ArticlePharmaceutical research2014
Computational models for neglected diseases: gaps and opportunities.
Article in Pharmaceutical research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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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.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Drug Repurposing for Kala-Azar.Pharmaceutics · 2025Article
- Prediction of antischistosomal small molecules using machine learning in the era of big data.Molecular diversity · 2022Review
- A computer-aided approach to identify novel Leishmania major protein disulfide isomerase inhibitors for treatment of leishmaniasis.Journal of computer-aided molecular design · 2021Article
- A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.ACS infectious diseases · 2021Article
- High Throughput and Computational Repurposing for Neglected Diseases.Pharmaceutical research · 2018Review
- Chemoinformatics Strategies for Leishmaniasis Drug Discovery.Frontiers in pharmacology · 2018Review
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
- Open drug discovery for the Zika virus.F1000Research · 2016Article
- Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.PLoS neglected tropical diseases · 2015Article
- Bigger data, collaborative tools and the future of predictive drug discovery.Journal of computer-aided molecular design · 2014Article
- Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis.Journal of chemical information and modeling · 2014Article
- Finding new collaboration models for enabling neglected tropical disease drug discovery.PLoS neglected tropical diseases · 2014Article
- Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.Journal of chemical information and modeling · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.
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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.