Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

ABSTRACT: The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibiti

SUBMITTER: Zorn KM 

PROVIDER: S-EPMC7702308 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature