Improved Prediction of HIV-1 Protease Genotypic Resistance Testing Assays using a Consensus Technique

Mutations in HIV-1 drug targets can cause reduced affinity to antiretroviral inhibitors, leading to the emergence of resistant variants resulting in failure of treatment in infected individuals. Resistance testing is an important factor in the continued success of viral therapy. We found that through combining a structural based computational docking method and a classic machine learning technique we could create a consensus system capable of improving the prediction accuracy by 5.56% over either method used individually. The result was the creation of a genotypic resistance testing approach capable of classifying a wider cross-section of strains, hence making it a more accurate resistance testing method.