Application of neural networks to the analysis of the resistance of the human immunodeficiency virus to HIV reverse transcriptase inhibitors

AIDS and the opportunistic infections and some other complications, associated with this syndrome lead to more than one million of human deaths per year. Human Immunodeficiency Virus is a cause of AIDS. The drugs, targeted proteins of HIV, can only lead to decrease of HIV copies in the human organism but still do not eliminate HIV from an organism. The main cause of antiretroviral drug therapy failure is HIV resistance to main drug classes: inhibitors of HIV structural proteins, protease and reverse transcriptase. One of the approaches to the classification of HIV variants into the resistant and susceptible ones is the use of machine learning methods. The aim of this work is the classification of the HIV variants into susceptible and resistant based on the nucleotide sequence of the HIV protease using neural networks. In our work, we used two topologies of neural networks: multilayer perceptron and convolutional neural network. Neural Networks were built using Python Tensor Flow and Keras libraries, where optimization of the neural networks can be performed. The training and test sets include experimental data on the nucleotide sequence of HIV protease and their resistance. Sensitivity, specificity, balanced accuracy were used as the main parameters, reflected the quality of classification. Those parameters were calculated for the test set, collected in the later period comparing to the sequences of the data set. Copyright © 2019 for the individual papers by the papers’ authors.