Chemformer: a pre-trained transformer for computational chemistry

Transformer models coupled with a 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 the Chemformer model—a Transformer-based 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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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Single-step retrosynthesis USPTO-50k Chemformer Top-1 accuracy 54.3 # 3
Top-5 accuracy 62.3 # 14
Top-10 accuracy 63.0 # 14


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