QSAR Methods.
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.
DEIB, Politecnico di Milano, Milano, Italy. gini@elet.polimi.it.
Giuseppina
G
Gini
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
Computer models; SAR and QSAR; Toxicity prediction