[Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds]. / Primenenie neironnykh setei, ispol'zuiushchikh algoritm prostranstvennogo obucheniia dlia issledovaniia sviazei struktura-aktivnost' khimicheskikh soedinenii.

A volume learning algorithm for artificial neural networks was developed to quantitatively describe the three-dimensional structure-activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped to reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.