A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds

HIV resistance emerging against antiretroviral
drugs represents
a great threat to the continued prolongation of the lifespans of HIV-infected
patients. Therefore, methods capable of predicting resistance susceptibility
in the development of compounds are in great need. By targeting the
major reverse transcription residues Y181, K103, and L100, we used
the biological activities of compounds against these enzymes and the
wild-type reverse transcriptase to create Naïve Bayes Networks.
Through this machine learning approach, we could predict, with high
accuracy, whether a compound would be susceptible to a loss of potency
due to resistance. Also, we could perfectly predict retrospectively
whether compounds would be susceptible to both a K103 mutant RT and
a Y181 mutant RT. In the study presented here, our method outperformed
a traditional molecular mechanics approach. This method should be
of broad interest beyond drug discovery efforts, and serves to expand
the utility of machine learning for the prediction of physical, chemical,
or biological properties using the vast information available in the
literature.