HIV-1 reverse transcriptase inhibitor design using artificial neural networks

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.