The video-presentation in the **introduction** page shows some fundamental information about QSAR. From this page you can learn what a QSAR model is and how model developers build them.

You can visualize the presentation here but you can also **download **it as a PPT file on your computer.

The methods called quantitative structure-activity relationship (QSAR) are based on the assumption that the activity of a certain chemical compound is related to its structure. More precisely, this approach says that the activity, or the property, for instance the toxic effect, is related to the chemical structure through a certain mathematical algorithm, or rule. QSAR models are also called in silico methods, which actually refer to a somehow broader set of methods.

The typical way to derive QSAR model is here represented. The basic assumption is that there is a mathematical function of the chemical properties which is related to the effect.

Thus, the effect called y is a function called f of the chemical properties, called x. Mathematically, y = f(x). But how to find this mathematical algorithm f(x)? Typically, we use a number of chemical compounds with know values of the toxic effect (y). For each chemical compound we calculate a series of parameters, called chemical descriptors. Then, we find an algorithm that provides a quite accurate value, similar to the real experimental value. The final step is to check if the so-obtained algorithm is capable to predict the property values for other chemicals, not used to build up the model. This last phase is called validation of the QSAR model.

This last phase is very important. Indeed, it is very important to generate a model which is working not only for the chemical substances used within the training set, but also for other chemicals. The challenge is to define the correct statistical properties of the model.