Design of New Biologically Active Molecules by Recursive Neural Networks

In this paper, we face the design of novel molecules belonging to the class of adenine analogues (8-azaadenine derivatives), that present a widespread potential therapeutic interest, in the new perspective offered by recursive neural networks for Quantitative Structure-Activity Relationships analysis. The generality and flexibility of the method used to process structured domains allows us to propose new solutions to the representation problem of this set of compounds and to obtain good prediction results, as it has been proved by the comparison with the values obtained 'a posteriori' after synthesis and biological essays of designed molecules.