Yuquan Li

Yuquan Li

Guizhou University

Yuquan Li, Distinguished Professor, Master’s Supervisor at Guizhou University. He has long been engaged in the research of artificial intelligence and scientific intelligence. His related work has been published as the first author in journals such as the Nature sub-journal “Nature Machine Intelligence”. So far, he has published 10 SCI papers with an H-index of 8 and a total of 326 citations. He serves as a youth editorial board member of the journal “Exploration”.

Main research directions include:

  • Basic AI research: large language models, graph learning, automated machine learning
  • AI for Science research: AI molecular design, AI material design

Recruiting now: Relying on the team, there are many master’s degree quotas and 1 doctoral quota each year. Students can be recommended to Zhejiang University and the Chinese University of Hong Kong for doctoral studies or to Tencent for internships! This team focuses on paper publication, providing in-depth scientific research training, specific topic selection and guidance, rich computing resources, and additional financial support to ensure that every master’s student can publish a first-author SCI Q1/CCF A paper before graduation. Outstanding students will be fully supported to publish first-author CNS sub-journal papers.

🎓 Educations

2019.9 - 2024.6  Master and PhD - Lanzhou University, College of Chemistry and Chemical Engineering (Major: Chemoinformatics, Supervisor: Professor Yao Xiaojun)
2015.9 - 2019.6  Bachelor - Qinghai University, College of Computer Science and Technology (Major: Computer Science and Technology)

🧑‍💻 Jobs

2024.10 - Present    Guizhou University, State Key Laboratory of Public Big Data Distinguished Professor, Master’s Supervisor
2024.10 - Present    Guizhou University, College of Computer Science and Technology Master’s Supervisor
2022.7 - 2023.4  Beijing Academy of Artificial Intelligence (BAAI), Jie Fu’s Team Research Intern
2020.8 - 2022.6  Tencent, Quantum Lab Joint Training (Co-supervisor: Dr. Hsieh Chang-Yu)

📑 Projects

[1] Guizhou University Talent Introduction Special Post Project, Research on New Methods for Multi-constraint Small Molecule Compound Generation and Design based on Artificial Intelligence, 2024.10-2028.10, 400,000 RMB, PI.

👥 People

Zhang Xiao,   ‘21 PhD student, Research direction: AI nucleic acid pesticide design.
Dong Xinyu, ‘24 PhD student, Research direction: Molecular generation design.
Huang Guangyi, ‘24 PhD student, Research direction: Enzyme active site prediction.
Gong Daohong, ‘22 Master’s student, Research direction: Molecular generation design.
Xie Chaoyang, ‘23 Master’s student, Research direction: Molecular property prediction (Q1 journal, under review).
Zhou Jun,  ‘24 Master’s student, Research direction: Protein function prediction.
Zhang Jun,  ‘24 Master’s student, Research direction: Protein interaction prediction.
Zhang Yilun, ‘24 Master’s student, Research direction: Enzyme function and active site prediction.
Wu Nanwan, ‘24 Master’s student, Research direction: AI antibody-drug conjugate design.
Luo Xixuan, ‘24 Master’s student, Research direction: AI drug-drug synergy prediction.
Zhang Longbiao, ‘24 Master’s student, Research direction: AI drug delivery material design.
Hong Huiyang, ‘22 Undergraduate, Research direction: Small molecule property prediction (Communications Chemistry, Q1 journal, under revision).
*Co-supervisors are Professor Hao Gefei and Professor Wang Qi from Guizhou University

Wang Shihang, ‘25 PhD student, Research direction: Automated molecular graph learning, AI+XDC drug design, Personal Homepage.
He Mutian, ‘25 PhD student, Research direction: Automated molecular graph learning.
*Co-supervisor is Professor Yao Xiaojun from Macao Polytechnic University

Deep Partner:
Wang Xiaorui, Postdoctoral fellow in Professor Hou Tingjun’s research group at Zhejiang University, Research direction: AI synthesis planning

📝 Publications

Representative Publications

[1] Li et al. An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence IF=23.8◆ [HTML] [PDF]

[2]Li et al. Introducing block design in graph neural networks for molecular properties prediction. Chemical Engineering Journal IF=16.7◆ [HTML] [PDF]

[3] Li†, Li†. et al. TrimNet: learning molecular representation from triplet messages for biomedicine. Briefings in Bioinformatics IF=13.9◆ [HTML] [PDF]

[4] Wang†, Li†, et al. RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal IF=16.7◆ [HTML] [PDF]

†Equal contribution *Corresponding authors

◆IF of the publication year, to a certain extent, represents the recognition and publication difficulty of the journal in that year

