Long-term engagement in the research field of AI for Science. Addressing the bottlenecks of high difficulty, cost, and long cycles in molecular design, my work focuses on AI-driven de novo design of small molecules, peptides, proteins, and small nucleic acids. I concentrate on the “Human Expertise + General AI + Specialized AI + Specialized Tools” four-in-one collaborative molecular design theory, and building a full-chain, closed-loop, dry-wet lab intelligent molecular design platform. The goal is to significantly improve the success rate and efficiency of molecular design. Related work has been published as the first author in journals such as Nature Machine Intelligence. Current research interests include:
- Fundamental AI Research: Multi-Agent Systems, Large Language Models, Molecular Representation Learning
- AI for Science Research: De Novo Design of Small Molecules/Peptides/Proteins/Small Nucleic Acids, Target Discovery, Synthesis Planning, Delivery Material Design
Call for Papers: Welcome to contact me for submissions to top-tier journals like Innovation/Exploration/iMeta. Priority review will be recommended.
Recruiting Students: Our team has numerous openings for Master’s and PhD students annually, with opportunities for PhD studies at Zhejiang University or internships at Tencent. I promise to never compete for first authorship, provide introductory training at the beginning of the semester, offer hands-on guidance throughout, and provide exclusive access to 200x4090 GPUs. I ensure that every Master’s student will publish at least one first-author paper in a Q1 journal/top-tier conference (A-level). Outstanding students will receive full support for publishing in high-impact journals.
🎓 Educations
2019.9 - 2024.6 Ph.D. - Lanzhou University, College of Chemistry and Chemical Engineering (Major: Cheminformatics, Supervisor: Prof. Xiaojun Yao)
2015.9 - 2019.6 B.S. - Qinghai University, College of Computer Science and Technology (Major: Computer Science and Technology)
🧑💻 Work Experience
2024.10 - Present Guizhou University, State Key Laboratory of Public Big Data/College of Computer Science and Technology, Distinguished Professor, Master’s Supervisor
2024.10 - Present Guizhou University, State Key Laboratory for Green Pesticide, Visiting Researcher
2022.7 - 2023.4 Beijing Academy of Artificial Intelligence (BAAI) Jie Fu’s Team, Research Intern
2020.8 - 2022.6 Tencent, Quantum Lab, Joint Ph.D. Program (Co-supervisor: Dr. Chang-Yu Hsieh)
🏛️ Academic Services
2025.8 - Present The Innovation (Q1, IF=25.7), Youth Editorial Board Member
2025.8 - Present iMeta (Q1 in Biology, IF=33.2), Youth Editorial Board Member
2025.1 - Present Guizhou Provincial Big Data Administration, AI Industry Direction, Head of Expert Group
2024.10 - Present China-Sri Lanka Belt and Road Joint Laboratory, Founding Construction Participant
2024.9 - Present Exploration (Q1, IF=22.5), Youth Editorial Board Member (Recipient of the Outstanding Youth Editorial Board Member Award)
📑 Research Projects
[1]Regional Project of National Natural Science Foundation of China, AI-based Key Gene Mining for RNA Interference in Wheat Aphids and Broad-spectrum dsRNA Design, PI
[2]Special Talent Introduction Project of Guizhou University, Research on New Methods for Multi-Constraint Small Molecule Generative Design based on Artificial Intelligence, PI
👥 Team Members
Xiao Zhang, 21 Ph.D., ①Nucleic Acid Pesticide Design. Xinyu Dong, 24 Ph.D., ①Molecule Generation, ②Cyclic Peptide Design. Guangyi Huang, 24 Ph.D., ①AI for Target Discovery. Shihang Wang, 25 Ph.D., ①Molecular Representation Learning. Mutian He, 25 Ph.D., ①Molecular Representation Learning, ②DEL. Daohong Gong, 25 Ph.D., ①Molecular Representation Learning. Hushuangyin Tang, 26 Ph.D., ①AI for Drug and Delivery System Design. Yong Zhou, 25 M.S., ①Agentic Nucleic Acid Pesticide Design. Huiyang Hong, 22 B.S., ①Agentic Molecular Property Prediction. Chaoyang Xie, 23 M.S., ①Molecular Property Prediction. Jun Zhou, 24 M.S., ①Yield Prediction and Synthesis Planning. Jun Zhang, 24 M.S., ①PPI, DDI Prediction. Yilun Zhang, 24 M.S., ①Enzyme Function Prediction, ②ABA Analogue Design. Nanwan Wu, 24 M.S., ①AI for Antibody-Drug Conjugate Design. Xixuan Luo, 24 M.S., ①DTI, DDI Prediction. Longbiao Zhang, 24 M.S., ①AI for Delivery System Design. Yuxuan Jiang, 25 M.S., ①Agentic Target Discovery. Weixun Chen, 25 M.S., ①Molecular Representation Learning.
*Co-supervisors include Prof. Gefei Hao, Prof. Qi Wang, and Prof. Xiaojun Yao.
Close Collaborator: Xiaorui Wang, Postdoc in Prof. Tingjun Hou’s group at Zhejiang University, researching AI for Synthesis Planning
📝 Publications
[1] Li et al. An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence IF=23.8◆
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[2] Li et al. Introducing block design in graph neural networks for molecular properties prediction. Chemical Engineering Journal IF=16.7◆
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[3] Li†, Li†. et al. TrimNet: learning molecular representation from triplet messages for biomedicine. Briefings in Bioinformatics IF=13.9◆
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[4] Wang†, Li†, et al. RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal IF=16.7◆
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†Equal contribution *Corresponding author ◆IF in the year of publication
To some extent, this reflects the journal’s recognition and the difficulty of publication in that year. Mainly too lazy to update the latest IF every 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]
🌟 Awards & Honors
2025.9 Exploration Journal, Outstanding Youth Editorial Board Member of 2025
2024.10 Guizhou University, Special Introduced Talent for First-class Discipline Construction
🏛️ Academic Activities
2023.9-Present Reviewer for journals such as iMeta, Nature Communications, Briefings in Bioinformatics, etc.
2021.9-Present Professional Member of the Chinese Association for Artificial Intelligence (CAAI), China Society of Plant Protection (CSPP), China Computer Federation (CCF), and Chinese Chemical Society (CCS).
2023.3 The 15th Graduate Academic Annual Conference of Lanzhou University, Talk Title: Chemistry × AI, Present and Future
🙌 Others
Super Smash Bros., War3 RPG/RTS, DNF
Owner of 5 * Legendary Fire Poker
I know clearly that the path of one person cannot be replicated by another; I lie in my own bed.
