Malaria is a global health burden and drug resistance is major hurdle preventing early effective treatment. Malaria parasites show considerable heterogeneity in the gene expression programs when exposed to the most effective antimalarial drug Artemisinin. The cellular mechanisms of Artemisinin tolerance are not fully understood. We aim to unravel the mechanisms of drug resistance by Machine Learning (ML), Artificial Intelligence (AI)-driven modelling of the transcriptional landscapes at single-cell level. This project can naturally be extended in the next phase towards data-driven ML modeling of Plasmodium against other antimalarial drugs and indeed for other protists or bacteria or fungi against anti-infective compounds.
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