刘旦麟

       博士，副研究员。长期专注于计算机辅助药物设计以及人工智能技术在创新药物研发中方法开发和应用。重点针对蛋白质结构预测、分子相互作用与性质预测等药物研发上游环节中科学难点问题进行探索。成果已发表在Nat. Mach. Intell.、Nat. Commun.、Brief.Bioinform、J. Chem. Theory Comput.等国际知名期刊上。获得2022年上海市“超级博士后”激励计划，主持国家自然科学基金青年科学基金1项。

1. 人工智能辅助靶标识别与发现

2. 药物设计方法开发与应用

1.      国家自然科学基金青年项目，“多尺度视角下蛋白质作用位点语言模型及其可解释性研究”，项目批准号: 82404518，2025.01.01-2027.12.31，主持。

2.     华东师范大学“人工智能”种子专项项目—培育项目，2025.01-2025.12，主持。

1.    Wang B.*; Wang C.*; Chen J.*; Liu D.; Sun C.; Zhang J.; Zhang K.; Li H.; Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles, Nature Machine Intelligence, 2026, 1-20 

2.     Shi S.*; Miao R.*; Liu D*; Zhang Y.; Ruan S.; Xu Q.; Wang J.; Li H.; Li S.; ME-pKa: a deep learning method with multimodal learning for protein pKa prediction, Journal of Chemical Theory and Computation, 2026, 22(2): 1149-1163 

3.     Hu Q.*; Sun C.*; He H.*; Xu J.*; Liu D.*; Zhang W.; Shi S; hang K.; Li H.; Target-aware 3D molecular generation based on guided equivariant diffusion, Nature Communications, 2025, 16(1): 7928

4.     Miao R.*, Liu D.*, Mao L., Chen X., Zhang L., Yuan Z., Shi S., Li H., LI S., GR-pKa: A message-passing neural network with retention mechanism for pKa prediction. Briefings in Bioinformatics, 2024, 25.5: bbae408.

5.     Dai Y.*, Shen J.*, Zhai Z.*, Liu D.*, Chen J., Sun Y., Li P., Zhang K. High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion. ICML, 2024. 

6.     Liu D*, de Souza JV*, and Bronowska AK, Structure, dynamics, and small molecule ligand recognition of human-specific CHRFAM7A (Dupα7) nicotinic receptor linked to neuropsychiatric disorders., International Journal of Molecular Sciences, 2021, 22(11):5466. 

7.     Liu D, Richardson G, Benli FM, Park C, de Souza JV, Bronowska AK, Spyridopoulos I., Inflammageing in the cardiovascular system: mechanisms, emerging targets, and novel therapeutic strategies., Clinical Science, 2020 Sep 18;134(17):2243-2262. doi: 10.1042/CS20191213.

1.      上海市“超级博士后”激励计划