Evaluation of potential HIV-1 reverse transcriptase inhibitors by artificial neural networks

Artificial neural networks were used to analyze the human immunodeficiency virus type 1 reverse transcriptase inhibitors and to evaluate newly synthesized substances on this basis. The training and control set included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of molecules were taken from literature. Topological indices were calculated and used as molecular parameters. 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. The 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. The activity was confirmed by further biological tests