RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions

Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a single-step template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two stages are accomplished with versatile Transformer models, respectively. RetroPrime achieves the Top-1 accuracy of 64.8% and 51.4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset. And the Top-1 accuracy is close to the state-of-the-art transformer-based method in the large dataset USPTO-full. It is known that outputs of the Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high chemical implausibility. These problems may limit the potential of Transformer-based methods in real practice, yet few works address both issues simultaneously. RetroPrime is designed to tackle these challenges.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Single-step retrosynthesis USPTO-50k RetroPrime Top-1 accuracy 51.4 # 10
Top-3 accuracy 70.8 # 4
Top-5 accuracy 74.0 # 7
Top-10 accuracy 76.1 # 7