ArticleMolecules (Basel, Switzerland)2016
In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.
Article in Molecules (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum.Molecular diversity · 2022Pooled it
- Gene Expression, Docking and Machine Learning in Malaria Drug Discovery: A Systematic Review.Biochemistry research international · 2026Review
- Synthesis and bioactivity of tetrahydrothiopyran derivatives as potential acaricides against Psoroptes cuniculi.Molecular diversity · 2025Article
- Chemoselective Synthesis and Anti-Kinetoplastidal Properties of 2,6-Diaryl-4Molecules (Basel, Switzerland) · 2024Article
- Preliminary insight on diarylpentanoids as potential antimalarials: In silico, in vitroHeliyon · 2024Article
- Bioactive Clerodane Diterpenoids from the Leaves ofMolecules (Basel, Switzerland) · 2023Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Duration of Storage Reduced Erythrocytes Profiles andJournal of blood medicine · 2021Article
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- A Chemographic Audit of anti-Coronavirus Structure-activity Information from Public Databases (ChEMBL).Molecular informatics · 2020Article
- Multi-Anti-Parasitic Activity of Arylidene Ketones and Thiazolidene Hydrazines against Trypanosoma cruzi and Leishmania spp.Molecules (Basel, Switzerland) · 2017Article
- QSAR modeling and chemical space analysis of antimalarial compounds.Journal of computer-aided molecular design · 2017Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.
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