李臻，男，博士，副教授，博士生导师，青岛大学特聘教授,现任智能科学与技术系主任。
招收软件工程学博、电子信息专博、计算机科学与技术学硕、计算机技术专硕，
主要研究方向为：人工智能与生物信息学。
教育工作经历
2003.09至2007.06 中国海洋大学， 获学士学位
2007.09至2014.01 中国海洋大学， 获博士学位
2010.09至2012.09 美国匹兹堡大学，联合培养博士
2014.01至2020.10 中国海洋大学， 讲师/副教授
2020.11至今 青岛大学， 副教授/特聘教授
主持项目
国家自然科学基金-面上项目，基于图深度学习模型的药物靶点亲和活性预测，2024-2027
青岛市关键技术攻关及产业化示范类项目，纺纱全流程智能控制管理系统关键技术研发与应用示范，2022-2024
科研成果
[1] Niu, D., Xu, L., Pan, S., Xia, L., & Li, Z*. (2024). SRR-DDI: A drug–drug interaction prediction model with substructure refined representation learning based on self-attention mechanism. Knowledge-Based Systems, 285, 111337.
[2] Niu, D., Zhang, L., Zhang, B., Zhang, Q., & Li, Z*. (2024). DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction. Journal of Biomedical Informatics, 104672
[3] Zhang, L., Niu, D., Zhang, B., Zhang, Q., & Li, Z*. (2024). FSRM-DDIE: few-shot learning methods based on relation metrics for the prediction of drug-drug interaction events. Applied Intelligence, 54(23), 12081-12094.
[4] Zhang, B., Niu, D., Zhang, L., Zhang, Q., & Li, Z*. (2024). MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction. BMC bioinformatics, 25(1), 275.
[5] Zhang, L., Niu, D., Zhang, B., Zhang, Q., & Li, Z*. (2024). Property-guided few-shot learning for molecular property prediction with dual-view encoder and relation graph learning network. IEEE Journal of Biomedical and Health Informatics.
[6] Xia, L., Xu, L., Pan, S., Niu, D., Zhang, B., & Li, Z*. (2023). Drug-target binding affinity prediction using message passing neural network and self supervised learning. BMC genomics, 24(1), 557
[7] Pan, S., Xia, L., Xu, L., & Li, Z*. (2023). SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features. BMC bioinformatics, 24(1), 334.
[8] Li, Z,Jiang, M., Wang, S., & Zhang, S. (2022). Deep learning methods for molecular representation and property prediction. Drug Discovery Today, 27(12), 103373.
[9] Wang, S., Song, T., Zhang, S., Jiang, M., Wei, Z., & Li, Z*. (2022). Molecular substructure tree generative model for de novo drug design. Briefings in bioinformatics, 23(2), bbab592.
[10] 山东省科技进步一等奖"面向领域的智能计算理论方法与产业技术应用"（第四位）
联系方式
Email: [email protected]
