To probe the structure-activity relationship of anti-aggregation activity, partial least squares (PLS) 70 regression was performed, using a set of physicochemical descriptors as input. Molec- ular descriptors were generated using the Mordred package 71 in Python and ltered based on their utility in the context of chemical intuition, leaving a total of 106 descriptors. PLS regression was performed on the entire dataset, employing leave-one-out cross validation 72 (LOO- CV) to nd the optimal number of components to include in the model. This is achieved by using a number of components that causes the root mean squared error of prediction to be at a minimum, while also taking into account the principle of parsimony and avoiding overtting. Four components were used in the nal PLS model. To measure the robustness and ecacy of the model in predicting data, the dataset was split into a partition of 0.8/0.2 training data/ test data. LOO-CV was performed on the training dataset, the model was applied to predict the test dataset and the Q 2 was recorded as a measure of predictive accuracy. This was repeated 1000 times; the Q 2 reported hereafter is the median average of these repetitions.