Prediction of HIV-1 protease resistance using genotypic, phenotypic, and molecular information with artificial neural networks.

ABSTRACT: Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money-consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this study is to construct drug isolate fold (DIF) change-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. We have obtained 11,803 genotype-phenotype data point

SUBMITTER: Tunc H 

PROVIDER: S-EPMC10038082 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature