李春艳，博士，副研究员，云南师范大学高层次引进人才。2022年6月毕业于厦门大学信息学院，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1项（在研），主持教育厅科研项目2项（结项2项，优秀2项）。近五年以第一作者和通讯作者在国际知名期刊《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Journal of Biomedical and Health Informatics》、《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10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期刊的审稿人、多个国际知名会议AAAI、ICANN等的Program Committee Member和参与国际知名公众号DrugAI的运营和维护。

研究方向：智能计算、面向生物医药的人工智能

科研项目

1. 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，2023，在研，主持；

2. 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），主持；

3. 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），主持。

发表论文

[1] Li Chunyan; Yao Junfeng; Su Jinsong; Liu Zhaoyang; Zeng Xiangxiang; Huang Chenxi. LagNet: Deep Lagrangian Mechanics for Plug-and-Play Molecular Representation Learning [C]. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 37(4), 5169-5177, 2023. (AAAI-23, Oral)

[2] 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), 2022.

[3] 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.

[4] 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.

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

[6] 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.

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

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] Hu Qian; Lin Fan*; Wang Beizhan*; Li Chunyan. MBRep: Motif-based Representation Learning in Heterogeneous Networks [J]. Expert Systems with Applications (ESWA), 190, p.116031, 2022.

[13] Xu Meiyan; Yao Junfeng*; Zhang Zhihong; Li Rui; Yang Baorong; Li Chunyan; Li Jun; Zhang Junsong*. Learning EEG topographical representation for classification via convolutional neural network [J]. Pattern Recognition, 105, p.107390, 2020.

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