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团队简介：

Scientific AGITeam聚焦于科学通用人工智能（Scientific AGI），由云智药创与三维视觉创新团队构成，团队依托国家及省部级科研项目，聚焦人工智能与生物医药、数字媒体及三维重建交叉领域。团队以“理论创新—技术研发—产业落地”为研究闭环，致力于推动AI在药物研发、分子建模、三维场景重建和数字孪生等领域的应用与突破。团队成员涵盖智能算法、计算生物学、三维视觉与生成式AI等多方向青年学者，形成多学科融合的创新科研体系，并与国内外高校、药企保持紧密合作，推动科研成果高效转化。

团队负责人简介：

李春艳，博士，副研究员，博士生导师，云南师范大学高层次引进人才，中国计算机学会高级会员，中国人工智能学会会员。2022年6月毕业于厦门大学zoty中欧体育全站，获工学博士学位。主要研究方向为智能计算、面向生物医药的人工智能。主持国家自然科学基金2项（在研），主持云南省重点项目1项（结项），主持教育厅科研项目2项（结项2项，优秀2项）。近五年在国际知名期刊《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Journal of Biomedical and Health Informatics》、《Information Fusion》、《Journal of Chemical Theory and Computation》、《IEEE Transactions on Computational Biology and Bioinformatics》、《BMC Biology》、《Expert Systems with Applications》、《Neurocomputing》、《Briefings in Bioinformatics》、《Pattern Recognition》等SCI期刊和AAAI等CCF A类会议发表学术论文40余篇，研究成果在多个学术媒体公众号上进行了报道。社会服务方面，长期担任多个国际SCI期刊的编委和审稿人，并活跃于AAAI、KDD、ICANN等国际顶级会议的Program Committee，同时参与国际知名公众号DrugAI的运营与维护，积极推动学术交流与行业应用。

研究方向：

1.AI4Science：智能计算、面向生物医药的人工智能

云智药创团队依托国家及省部级科研项目，聚焦人工智能与生物医药交叉领域，重点攻克药物性质预测、分子分布外泛化、3D药物分子自监督学习、蛋白表征和设计、虚拟细胞等前沿问题。团队成员涵盖智能算法设计、生物信息学应用等多方向青年学者，形成“理论创新-技术研发-产业落地”的闭环研究体系。团队与国内外高校、药企保持紧密合作，致力于推动AI驱动的生物医药技术创新。团队以扎实的学术积累、活跃的产学研联动，成为区域人工智能领域的新锐力量。

2.Scientific AI：三维重建、数字媒体，几何、三维与智能创新融合

三维重建团队与厦门大学数字媒体计算中心合作，长期从事三维视觉与场景建模的基础理论与关键技术研究，聚焦面向真实复杂环境的高保真、高效率三维重建。主要研究方向包括：神经隐式表示、3D高斯泼溅（3D Gaussian Splatting）等新型可微渲染框架；生成式人工智能驱动的三维内容创建与编辑；以及面向自动驾驶、数字孪生与低空经济等国家重大需求的应用导向型三维感知系统，致力于构建“几何—语义—动态”一体化的下一代三维视觉范式，推动从重建到生成、从静态到4D时空连续建模的理论突破与技术落地。

人才招聘：

我们长期欢迎本科生、研究生、博士生、及青年博士加入我们的团队（2027级博士生目前有一个名额）。我们坚信，兴趣与热情是驱动卓越科研的核心力量。我们致力于将人工智能与药物研发深度融合，探索前沿科技，推动新药发现效率的提升；我们致力于将三维视觉与生成式人工智能深度融合，探索高保真、高效率三维重建前沿技术，推动从静态到4D时空连续建模的理论突破与应用落地，服务自动驾驶、数字孪生及低空经济等国家重大需求。在这里，你将与一群富有激情与创新精神的科研人员并肩作战，共同攻克科学难题，参与高水平项目研究，收获成长与突破。希望大家保持好奇、勇于挑战、协同合作，在AGI的舞台上书写属于自己的精彩篇章！

