_1

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  17 in total

1.  The characterization of chemical structures using molecular properties. A survey

Authors: 
Journal:  J Chem Inf Comput Sci       Date:  2000-03

2.  Chemists synthesize a single naming system.

Authors:  David Adam
Journal:  Nature       Date:  2002-05-23       Impact factor: 49.962

3.  Statistical evaluation of the Predictive Toxicology Challenge 2000-2001.

Authors:  Hannu Toivonen; Ashwin Srinivasan; Ross D King; Stefan Kramer; Christoph Helma
Journal:  Bioinformatics       Date:  2003-07-01       Impact factor: 6.937

Review 4.  Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology.

Authors:  Romualdo Benigni; Cecilia Bossa
Journal:  Mutat Res       Date:  2008-07-11       Impact factor: 2.433

5.  Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.

Authors:  T Ferrari; D Cattaneo; G Gini; N Golbamaki Bakhtyari; A Manganaro; E Benfenati
Journal:  SAR QSAR Environ Res       Date:  2013-05-28       Impact factor: 3.000

6.  Toxicology for the twenty-first century.

Authors:  Thomas Hartung
Journal:  Nature       Date:  2009-07-09       Impact factor: 49.962

Review 7.  Computational predictive programs (expert systems) in toxicology.

Authors:  E Benfenati; G Gini
Journal:  Toxicology       Date:  1997-05-16       Impact factor: 4.221

Review 8.  Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.

Authors:  Emilio Benfenati; Qasim Chaudhry; Giuseppina Gini; Jean Lou Dorne
Journal:  Environ Int       Date:  2019-08-01       Impact factor: 9.621

Review 9.  Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future.

Authors:  Pavel Polishchuk
Journal:  J Chem Inf Model       Date:  2017-10-13       Impact factor: 4.956

10.  Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity.

Authors:  J Ashby
Journal:  Environ Mutagen       Date:  1985
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