Wang Zihan, Lin Kangjie, Pei Jianfeng, Lai Luhua
BNLMS, Peking-Tsinghua Center for Life Sciences, College of Chemistry and Molecular Engineering, Peking University Beijing 100871 China
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
Chem Sci. 2024 Dec 6;16(2):854-866. doi: 10.1039/d4sc05946h. eCollection 2025 Jan 2.
Computer-assisted synthesis planning has emerged as a valuable tool for organic synthesis. Prediction of reaction conditions is crucial for applying the planned synthesis routes. However, achieving diverse suggestions while ensuring the reasonableness of predictions remains an underexplored challenge. In this study, we introduce an innovative method for forecasting reaction conditions using a combination of graph neural networks, reaction templates, and clustering algorithm. Our method, trained on the refined USPTO dataset, excels with a top-3 accuracy of 63.48% in recalling the recorded conditions. Moreover, when focusing solely on recalling reactions within the same cluster, the top-3 accuracy increases to 85.65%. Finally, by applying the method to recently published molecule synthesis routes and achieving an 85.00% top-3 accuracy at the cluster level, we demonstrate our approach's capability to deliver reliable and diverse condition predictions.
计算机辅助合成规划已成为有机合成的一种有价值的工具。反应条件的预测对于应用规划好的合成路线至关重要。然而，在确保预测合理性的同时获得多样化的建议仍然是一个未被充分探索的挑战。在本研究中，我们引入了一种创新方法，该方法结合图神经网络、反应模板和聚类算法来预测反应条件。我们的方法在经过优化的美国专利商标局（USPTO）数据集上进行训练，在召回记录条件方面，前3准确率达到63.48%，表现出色。此外，当仅专注于召回同一簇内的反应时，前3准确率提高到85.65%。最后，通过将该方法应用于最近发表的分子合成路线，并在簇级别实现了85.00%的前3准确率，我们证明了我们的方法能够提供可靠且多样化的条件预测。
