I have long devoted myself to research in AI for Science. To address the bottlenecks of high difficulty, high cost, and long cycles in molecular design, I work on AI-driven de novo design of small molecules, peptides, proteins, and small nucleic acids. My research centers on a four-in-one collaborative molecular design paradigm—”Expert Wisdom + General AI + Specialized AI + Wet-Lab Robotics”—and on building an end-to-end, dry–wet closed-loop platform for intelligent molecular design, with the goal of substantially improving the success rate and efficiency of the design process. Representative work has appeared as lead author in journals such as Nature Machine Intelligence and Advanced Science. Current research interests include:

• Foundational AI: agents and swarm intelligence; graph data processing and graph learning
• AI for Science: de novo generative design of small molecules, peptides, proteins, small nucleic acids, and materials; intelligent green pesticide discovery

Let’s talk: I’m always glad to hear from fellow researchers and students—happy to exchange ideas and explore collaborations (WeChat: yvquanli).

Call for papers: Submissions to TIDD / The Innovation / Exploration / iMeta are warmly welcomed—high-quality manuscripts will be recommended for expedited peer review.

Recruiting: Openings for Master’s/PhD students, joint-training candidates, and interns. My pledge: never grab first authorship, never berate students, never unjustly delay graduation. You can expect a proper onboarding, attentive day-to-day guidance, and ample computing resources. My goal is for every student to produce a first-author paper in a Q1 journal or A-level conference, with full support for outstanding students aiming at Nature/Cell/Science sub-journals.

🎓 Education

🧑‍💻 Work Experience

🏛️ Academic Services

📑 Research Projects

[1] National Data Bureau Pilot Dataset Program — Multimodal Plant-Protection Dataset of Crop Pests, Diseases, Weeds, and Pesticides, 2026, Sub-project PI
[2] NSFC Regional Project — AI-Driven Mining of RNAi Genes in Wheat-Field Aphids and RNAi Pesticide Design, ¥320K, 2026, PI
[3] Guizhou University Talent Introduction Program — Novel Methods for Multi-Constrained Small-Molecule Generative Design, ¥400K, 2024, PI

👥 Team Members

Close Collaborators (Supervisors): Gefei Hao¹, Xiaojun Yao², Wenchao Yang³, Joint Supervisor⁴
Close Partner: Xiaorui Wang, Special-term Associate Professor at School of Synthetic Biology, Shenzhen University of Technology, Research Direction: AI Synthesis Planning

📝 Selected 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* et al. Spectral decomposition of chemical semantics for activity cliffs-aware molecular property prediction. Advanced Science IF=14.1^▴ [HTML] [PDF]

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

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

*Corresponding Author ^†Co-first Author ^▴IF at Publication

All Publications

2026

• [2026b] Chaoyang Xie, Junhu Xu, Guangyi Huang, Shihang Wang, Mutian He, Xinyu Dong, Huiyang Hong, Xiaojun Yao, Qi Wang*, Yuquan Li*. Spectral decomposition of chemical semantics for activity cliffs-aware molecular property prediction[J]. Advanced Science, 2026: e17579. [HTML] [PDF]
• [2026a] Huiyang Hong, Xinkai Wu, Hongyu Sun, Chaoyang Xie, Qi Wang*, Yuquan Li*. Learning hierarchical interaction for accurate molecular property prediction[J]. Communications Chemistry,2026. [HTML] [PDF]

2025

• [2025c] Yanan Tian, Ruiqiang Lu, Xiaoqing Gong, Yuquan Li, Wei Zhao, Xiaorui, Wang, Xinming Jia, Qin Li, Yuwei Yang, Henry H. Y. Tong, Joel P. Arrais*, Huanxiang Liu*, Xiaojun Yao*. Enhancing Kinase-Inhibitor Activity and Selectivity Prediction Through Multimodal and Multiscale Contrastive Learning with Attention Consistency[J]. Nature Communications,2025,16:10860. [HTML] [PDF]
• [2025b] Xiaorui Wang†, Xiaodan Yin†, Xujun Zhang†, Huifeng Zhao, Shukai Gu, Zhenxing Wu, Odin Zhang, Wenjia Qian, Yuansheng Huang, Yuquan Li, Dejun Jiang, Mingyang Wang, Huanxiang Liu, Xiaojun Yao*, Chang-Yu Hsieh*, Tingjun Hou*. A virtual platform for automated hybrid organic-enzymatic synthesis planning[J]. Nature Communications,2025,16:10929. [HTML] [PDF]
• [2025a] Zhenglu Chen, Chunbin Gu*, Shuoyan Tan, Xiaorui Wang, Yuquan Li, Mutian He, Ruiqiang Lu, Shijia Sun, Chang-Yu Hsieh*, Xiaojun Yao*, Huanxiang Liu*, Pheng-Ann Heng. Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework[J]. Advanced Science, 2025. [HTML] [PDF]

2024

• [2024a] 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] [PDF]

2023

• [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

• [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]

🌟 Honors & Awards

🏛️ Academic Activities

🙌 Others

ARAM, War3 RPG/RTS, DNF
Proud owner of five “Fire-Stick” Dragon-Slaying Sabers in Legend of Mir
I know all too well that no two people walk the same path—so I lie in a bed that is mine alone.