In addition to sequence-to-sequence fine-tuning, we also examined Chemformer's application to discriminative tasks. In particular, we fine-tuned on molecular property prediction and biological activity tasks. Since we aim to improve efficiency in cheminformatics research, and since there should be significant synergy between tasks, we trained the models to optimise for multiple tasks simultaneously -an approach known as "multi-task learning" . Specifically, we trained molecular property models to solve three property prediction tasks simultaneously, and trained biological activity models to predict activity for 133 genes simultaneously, rather than having separate models for each task.