ArticleJournal of chemical information and modeling2021
Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Repurposing Neurological Drugs Against Dengue Virus Infection.Journal of tropical medicine · 2025Review
- Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning.Marine drugs · 2024Article
- Organotypic culture of human brain explants as a preclinical model for AI-driven antiviral studies.EMBO molecular medicine · 2024Article
- 1-Sulfonyl-3-amino-1ACS omega · 2023Article
- Atom-Based Machine Learning Model for Quantitative Property-Structure Relationship of Electronic Properties of Fusenes and Substituted Fusenes.ACS omega · 2023Article
- "Freedom of design" in chemical compound space: towards rationalChemical science · 2023Article
- Learning from COVID-19: How drug hunters can prepare for the next pandemic.Drug discovery today · 2023Article
- Novel computational pipelines in antiviral structure‑based drug design (Review).Biomedical reports · 2022Review
- The Commoditization of AI for Molecule Design.Artificial intelligence in the life sciences · 2022Article
- Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions.Journal of cheminformatics · 2022Article
- Bis(Benzofuran-1,3-International journal of molecular sciences · 2022Article
- Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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 EC
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.