利用自动化、高通量、人工智能等现代化学技术，开发新型合成方法学，建立人工智能反应预测模型，聚焦PLPRO等呼吸系统疾病靶标开展新药研发。

1.合成方法学：围绕惰性化学键（碳氢键、碳碳键等）精准转化，定向开发方法学。

2.反应预测模型：利用自动化高通量技术，收集标准化的反应数据，建立人工智能反应预测模型，实现合成路径、反应条件或反应结果的精准预测，目前在研项目包括脱羧偶联、碳氢活化、多组分反应、不对称催化、光催化等类型的反应或反应体系。

3.呼吸系统疾病新药研发：开发新型化合物库合成以及活性筛选工具，研发抗冠状病毒的小分子药物。

We use Automation, HTE and AI technologies to develop novel methodology, build reaction prediction AI model, and develop new drug for respiratory system disease.

1.Develop novel methodology for selective functionalization of C–H, C–C and other inert bonds.

2.Build AI model for reaction outcome prediction based on the creation of standardized reaction database, ongoing projects are decarboxylative coupling, C–H activation, multicomponent reaction, asymmetric catalysis, photoredox catalysis, etc.

3.Develop new drug for respiratory system disease. We will focus on targets like PLPRO, develop novel tool for compound library synthesis and HTS to expedite the drug discovery process.

If we can achieve our goal of solving not only the theoretical problem of retrosynthesis analysis, of how to convert a target molecule into simpler precursors, but also the practical problem of forward reaction prediction, of how to assemble all the starting precursors to make the target molecule, it will definitely bring chemical synthesis into the era of Lego stacking.

We hope, in the future, chemists don't need to spend most of their time and energy on how to design routes and manually make molecules, but focusing on how to discover novel chemical reactions, what molecules to make, and how to use these molecules to solve problems.