Using neural models for evaluation of biological activity of selected chemical compounds

Abstract

The chapter shows how we can predict and evaluate the biological activity of particular chemical compounds using neural networks models. The purpose of the work was to verify the usefulness of various types and different structures of neural networks as well as various techniques of teaching the networks to predict the properties of defined chemical compounds, prior to studying them using laboratory methods. The huge number and variety of chemical compounds, which can be synthesized makes the prediction of any of their properties by computer modeling a very attractive alternative to costly experimental studies. The method described in this chapter may be useful for forecasting various properties of different groups of chemical compounds. The purpose of this chapter is to present the studied problem (and obtained solutions) from the point of view of the technique of neural networks and optimization of neural computations. The usefulness and wide-ranging applicability of neural networks have already been shown in hundreds of tasks concerning different and often very distant fields. Nevertheless, the majority of investigators tend to attain particular pragmatic ends, treating the used neural models purely as tools to get solutions: some particular network is arbitrarily chosen, results are obtained and presented, omitting or greatly limiting the discussion on which neural network was used, why it has been chosen and what could have been achieved if another network (or other non-neural methods, like regressive ones) had been applied. In this situation, every researcher undertaking any similar problem once more faces the serious methodological question: which network to select, how to train it and how to present the data in order to obtain the best results. This chapter will present the results of the investigations, in which, to the same (difficult) problem of predicting the chemical activity of quite a large group of chemical compounds, various networks were applied and different results were obtained. Basing ourselves on the results, we will draw conclusions showing which networks and methods of learning are better and which are worse in solving the considered problem. These conclusions cannot just be mechanically generalized because every question on the application of neural networks has its own unique specificity, but the authors of this chapter hope that their wide and precisely documented studies will appear useful for persons wanting to apply neural networks and considering which model to use as a starting one. © 2008 Springer-Verlag Berlin Heidelberg.