Machine Learning Methods. Binary classification models for each target were generated using five different machine learning algorithms: k-nearest neighbors(k-NN) , Random Forest (RF) , Gradient Boosting Trees(GBT) , Support Vector Machines(SVM) , and Feed-Forward Neural Networks (FFNN) . All machine learning methods were implemented using the Scikit-learn Python library (0.22.1). For model building, training instances were represented by a feature vector (fingerprint) and associated to a class label ("active" / "inactive"). To avoid hyperparameter bias when comparing different models, the hyperparameters for each model were optimized using stratified 10-fold cross-validation in an exhaustive search over a limited hyperparameter space. To keep the search space small, only selected hyperparameters on each algorithm were optimized. Hereunder, we provide brief explanations on each algorithm and the hyperparameters considered for its optimization; all hyperparameters not explicitly indicated in the text were set as default.