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个人简介：Personal Profile 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. IJCAI 2021.[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.