• Random Forest classifier (RF). Bayesian hyperparameter optimization for 20 iterations, training on the training split and measuring the ROC-AUC on the validation set (hyperparameters: number of trees between 50 and 500 with a step of 50, maximum tree depth between 5 and 12 with a step of 2, the minimum number of samples per split between 2 and 20, minimum number of samples per leaf between 2 and 100, number of features as a choice between the logarithm, the square root or 10% of the fingerprint size). We finally trained on the training set and evaluated the performance on the test set with 5 replicates.