[1]Yuquan Li, et al. An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence. 2022. (第一作者，Nature子刊，当年IF=23.8，中科院1区)
[2]Yuquan Li, et al. Introducing block design in graph neural networks for molecular properties prediction[J]. Chemical Engineering Journal. 2021. (第一作者，当年IF=16.7，中科院1区)
[3]Xiaorui Wang†, Yuquan Li†, et al. RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal. 2021. (共同第一作者，当年IF=16.7，中科院1区)
[4]Pengyong Li†, Yuquan Li†, et al. TrimNet : learning molecular representation from triplet messages for biomedicine[J]. Briefings in Bioinformatics. 2021. (共同第一作者，当年IF=13.9，中科院2区)
[5]Xiaorui Wang, Xiaodan Yin, Dejun Jiang, Huifeng Zhao, Zhenxing Wu, Odin Zhang, Jike Wang, Yuquan Li, et al. Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites. Nature Communications. 2024. (合作作者，Nature子刊，当年IF=14.7，中科院1区)
[6]Shuo Liu, Jialiang Yu, Ningxi Ni, Zidong Wang, Mengyun Chen, Yuquan Li, et al. Versatile Framework for Drug–Target Interaction Prediction by Considering Domain-Specific Features. Journal of Chemical Information and Modeling. 2024. (合作作者，当年IF=5.6，中科院2区)
[7]Xiaodan Yin, Xiaorui Wang, Yuquan Li, et al. CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules. Journal of Chemical Information and Modeling. 2023. (合作作者，当年IF=5.6，中科院2区)
[8]Ruiqiang Lu, Jun Wang, Pengyong Li, Yuquan Li, et al. Improving drug-target affinity prediction via feature fusion and knowledge distillation. Briefings in Bioinformatics. 2023. (合作作者，当年IF=6.8，中科院2区)
[9]Xiaorui Wang, Chang-Yu Hsieh, Xiaodan Yin, Jike Wang, Yuquan Li, et al. Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center. Research. 2023. (合作作者，当年IF=8.5，中科院1区)
[10]Dejun Jiang, Huiyong Sun, Jike Wang, Chang-Yu Hsieh, Yuquan Li, et al. Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning. Briefings in Bioinformatics. 2022. (合作作者，当年IF=6.8，中科院2区)