All Publications

2024

  • [2024c] Xiaorui Wang, Xiaodan Yin, Dejun Jiang, Huifeng Zhao, Zhenxing Wu, Odin Zhang, Jike Wang, Yuquan Li, Yafeng Deng, Huanxiang Liu, Pei Luo, Yuqiang Han, Tingjun Hou*, Xiaojun Yao*, Chang-Yu Hsieh*. Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites[J]. Nature Communications, 2024, 15(1): 7348. [HTML]
  • [2024b] Shuo Liu, Jialiang Yu, Ningxi Ni, Zidong Wang, Mengyun Chen, Yuquan Li, Chen Xu, Yahao Ding, Jun Zhang*, Xiaojun Yao*, Huanxiang Liu*. Versatile Framework for Drug–Target Interaction Prediction by Considering Domain-Specific Features[J]. Journal of Chemical Information and Modeling, 2024, 64(14): 5646-5656. [HTML] [PDF]
  • [2024a] Jianmin Wang, Jiashun Mao, Chunyan Li, Hongxin Xiang, Xun Wang, Shuang Wang, Zixu Wang, Yangyang Chen, Yuquan Li, Heqi Sun, Kyoung Tai No*, Tao Song*, Xiangxiang Zeng*. Interface-aware molecular generative framework for protein–protein interaction modulators[J]. Journal of Cheminformatics, 2024, 16(1): 142.[HTML] [PDF]

2023

  • [2023c] Xiaodan Yin†, Xiaorui Wang†, Yuquan Li, Jike Wang, Yuwei Wang, Yafeng Deng, Tingjun Hou, Huanxiang Liu, Pei Luo, and Xiaojun Yao*. CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules[J]. Journal of Chemical Information and Modeling, 2023, 63(20): 6169-6176. [HTML] [PDF]
  • [2023b] Ruiqiang Lu, Jun Wang, Pengyong Li, Yuquan Li, Shuoyan Tan, Yiting Pan, Huanxiang Liu, Peng Gao, Guotong Xie*, Xiaojun Yao*. Improving drug-target affinity prediction via feature fusion and knowledge distillation[J]. Briefings in Bioinformatics, 2023, 24(3): bbad145. [HTML]
  • [2023a] Xiaorui Wang†, Chang-Yu Hsieh†, Xiaodan Yin, Jike Wang, Yuquan Li, Yafeng Deng, Dejun Jiang, Zhenxing Wu, Hongyan Du, Hongming Chen, Yun Li, Huanxiang Liu, Yuwei Wang, Pei Luo, Tingjun Hou*, Xiaojun Yao*. Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center[J]. Research, 2023, 6: 0231. [HTML] [PDF]

2022

  • [2022b] Dejun Jiang†, Huiyong Sun†, Jike Wang†, Chang-Yu Hsieh, Yuquan Li, Zhenxing Wu, Dongsheng Cao*, Jian Wu*, Tingjun Hou*. Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning[J]. Briefings in Bioinformatics, 2022, 23(2): bbab597. [HTML] [PDF]
  • [2022a] Yuquan Li†, Chang-Yu Hsieh†, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Pengyong Li, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, Huanxiang Liu, Shengyu Zhang & Xiaojun Yao*. An adaptive graph learning method for automated molecular interactions and properties predictions[J]. Nature Machine Intelligence, 2022, 4(7):645-651. [HTML] [PDF]

2021

  • [2021c] Pengyong Li†, Yuquan Li†, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song*, Xiaojun Yao*. TrimNet: learning molecular representation from triplet messages for biomedicine[J]. Briefings in Bioinformatics, 2021, 22(4): bbaa266.[HTML] [PDF]
  • [2021b] Xiaorui Wang†, Yuquan Li†, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao*, Chang-Yu Hsieh*, Xiaojun Yao*. RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions[J]. Chemical Engineering Journal, 2021, 420: 129845. [HTML] [PDF]
  • [2021a] Yuquan Li, Pengyong Li, Xing Yang, Chang-Yu Hsieh, Shengyu Zhang, Xiaorui Wang, Ruiqiang Lu, Huanxiang Liu, Xiaojun Yao*. Introducing block design in graph neural networks for molecular properties prediction[J]. Chemical Engineering Journal, 2021, 414: 128817. [HTML] [PDF]

🏛️ Activities

  • Journal
    • Youth Editorial Board Member: 《Exploration》
    • Reviewer: 《Briefings in Bioinfomatics》
  • Society
    • Professional Member/Committee Member of China Association for Artificial Intelligence, China Computer Federation, and Chinese Chemical Society
  • Talk
    • 2023.3 The 15th Graduate Academic Annual Conference of Lanzhou University
      • Talk Title: Chemistry × AI, Now and Future
  • Social Service
    • Expert/Team Leader in the expert database of Guizhou Provincial Big Data Bureau

🙌 Others

  • DotA, War3 RPG/RTS, DNF
  • Owner of 5 legendary fire sticks
  • I know clearly that the paths between people cannot be replicated, and I lie on my own bed.