We present MolT5 -a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.MolT5 allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time.Since MolT5 pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity.Furthermore, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation.Our results show that MolT5-based models are able to generate outputs, both molecules and captions, which in many cases are high quality 1 .