Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

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 EC50 3.2 μM and CC50 24 μM,
which represents a new scaffold suitable for hit-to-lead optimization
that can expand the available drug discovery candidates for YF.