Retrosynthesis prediction is a cornerstone of drug discovery, enabling the synthesis of novel therapeutic candidates. However, current deep learning models falter when navigating the unexplored chemical space essential for innovation. In these realistic scenarios, models face a challenging mixed out-of-distribution (OO...
Jia-Hang Shen, Hongxin Xiang, Yi-Ping Liu· Proceedings of the Thirty-Fi...· 0 citations
This work proposes KnowRetro (Knowledge-Guided Retrosynthesis Prediction), a chemically-aware framework that learns chemical knowledge from large-scale unlabeled molecules to enhance the accuracy and diversity of retrosynthesis prediction.
Yu-Jie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
Attention analysis reveals that the proposed novel method for predicting reaction conditions in organic synthesis effectively captures critical motifs closely related to synthetic reaction conditions and exhibits interpretable capabilities.
Jia-Yi Zhang, Yu-Jie Chen, Zhou Yu et al.· Journal of Chemical Informat...· 0 citations
Retrosynthesis, the process of predicting reactants from products, remains a critical challenge in computational chemistry and drug discovery. While recent deep learning methods have shown strong performance, they remain overly reliant on reaction datasets, which are limited in availability and quality. Large-scale unl...
Yujie Chen, Tengfei Ma, Zhou Yu et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global syn-thesizability knowledge into step-wise retrosyn-thetic prediction and improves planning accuracy and search efficiency under realistic retrosynthetic settings.
Yu-Jie Chen, Ajie Lin, Tengfei Ma et al.· Proceedings of the Thirty-Fi...· 0 citations
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