Open AccessDevelopment of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus
Author(s) -
Victor O. Gawriljuk,
Daniel H. Foil,
Ana C. Puhl,
Kimberley M. Zorn,
Thomas R. Lane,
Olga Riabova,
Vadim Makarov,
Andre S. Godoy,
Glaucius Oliva,
Sean Ekins
Publication year - 2021
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.1c00460
Subject(s) - drug discovery , dengue virus , zika virus , yellow fever , machine learning , dengue fever , artificial intelligence , deep learning , computer science , drug , autoencoder , virus , computational biology , virology , biology , bioinformatics , pharmacology
Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC 50 3.2 μM and CC 50 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.