This study is concerned with identifying features of 4-aminoquinoline scaffolds that can help pinpoint characteristics that enhance activity against chloroquine-resistant parasites. Statistically valid predictive models are reported for a series of 4-aminoquinoline analogues that are active against chloroquine-sensitive (NF54) and chloroquine-resistant (K1) strains of Plasmodium falciparum. Quantitative structure activity relationship techniques, based on statistical and machine learning methods such as multiple linear regression and partial least squares, were used with a novel pruning method for the selection of descriptors to develop robust models for both strains. Inspection of the dominant descriptors supports the hypothesis that chemical features that enable accumulation in the food vacuole of the parasite are key determinants of activity against both strains. The hydrophilic properties of the compounds were found to be crucial in predicting activity against the chloroquine-sensitive NF54 parasite strain, but not in the case of the chloroquine-resistant K1 strain, in line with previous studies. Additionally, the models suggest that 'softer' compounds tend to have improved activity for both strains than do 'harder' ones. The internally and externally validated models reported here should also prove useful in the future screening of potential antimalarial compounds for targeting chloroquine-resistant strains. Graphical Abstract Predictive models reveal linear relationships for activity of 4-aminoquinoline analogues active against chloroquine-sensitive strains of Plasmodium falciparum.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097041 | PMC |
| http://dx.doi.org/10.1007/s00894-018-3755-z | DOI Listing |
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