基本要求：

科研热情：有强烈的学习动力和对学术研究的兴趣。

项目经历：参与过完整的科研项目，能够展示自己在其中的贡献和思考。

学术能力：英语良好，能够阅读和撰写英文科研文档。

团队毕业生就业：

陈睿哲：华为

陈述高：东风汽车

宋佳：小米

石玉婷：锦州市传染病医院

张志强：澳门理工大学读博

欢迎对我们实验室感兴趣的同学，发送邮件至Email:59888543@qq.com邮箱，并附上你的简历以及其它证明你能力的材料。

团队代表工作：

主持部分科研项目：

1.基于图上分布外泛化的分子表征解纠缠研究，国家自然科学基金（面上项目），2025，在研；

2.基于3D自监督学习的药物分子属性预测研究，国家自然科学基金（地区项目），2023，在研；

3.云南AI生物医药产业模式创新与对策研究，云南省哲学社会科学规划社会智库项目（重点项目），结项；

4.基于图卷积神经网络的3D分子指纹表征学习，云南省教育厅，2020，结项（优秀）；

5.基于序列建模增强的小分子药物性质预测研究，云南省教育厅，2022，结项（优秀）；

发表的部分论文：

[1]Li Chunyan*; Qiao Shaojie; Zhou Guifei; Wang Jianmin. Disentangled Molecular Representation Learning with Context-Aware Codebook for OOD Generalization [J].Knowledge-Based Systems, Volume 339, 2026-04-22.

[2]Li Chunyan;Yao Junfeng; Su Jinsong; Liu Zhaoyang; Zeng Xiangxiang; Huang Chenxi. LagNet: DeepLagrangian Mechanics for Plug-and-Play MolecularRepresentation Learning[C].Proceedings of theThirty-SeventhAAAI Conference on Artificial Intelligence, 37(4), 5169-5177, 2023. (AAAI-23, Oral)

[3]Li Chunyan; Yao Junfeng*; Wei Wei*; Niu Zhangming; Zeng Xiangxiang*; Li Jin; Wang Jianmin. Geometry-based Molecular Generation with Deep Constrained Variational Autoencoder [J].IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 35(4), pp.4852-4861, 2024.

[4]Li Chunyan; Wei Wei; Li Jin; Yao Junfeng*;Zeng Xiangxiang*; LV Zhihan. 3DMol-Net: Learn 3D Molecular Representation using Adaptive Graph Convolutional Network Based on Rotation Invariance [J].IEEE Journal of Biomedical and Health Informatics(JBHI),Volume:26, Issue:10, 2022.

[5]Li Chunyan; Wang Jianmin; Niu Zhangming; Yao Junfeng*; Zeng Xiangxiang*. A Spatial-temporal Gated Attention Module for Molecular Property Prediction Based on Molecular Geometry [J].Briefings in Bioinformatics, 22(5), bbab078, 2021.

[6]Li Chunyan; Feng Jihua; Liu Shihu; Yao Junfeng*. A Novel Molecular Representation Learning for Molecular Property Prediction with a MultipleSMILES-based Augmentation [J].Computational Intelligence and Neuroscience,Vol. 2022.

[7]Li Chunyan^#; Liu Hongju^#; Hu Qian; Que Jinlong; Yao Junfeng*. A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks [J].Cells,8(9), p.977, 2019.

[8]Li Chunyan; Wang Jiaji; Wang Shuihua; Zhang Yudong*. A Review of IoT Applications in Healthcare [J].Neurocomputing, 565,2024.

[9]Li Chunyan.AI alignment is all your need for future drug discovery [J].Frontiers in Artificial Intelligence, doi:10.3389/frai.2025.1668794, 2025-11-03.

[10]Zhu Hang; Yuan Sisi; Tang Mingjing; Zhou Guifei; Hu Zhanxuan; Liu Zhaoyang; Li Jin; Wang Jianmin;Li Chunyan*（通讯作者）.Molecular graph-based invariant representation learning with environmental inference and subgraph generation for out-of-distribution generalization [J].Journal of Cheminformatics, Volume 18, article number 12, 2026-01-02.

[11]Chen Ruizhe;Li Chunyan*（通讯作者）; Wang Longyue; Liu Mingquan; Chen Shugao; Yang Jiahao; Zeng Xiangxiang. Pretraining graph transformer for molecular representation with fusion of multimodal information [J].Information Fusion. Volume 115, March 2025, 102784.

[12]LiuZhaoyang; XiaoYuteng; WangHonglei;LiChunyan*（通讯作者）; YinHongsheng*.BBM: A novel beta-binomial-distribution-based biclustering algorithm for mining m⁶A co-methylation patterns [J].Expert Systems with Applications (ESWA),https://doi.org/10.1016/j.eswa.2024.125121, 2024.

[13]Liu Mingquan;Li Chunyan*（通讯作者）; Chen Ruizhe; Cao Dongsheng; Zeng Xiangxiang*.Geometric Deep Learning for Drug Discovery [J].Expert Systems with Applications (ESWA), 2023.

[14]Min Xiaoping; Lu Fengqing;Li Chunyan*（通讯作者）. Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction[J].Current Pharmaceutical Design,27(15), pp.1847-1855,2021.

[15]Huang Wei;Li Chunyan*（通讯作者）; Ju Ying; Gao Yan. The Next Generation of Machine Learning in DDIs Prediction [J].Current Pharmaceutical Design,27(23), pp.2728-2736,PMID: 33504300,2021.

