ArticleFrontiers in pharmacology2019
DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum.Molecular diversity · 2022Pooled it
- Review
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.Acta pharmaceutica Sinica. B · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- β-Carboline: a privileged scaffold from nature for potential antimalarial activity.RSC medicinal chemistry · 2025Review
- Article
- Lingering challenges in malaria elimination efforts in sub-Saharan Africa: Insights and potential solutions.Health science reports · 2024Article
- Leveraging computational tools to combat malaria: assessment and development of new therapeutics.Journal of cheminformatics · 2024Review
- Article
- MAIP: An Open-Source Tool to Enrich High-Throughput Screening Output and Identify Novel, Druglike Molecules with Antimalarial Activity.ACS medicinal chemistry letters · 2023Article
- Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.ACS omega · 2023Article
- Artificial intelligence, parasites and parasitic diseases.Parasites & vectors · 2023Article
- Molecular Descriptors Property Prediction Using Transformer-Based Approach.International journal of molecular sciences · 2023Article
- Phenotypic drug discovery: recent successes, lessons learned and new directions.Nature reviews. Drug discovery · 2022Review
- Exploring predictive frameworks for malaria in Burundi.Infectious Disease Modelling · 2022Article
- MolData, a molecular benchmark for disease and target based machine learning.Journal of cheminformatics · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual
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.