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

Abstract

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.

Development of machine learning models based on molecular fingerprints for selection of small molecule inhibitors against JAK2 protein

“…Now a days, ML has become an essential tool for mining chemical information from large compound databases, to design novel drugs with important biological features. For instance, Sean Ekins et al used ML to discover the novel antiviral compounds against yellow fever virus 40 . Akansha Rajput et al used ML for the prediction of repurposed drugs for coronavirus (Covid‐19) 41 .…”

Novel computational pipelines in antiviral structure‑based drug design (Review)

“…DeepScore, a deep learning-based model, constructs target-specific scoring functions, which can generate better results in structure-based virtual screening experiments in comparison to conventional, universal scoring functions (59). Lastly, deep learning and ML approaches have been implemented in the hunt for novel antiviral drugs, acting as a driving force in the drug discovery process against notable viral pathogens such as SARS-CoV-2, Yellow Fever Virus and Ebola virus (60)(61)(62). A notable issue when implementing ML models that carry out binding affinity predictions for the discovery of antivirals is the sturdy evaluation of model performance.…”

Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions

“…For example, popular repositories like PubChem and ZINC20 currently contain 1.2 million bioactivity assays and 1.4 billion unique compounds respectively [ 3 – 5 ]. Thanks to these resources, it is straightforward to obtain thousands of training points to develop high-performing predictive models, which can then be used to screen for novel ligands, antibiotics, antivirals and so forth [ 6 – 8 ].…”