Recent works have shown the effectiveness of large-scale pre-training followed by task-specific fine-tuning to obtain a task-specific language model. Pre-training on a larger dataset followed by fine-tuning on sparse data is observed to provide better generalization and performance in the area of QSAR modeling . Extrapolating this to our study, the original pre-trained model of Mol2vec with millions of compounds is fine-tuned on a smaller DrugBankspecific dataset of around 11k Morgan fingerprints. We refer to these features as the Mol2vec fine-tuned model (or Mol2vec ft).