Title
In silico mining for antimalarial structure-activity knowledge and discovery of novel antimalarial curcuminoids In silico mining for antimalarial structure-activity knowledge and discovery of novel antimalarial curcuminoids
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences. Pharmacy
Publication type
article
Publication
Bazel ,
Subject
Chemistry
Biology
Human medicine
Source (journal)
Molecules: a journal of synthetic chemistry and natural product chemistry. - Bazel
Volume/pages
21(2016) :7 , 18 p.
ISSN
1420-3049
1420-3049
Article Reference
853
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/01b72d/134082.pdf
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