李朋勇，博士，计算机科学与技术学院，副教授，研究生导师，毕业于清华大学生物医学工程系。中国人工智能学会智慧医疗专业委员会委员。主要研究方向为基于人工智能的药物研发，近年来基于图神经网络、自监督学习、强化学习等深度学习算法，围绕药物性质预测、药物靶点相互作用、药物设计生成等任务展开研究工作。在相关领域主流期刊和学术会议发表论文14篇，其中近5年以第一作者发表中科院一区论文4篇，CCF A类学术会议论文1篇。作为合作单位负责人主持国家自然科学联合基金重点项目，主持国家自然科学青年基金。指导学生获工信部举办的全国人工智能创新应用大赛一等奖，国家级大学生创新创业计划等。

发表文章

[1]Li, P., Wang, J., Qiao, Y., Chen, H., Yu, Y., Yao, X., et al. An effective self-supervised framework for learning expressive molecular global representations to drug discovery.Briefings in Bioinformatics.2021, 22(6): bbab109

[2]Li, P.^#, Li, Y.^#, Hsieh, C. Y., Zhang, S., Liu, X., Liu, H., et al. Trimnet: learning molecular representation from triplet messages for biomedicine.Briefings in Bioinformatics,2021, 22(4): bbaa266.

[3]Li, P.^#, Wang, J.^#, Li, Z., Qiao, Y., Liu, X., Ma, F., et al. Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks.IJCAI2021.

[4]Liu, X.^#,Li, P.^#, Meng, F., Zhou, H., Zhong, H., Zhou, J., et al. Simulated annealing for optimization of graphs and sequences.Neurocomputing, 2021,465, 310-324.

[5]Li, P.,Sun, M., Xu, Z., Liu, X., Zhao, W., & Gao, W. Site-selective in situ growth-induced self-assembly of protein–polymer conjugates into pH-responsive micelles for tumor microenvironment triggered fluorescence imaging.Biomacromolecules, 2018,19(11), 4472-4479.

[6]Li, Y.,Li, P., Yang, X., Hsieh, C. Y., Zhang, S., Wang, X., et al. Introducing block design in graph neural networks for molecular properties prediction.Chemical Engineering Journal, 2021,414, 128817.

[7]Liu, X., Luo, Y.,Li, P., Song, S., & Peng, J. Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.PLoS computational biology, 2021,17(8), e1009284.

[8]Ye, X., Li, Z., Ma, F., Yi, Z., Wang, J.,Li, P., et al. CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer.relation (QSAR), 2018,4(54869), 73770.

[9]Qiao, Y., Chen, H., Cao, L., Chen, L.,Li, P., et al..Deep Learning Track: Dense Matching for Nested Ranking.PASH at TREC, 2020

[10] Li, Y., Hsieh, C. Y., Lu, R., Gong, X., Wang, X.,Li, P., ... & Yao, X. (2022). An adaptive graph learning method for automated molecular interactions and properties predictions.Nature Machine Intelligence,4(7), 645-651.