聚焦于设计和开发人工智能算法对大规模生物医学数据进行处理、挖掘和分析，探索疾病的机理，为加速新药研发提供重要研究方案，主要包括：

一、疾病发现

随着高通量测序技术的不断发展，积累了大量DNA序列，高效且更准确的DNA序列数据处理和分析对疾病发现有着至关重要的作用。以构建高完整性、高质量、高分辨率的基因组序列为目标，研究内容包括序列匹配、序列纠错、单倍型基因组组装等。

二、药物设计

药物设计是一门涵盖生物学、化学、药理学等多学科领域的综合性研究过程，旨在寻找并开发可用于治疗特定疾病的药物。这一过程是建立在疾病发现基础之上，是实现“精准医疗”的核心内容。研究内容包括药物靶点预测、药物性质预测、分子生成等。

国家自然科学基金面上项目，面向多肽药物设计的深度学习方法研究（62372159），2024-2027。

国家自然科学基金青年项目，高通量测序的序列数据压缩算法研究（62102140），2022-2024。

招收2024年秋季入学硕士研究生4名

2021届：周珍冉、陶雯（研究生国奖2万，发表CCF B BIB论文，阿德莱德读博）

2022届：夏忻焱（AIIM论文在投）、沈祥振（研二上学期开始微软亚洲研究院实习、发表CCF B BIB论文和中科院二区IEEE-JBHI论文）

2023届：张艺才、李津松

主要论文 (^*为通讯作者)

Xiangzhen Shen, Zimeng Li,Yuansheng Liu^*, Bosheng Song, Xiangxiang Zeng, PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug-Drug Interaction Prediction.IEEE Journal of Biomedical and Health Informatics, 2024, 28(1): 569-579.(中科院一区)

Yuansheng Liu, Xiangzhen Shen, Yongshun Gong, Yiping Liu, Bosheng Song, Xiangxiang Zeng, Sequence Alignment/Map format: A comprehensive review of approaches and applications.Briefings in Bioinformatics, 2023, 24(5): bbad320.

Wen Tao,Yuansheng Liu^*, Xuan Lin, Bosheng Song, Xiangxiang Zeng, Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning.Briefings in Bioinformatics, 2023, 24(5): bbad371.

Xixi Yang, Li Fu, Yafeng Deng,Yuansheng Liu^*, Dongsheng Cao^*, Xiangxiang Zeng^*, GPMO: Gradient perturbation-based contrastive learning for molecule optimization,IJCAI, 2023: 4940-4948. (CCF A)

Bosheng Song, Xiaoyan Luo, Xiaoli Luo,Yuansheng Liu^*, Zhangming Niu^*, and Xiangxiang Zeng^*. Learning spatial structures of proteins improves protein-protein interaction prediction.Briefings in Bioinformatics, 2022, 23(2):bbab558.(生物信息学顶刊，中科院小类一区，IF: 11.622)ESI高被引论文

Xiangxiang Zeng, Xinqi Tu,Yuansheng Liu^*, Xiangzheng Fu, Yansen Su. Toward better drug discovery with knowledge graph,Current Opinion in Structural Biology, 2022, 72, 114-126. (中科院二区) ESI高被引论文，ESI热点论文

Jingxin Dong, Mingyi Zhao,Yuansheng Liu^*, Yansen Su^*, Xiangxiang Zeng. Deep learning in retrosynthesis planning: datasets, models and tools,Briefings in Bioinformatics, 2022, 23(1): bbab391.(生物信息学顶刊，中科院小类一区，IF: 11.622)ESI高被引论文

Yuansheng Liu, Jinyan Li. Hamming-Shifting graph of genomic short reads: efficient construction and its application for compression.PLOS Computational Biology, 2021, 17 (7), e1009229. (生物信息学顶刊，中科院小类一区)

Yuansheng Liu, Xiaocai Zhang, Quan Zou, Xiangxiang Zeng. Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers.Bioinformatics, 2021,37 (11), 1604-1606.(生物信息学顶刊，中科院小类一区)

Yuansheng Liu, Limsoon Wong, Jinyan Li. Allowing mutations in maximal matches boosts genome compression performance.Bioinformatics, 2020, 36(18): 4675-4681. (生物信息学顶刊，中科院小类一区)

Yuansheng Liu, Zuguo Yu, Marcel E. Dinger, Jinyan Li. Index suffix-prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression.Bioinformatics, 2019, 35(12):2066-2074.(生物信息学顶刊，中科院小类一区)

Yuansheng Liu, Leo Yu Zhang, Jinyan Li. Fast detection of maximal exact matches via fixed sampling of query k-mers and Bloom filtering of index k-mers.Bioinformatics, 2019, 35(22):4560-4567.(生物信息学顶刊，中科院小类一区)