SynCluster: Reaction Type Clustering and Recommendation Framework for Synthesis Planning

Tiantao Liu; Zheng Cao; Yuansheng Huang; Yue Wan; Jian Wu; Chang-Yu Hsieh; Tingjun Hou; Yu Kang

doi:10.1021/jacsau.3c00607

SynCluster: Reaction Type Clustering and Recommendation Framework for Synthesis Planning

JACS Au. 2023 Nov 17;3(12):3446-3461. doi: 10.1021/jacsau.3c00607. eCollection 2023 Dec 25.

Authors

Tiantao Liu¹, Zheng Cao², Yuansheng Huang¹, Yue Wan³, Jian Wu⁴, Chang-Yu Hsieh¹, Tingjun Hou¹, Yu Kang¹

Affiliations

¹ Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China.
² College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China.
³ Tencent Quantum Laboratory, Shenzhen 518057, Guangdong, China.
⁴ Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Abstract

AI-assisted synthesis planning has emerged as a valuable tool in accelerating synthetic chemistry for the discovery of new drugs and materials. The template-free approach, which showcases superior generalization capabilities, is seen as the mainstream direction in this field. However, it remains unclear whether such an end-to-end approach can achieve problem-solving performance on par with experienced chemists without fully revealing insights into the chemical mechanisms involved. Moreover, there is a lack of unified and chemically inspired frameworks for improving multitask reaction predictions in this area. In this study, we have addressed these challenges by investigating the impact of fine-grained reaction-type labels on multiple downstream tasks and propose a novel framework named SynCluster. This framework incorporates unsupervised clustering cues into the baseline models and identifies plausible chemical subspaces which is compatible with multitask extensions and can serve as model-independent indicators to effectively enhance the performance of multiple downstream tasks. In retrosynthesis prediction, SynCluster achieves significant improvements of 4.1 and 11.0% in top-1 and top-10 prediction accuracy, respectively, compared to the baseline Molecular Transformer, and achieves a notable enhancement of 13.9% in top-10 accuracy when combined with Retroformer. By incorporating simplified molecular-input line-entry system augmentation, our framework achieves higher top-10 accuracy compared to state-of-the-art sequence-based retrosynthesis models and improves over the baseline on the diversity and validity of reactants. SynCluster also achieves 94.9% top-10 accuracy in forward synthesis prediction and 51.5% top-10 Maxfrag accuracy in reagent prediction. Overall, SynCluster provides a fresh perspective with chemical interpretability and reinforcement of domain knowledge in the synthesis design. It offers a promising solution for improving the accuracy and efficiency of AI-assisted synthesis planning and bridges the gap between template-free approaches and the problem-solving abilities of experienced chemists.