The default training/validation/test 80/10/10 scaffold splitting from DeepChem library was utilized . The ChemBERTa-2-77M-MLM and ChemBERTa-2-77M-MTR from Hugging Face 20 were fine-tuned using T4 GPU on Google Colab using the simpletransformers library . To compensate for the class imbalance, automatic weight balancing was used. To prevent overfitting, early stopping based on validation loss was used during hyperparameter tuning. Each model was evaluated on the holdout test set with AUROC and confusion matrix. The code for the model training is released in a GitHub repository §.