Model Optimization. The baseline methods Random Forests and XGBoost are robust and do not require extensive parameter optimization. The quality of deep learning models is more dependent on the number of descriptors, hyperparameters and computational capabilities (e.g., use of GPU). We first report parameter optimization performed using one of the five-fold crossvalidation training sets. For the DNN, a series of 260 models were built using different combinations of optimizer learning rate, activation function, number of epochs and batch size for each descriptor type. The same dense layer architecture was maintained for the first round of grid search and once the best parameters were obtained, we tried to find an optimal dense layer structure for each descriptor. The DNNs in this study typically consisted of three to five layers with decreasing number of neurons as it moves forward that resulted in a pyramidal network structure, previously reported as an optimal setting for DNNs. The number of units in the input layer was defined based on the shape of the incoming descriptors and was reduced in the hidden layers and finally, the output layer consists of only one unit which uses a sigmoid activation function to return the output. Different combinations of the number of hidden layers and the number of neurons per hidden layer were examined and the best performing architecture was retained for both five-fold cross-validation and final validation. In the case of LSTM, along with the parameters considered for DNN, we also investigated the number of LSTM units. The grid search results for DNN are provided in the supporting information (S3).