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

15 Sep 2021Chemical Engineering Journal 2021 9archive 2025-07-28

Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yaoa

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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Tasks

DiversityRetrosynthesisSingle-step retrosynthesis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Single-step retrosynthesis USPTO-50k RetroPrime (reaction class unknown) Top-1 accuracy 51.4 #29 of 35 Archive leaderboard report
Single-step retrosynthesis USPTO-50k RetroPrime (reaction class unknown) Top-10 accuracy 76.1 #29 of 35 Archive leaderboard report
Single-step retrosynthesis USPTO-50k RetroPrime (reaction class unknown) Top-3 accuracy 70.8 #29 of 35 Archive leaderboard report
Single-step retrosynthesis USPTO-50k RetroPrime (reaction class unknown) Top-5 accuracy 74.0 #29 of 35 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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