ReviewParasitology2020
Computational approaches for drug discovery against trypanosomatid-caused diseases.
Review in Parasitology, 2020. 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
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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.
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.
- Identification of New Anti-Trypanosoma brucei Leads: An Integrated CRK12-Guided Virtual Screen With Experimental Validation.Chemical biology & drug design · 2026Article
- From genomes to interventions: computational strategies transforming parasitology.Frontiers in veterinary science · 2026Review
- Discovery of Novel CRK12 Inhibitors for the Treatment of Human African Trypanosomiasis: An Integrated Computational and Experimental Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Identification of novel compounds againstComputational and structural biotechnology journal · 2025Article
- Exploring the Inhibition ofInternational journal of molecular sciences · 2024Article
- Therapeutic applications of carbohydrate-based compounds: a sweet solution for medical advancement.Molecular diversity · 2024Review
- AnHeliyon · 2024Article
- State-of-the-Art in the Drug Discovery Pathway for Chagas Disease: A Framework for Drug Development and Target Validation.Research and reports in tropical medicine · 2023Review
- Identification and Validation of Novel Biomarkers and Potential Targeted Drugs in Cholangiocarcinoma: Bioinformatics, Virtual Screening, and Biological Evaluation.Journal of microbiology and biotechnology · 2022Article
- A global picture: therapeutic perspectives for COVID-19.Immunotherapy · 2022Review
- Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery.Molecules (Basel, Switzerland) · 2020Review
- High throughput virtual screening reveals SARS-CoV-2 multi-target binding natural compounds to lead instant therapy for COVID-19 treatment.International journal of biological macromolecules · 2020Article
- Drug targets for COVID-19 therapeutics: Ongoing global efforts.Journal of biosciences · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.
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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.