Since the primary task of the ML models is to predict the outcome of a clinical trial, we present the results of the cluster-based scheme discussed in Section 3.5. The clusters identified by the k-means algorithm are depicted in Section S8.1 of Supporting Information. There are six clusters in all and about four of these six have roughly the same number of data points. results of 5-fold cross-validation are summarized in Table . We find that the highest accuracy is obtained using the RF classifier; the various performance measures computed on the predictions by the RF are 0.93 (ACC), 0.99 (TPR), 0.58 (TNR), 0.96 (F1-Pass) and 0.68 (F1-Fail) and the AdaBoost model results in 0.87 (ACC), 0.96 (TPR), 0.24 (TNR) and 0.93 (F1-Pass) and 0.32 (F1-Fail). The Receiver Operating curve (ROC) for the entire dataset (marked 'Whole Dataset' in the legend) is shown in brown in Figure and the area under the receiver operating curve, AUC is 0.67. A plot of the histogram of the ATC groups within each cluster (Section S8.2 of Supporting Information) reveals that ATC group C or ATC group N or ATC group A is the most dominant ATC group in these clusters. A plot of the ROC curves for the top three therapeutic categories -Nervous system (labeled 'ATC group N'), the Cardiovascular system (labeled 'ATC group C') and the Alimentary tract and metabolism (labeled 'ATC group A') are shown in Figure . In particular, the cluster-based approach results in accurate predictions for the trials labeled 'Pass' (F1-score of 0.96 for 'Pass') using the RF classifier.