Raymond Lui (The University of Sydney) Automated machine learning method using 59 permutation feature importance selected Mordred and quantum mechanical descriptors optimised for Mean Absolute Error. It was originally intended for each of the four winning entrants (first and second place winners) to generate two new structures that were predicted to be active using their models: one possessing the Series 4 triazolopyrazine core and the other being structurally distinct. This would give a total of eight molecules to be synthesised and validated experimentally. In addition to optimising potency, model generators were tasked with keeping good solubility in mind as a design criterion. It became evident that certain suggested compounds were synthetically inaccessible, or would take major resources to pursue. The former is often an issue when predictive models do not take into account known synthetic pathways, though there is significant activity at present to improve the incorporation of synthetic planning into library suggestion . The initial list was narrowed to focus on five predicted triazolopyrazine compounds (Fig. ). The five compounds were successfully synthesised and subsequently evaluated for in vitro (growth-inhibition) activity against P. falciparum along with the previously reported positive control for the series . In addition to the standard potency (in vitro growth) assay, these compounds were evaluated for their ability to inhibit PfATP4 in biochemical (Na + regulation) assays to confirm that the MoA had not changed following these structural changes.