[16]WangJianmin; Wang Xun; Chu Yanyi;Li Chunyan; Li Xue; Meng Xiangyu; Fang Yitian; Tai No Kyong; Mao Jiashun; Zeng Xiangxiang. Exploring the Conformational Ensembles of Protein−Protein Complex with Transformer-Based Generative Model [J]. Journal of Chemical Theory and Computation, DOI: 10.1021/acs.jctc.4c00255, 2024.

[17]Wang Jianmin; Mao Jiashun;Li Chunyan; Xiang Hongxin; Wang Xun; Wang Shuang; Wang Zixu; Chen Yangyang; Li Yuquan; Tai No Kyong; Song Tao; Zeng Xiangxiang.Interface-aware Molecular Generative Framework for Protein-Protein Interaction Modulators [J]. Journal of Cheminformatics, 142, 2024.

[18]Wang Zixu; Chen Yangyang; Guo Xiulan; Li Yayang; Li Pengyong;Li Chunyan; Ye Xiucai; Sakurai Tetsuya. DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization [J]. Current Bioinformatics, Volume 20, Issue 1, 2025, pp.46-58.

[19]Lu Fengqing; Li Mufei; Min Xiaoping*;Li Chunyan; Zeng Xiangxiang*. De Novo generation of dual-target ligands using adversarial training and reinforcement learning [J].Briefings in Bioinformatics,22(6),bbab333,2021.

[20]HuQian;LinFan*;WangBeizhan*;LiChunyan. MBRep: Motif-based Representation Learning in Heterogeneous Networks [J].Expert Systems with Applications(ESWA),190, p.116031, 2022.

[21]XuMeiyan;YaoJunfeng*;ZhangZhihong;LiRui;YangBaorong;LiChunyan;LiJun;ZhangJunsong*. Learning EEG topographical representation for classification via convolutional neural network [J].Pattern Recognition,105, p.107390,2020.

[22]Jin Shuting; Liu Xiangrong; Xu Junlin; Xiang Hongxing; Shen Lian;Li Chunyan; Niu Zhangming; Jiang Yinhui. Adaptive symmetry-based adversarial perturbation augmentation for molecular graph representations with dual-fusion attention information [J].Information Fusion, https://doi.org/10.1016/j.inffus.2025.103062, 2025.

[23]Song Jia; Zhuang Wanru; Lin Yujie; Zhang Liang;Li Chunyan; Su Jinsong; He Song; Bo Xiaochen. Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment [C]. 2024IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1161-1168, 2024.

[24]Wen Chaoyu; Cai Li;Li Chunyan; Li Jin. TrustworthyCPI: Trustworthy Compound–Protein Interaction Prediction [J].IEEE Transactions on Computational Biology and Bioinformatics, 22(2), 2025, pp.732-743.

[25]Wen Chaoyu;Li Chunyan; Li Jin. Contrastive Prior Enhances the Performance of Bayesian Neural Network-based Molecular Property Prediction [J]. Expert Systems with Applications (ESWA), https://doi.org/10.1016/j.eswa.2025.130019. Volume 299, Part C, 1 March 2026, 130019.

[26]Zhou Peng; Wang Jianmin;Li Chunyan; Wang Zixu; Liu Yiping; Sun Siqi; Lin Jianxin; Wei Leyi; Cai Xibao; Lai Houtim; Liu Wei; Wang Longyue; Liu Yuansheng; Zeng Xiangxiang. Instruction multi-constraint molecular generation using a teacher-student large language model [J].BMC Biology, 23, 105, 2025.

[27]Zhao Tianze; Yin Zhenyu; Lu Yong; Cheng Shaocong;Li Chunyan. Insight mixed deep neural network architectures for molecular representation [J].Alexandria Engineering Journal, https://doi.org/10.1016/j.aej.2024.08.113, 2024.

[28]Lu Yong; Wang Chenxu; Wang Ze; Zhang Xukun; Zhou Guifei;Li Chunyan. Semi-supervised learning-based virtual adversarial training on graph for molecular property prediction [J].Alexandria Engineering Journal, https://doi.org/10.1016/j.aej.2024.11.110, 2024.

[29]Yacoub Taher; Depenveiller Camille; Tatsuma Atsushi;LiChunyan;et al. SHREC 2025: Protein surgace shape retrieval including electrostatic potential [J].Computers & Graphics, 2025.

[30]Zi Tong; Tang Mingjing; Jin Kui; Liu Yuxi;Li Chunyan; Gao Wei. DGATGRN: A Directed Graph Attention Network Framework for Inferring Gene Regulatory Networks from scRNA-Seq Data [C]. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.467-472, 2025-12-15.