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We apply a Transformer architecture, specifically BERT, to learn flexible andhigh quality molecular representations for drug discovery problems. We studythe impact of using different combinations of self-supervised tasks forpre-training, and present our results for the established Virtual Screening andQSAR benchmarks. We show that: i) The selection of appropriate self-supervisedtask(s) for pre-training has a significant impact on performance in subsequentdownstream tasks such as Virtual Screening. ii) Using auxiliary tasks with moredomain relevance for Chemistry, such as learning to predict calculatedmolecular properties, increases the fidelity of our learnt representations.iii) Finally, we show that molecular representations learnt by our model`MolBert' improve upon the current state of the art on the benchmark datasets.

Molecular representation learning with language models and domain-relevant auxiliary tasks