Undesirable toxicity is still a major block in the drug discovery process. Obviously, capable techniques that
identify poor effects at a very early stage of product development and provide reasonable toxicity estimates for the huge
number of untested compounds are needed. In silico techniques are very useful for this purpose, because of their
advantage in reducing time and cost.
These case studies give the description of in silico validation techniques and applied modeling methods for the prediction
of toxicity of chemical compounds. In silico toxicity prediction techniques can be classified into two categories:
Molecular Modeling and methods that derive predictions from experimental data.
Molecular modeling is a computational approach to mimic the behavior of molecules, from small molecules (e.g. in
conformational analysis) to biomolecules. But the same approaches can also be applied for toxicological purposes, if the
mechanism is receptor mediated.
Quantitative Structure-Toxicity Relationships (QSTRs) models are typical examples for the prediction of toxicity which
relates variations in the molecular structures to toxicity.
There are many applied modeling techniques in QSTR such as Partial Least Squares, Artificial Neural Networks, and
Principal Component Regression (PCR). The applicability of these techniques in predictive toxicology will be discussed
with different examples of sets of chemical compounds.