When training, we used a mean squared error (MSE) loss for the regression task and a binary cross-entropy (BCE) loss for the classification task. We used the AdamW optimizer with a learning rate of 10 -5 and a batch size of 16. We trained the classification models for 5 epochs and the regression models for 50 epochs. The remaining hyper-parameters and training details can be found in the supporting information.