Long-term engaged in research in the field of AI for Science. Addressing the bottlenecks of high difficulty, high cost, and long cycles in molecular design, I conduct AI-driven de novo design of small molecules/peptides/proteins/small nucleic acids. My research focuses on the “Expert Wisdom + General AI + Specialized AI + Wet Lab Robots” four-in-one collaborative molecular design theory, building a full-chain dry-wet closed-loop intelligent molecular design platform, aiming to significantly improve the success rate and efficiency of molecular design. Relevant work has been published in journals such as Nature Machine Intelligence and Advanced Science as the lead author. Current research interests include:

• AI Basic Research: Multi-agent systems, Large Language Models (LLMs), Molecular Representation Learning
• AI for Science Research: De novo design of small molecules/peptides/proteins/small nucleic acids/delivery materials, Target Discovery, Synthesis Planning

Open for Exchange: Hoping to establish contact with experts to spark new ideas (WeChat: yvquanli).

Call for Papers: Welcome to contact me to submit to TIDD/Innovation/Exploration/iMeta journals; high-quality manuscripts can be arranged for a fast-track publication process.

Recruiting: Masters/PhDs/Joint Training/Interns wanted. I promise never to steal first authorship, never to be abusive, and never to delay graduation. I provide introductory training upon enrollment, detailed guidance throughout the process, and abundant computing resources to help every student produce a first-author Q1/Class A conference paper. Outstanding students will be fully supported to publish in subsidiary journals.

🎓 Education

2019.9 - 2024.6   M.S. & Ph.D. - Lanzhou University, College of Chemistry and Chemical Engineering (Major: Chemoinformatics, Supervisor: Prof. Xiaojun Yao)
2015.9 - 2019.6   B.S. - Qinghai University, School of Computer Science (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
2024.10 - Present    Guizhou University, State Key Laboratory of 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 Training (Co-supervisor: Dr. Changyu Hsieh)

🏛️ Academic Service

2026.1  - Present   《Innovation Drug Discovery》Journal (Subsidiary Journal Positioning), Founding Committee Member, Executive Editorial Board Member
2025.8  - Present   《Innovation》Journal (Comprehensive Q1, IF=25.7), Youth Editorial Board Member
2025.8  - Present   《iMeta》Journal (Biology Q1, IF=33.2), Youth Editorial Board Member
2024.9  - Present   《Exploration》Journal (Comprehensive Q1, IF=22.5), Youth Editorial Board Member (Awarded Outstanding Youth Editorial Board Member)
2024.8  - Present    China-Sri Lanka Joint Laboratory for Green Prevention and Control of Tea Pests and Diseases (Belt and Road), Founding Construction Participant
2026.1       The 6th International Conference on Green Plant Protection Technology Innovation, Organizing Committee
2025.1、2025.8   Guizhou Big Data Bureau Project AI Industry Direction Review Expert Group, Leader

📑 Research Projects

[1] National Data Bureau Dataset Pilot Project, National Crop Pest and Weed-Pesticide Multimodal Plant Protection Dataset, 2026, Project Leader
[2] National Natural Science Foundation of China (Regional Project), AI-based Mining of Aphid RNAi Genes in Wheat Fields and RNAi Pesticide Design, 320k, 2026, Host
[3] Guizhou University Talent Introduction Special Post Project, Research on New Methods for Multi-constraint Small Molecule Generative Design, 400k, 2024, Host

👥 Team Members

Deep Cooperation Supervisors: Gefei Hao¹, Xiaojun Yao², Qi Wang³, Wenchao Yang⁴ (In no particular order)
Deep Cooperation Partner: Xiaorui Wang, Postdoc in Prof. Tingjun Hou’s Group at Zhejiang University, Research Direction: AI 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^▴ [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 of publication year
To some extent represents the recognition and publication difficulty of that year, mainly too lazy to update the latest IF every year

All Publications

Year 2025

• [2025d] 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, 2025. (accept)
• [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]

Year 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]

Year 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]

Year 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]

Year 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 2025 Outstanding Youth Editorial Board Member Award
2024.10    Guizhou University World-Class Discipline Construction Special Talent Introduction

🏛️ Academic Activities

2025.11   Yangzhou University, Green Agriculture Protection Academic Lecture, Talk Title: AI-Aided Pesticide Design
2025.10   14th National Conference on Bioinformatics and Systems Biology, Drug Discovery Session, Talk Title: Multi-objective Gradient-guided Molecular Generation
2023.3     Lanzhou University 15th Graduate Academic Annual Conference, Talk Title: Chemistry × AI, Present and Future
2023.5-Present   Reviewer for journals such as iMeta, Nature Communications, etc.
2021.9-Present   Professional Member of CSPP, CAAI, CCF, CCS

🙌 Others

ARAM, War3 RPG/RTS, DNF
Owner of 5x “Fire Sticks” in World of Legend
I clearly know that the path between people cannot be replicated; I lie in my own bed.