ArticleACS omega2019
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads.
Article in ACS omega, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 57 citations in OpenAlex.
- Exploring the druggability of the UEV domain of human TSG101 in search for broad-spectrum antivirals.Protein science : a publication of the Protein Society · 2025Article
- High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.Journal of medicinal chemistry · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- Transporter Inhibition Profile for the Antivirals Tilorone, Quinacrine and Pyronaridine.ACS omega · 2023Article
- Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors.International journal of molecular sciences · 2023Article
- Novel computational pipelines in antiviral structure‑based drug design (Review).Biomedical reports · 2022Review
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Machine Learning for Discovery of New ADORA Modulators.Frontiers in pharmacology · 2022Article
- Machine Learning Models Identify Inhibitors of SARS-CoV-2.Journal of chemical information and modeling · 2021Article
- Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model.ACS chemical neuroscience · 2021Article
- The Antiviral Drug Tilorone Is a Potent and Selective Inhibitor of Acetylcholinesterase.Chemical research in toxicology · 2021Article
- A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.ACS infectious diseases · 2021Article
- Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.Molecular pharmaceutics · 2021Article
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.IEEE access : practical innovations, open solutions · 2021Article
- Comparing Machine Learning Models for Aromatase (P450 19A1).Environmental science & technology · 2020Article
- Comparison of Machine Learning Models for the Androgen Receptor.Environmental science & technology · 2020Article
- Machine Learning for Discovery of GSK3β Inhibitors.ACS omega · 2020Article
- Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.Environmental science & technology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC
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.