Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.

ABSTRACT: The rapid emergence of drug resistance in viral infections represents a significant global health challenge, threatening the efficacy of treatments for multiple diseases. Machine learning models have emerged as valuable tools for predicting antiviral drug resistance from genomic data, with HIV-1 protease serving as a well-characterized model system due to its extensive experimental data and clinical relevance. Here, we systematically evaluate multiple previously published HIV-1 protease inhibitor (PI) resistance prediction models across three distinct data sets with different preprocessing and ambiguous sequencing processing strategies and propose a new approach for preprocessing. We tested Steiner's data set (n = 1540) with first-amino-acid selection at ambiguous positions, Shen's

SUBMITTER: Riveros Maidana RLB 

PROVIDER: S-EPMC12529765 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature