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Chemformer: A Pre-Trained Transformer for Computational Chemistry

Abstract

Transformer models coupled with Simplified Molecular Line Entry System (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics. These
models, however, are often developed specifically for a single application and can be very
resource-intensive to train. In this work we present Chemformer model – a Transformerbased
model which can be quickly applied to both sequence-to-sequence and discriminative
cheminformatics tasks. Additionally, we show that self-supervised pre-training can improve
performance and significantly speed up convergence on downstream tasks. On direct synthesis
and retrosynthesis prediction benchmark datasets we publish state-of-the-art results for top-
1 accuracy. We also improve on existing approaches for a molecular optimisation task and
show that Chemformer can optimise on multiple discriminative tasks simultaneously. Models,
datasets and code will be made available after publication.

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