研究方向：化学与生物信息学：基于机器学习建立一类用于复杂样本和复杂数据(化学、生命、材料、医药学等领域)的分析新方法，实现计算与实验相结合的研究模式，提高实验研究和分析的效率和准确度。

1. 国家自然科学基金地区项目，22463013，基于机器学习的有机聚集诱导发光TADF材料设计研究，2025-01至2028-12，32万元，在研，主持

2. 贵州省科技厅青年引导项目，黔科合基础-[2024]青年322,基于机器学习的红光TADF材料快速设计，2024-8至2027-8，5万元，在研，主持

3. 市校联合项目，黔科平台人才[2021]1350-017，数据驱动的高效红光TADF材料的设计研究，2023-10至2025-10，4万元，在研，主持

4. 遵义医科大学博士启动资金，黔科平台人才[2020]-041，基于机器学习的AIE有机小分子荧光材料的快速开发，2023-3至2025-3，10万元，在研，主持。

目前以第一作者或共同第一作者发表SCI论文7篇，最高单篇文章影响因子达29.4，参与SCI文章发表13篇。

代表性文章：

1）Zhao Y*, He X, Wan Q, Combined machine learning models, docking analysis, ADMET studies and molecular dynamics simulations for the design of novel FAK inhibitors against glioblastoma, BMC Chemistry, 2024 DOI 10.1186/s13065-024-01316-x

2）Zhao Y*, Wan Q, He X, Construction of IRAK4 inhibitor activity prediction model based on machine learning, Molecular Diversity,  2024:1-12

3）Zhao Y*, Yu B, Li X, Development of organic aggregation-induced emission fluorescent materials based on machine learning models and experimental validation, Journal of Molecular Structure, 2024:139126

4）Zhao Y*, Chen K, Zhu L, Data-driven machine learning models for quick prediction of the Stokes shift of organic fluorescent materials, Dyes and Pigments, 2023: 111670.

5）Zhao Y, Fu C, Fu L, Lu, Z.*, & Pu, X*. Data-driven machine learning models for quick prediction of thermal stability properties of OLED materials, Materials Today Chemistry, 2021,22: 100625.

6）Luo, Y., Li, S., Zhao, Y., Li, C., Pang, Z., Huang, Y., Lu, Z*. An Ultraviolet Thermally Activated Delayed Fluorescence OLED with Total External Quantum Efficiency over 9%, Advanced Materials,2020, 32(32): 2001248.

7）Zhao, Y., Yuan, Y., Chen, J., Li, M., & Pu, X*. Chemometrics-enhanced high performance liquid chromatography strategy for simultaneous determination on seven nitroaromatic compounds in environmental water, Chemometrics and Intelligent Laboratory Systems, 2018,174,149-155.