AI for Chemistry · Synthesis Planning · Enzyme Catalysis

Xiaorui Wang

Xiaorui Wang, PhD, is an Assistant Professor, and PhD Supervisor at the School of Synthetic Biology, Shenzhen University of Advanced Technology. His research spans computational enzyme science and intelligent synthesis planning, with a focus on enzyme catalytic function prediction, intelligent enzyme design, enzymatic synthesis planning, and specialized scientific research agents.

Shenzhen, China · Shenzhen University of Advanced Technology

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Xiaorui Wang

About

Xiaorui Wang, PhD, is an Assistant Professor and PhD Supervisor at the School of Synthetic Biology, Shenzhen University of Advanced Technology. His research spans computational enzyme science and intelligent synthesis planning, with a focus on enzyme catalytic function prediction, intelligent enzyme design, enzymatic synthesis planning, and specialized scientific research agents. He has developed original algorithms and application platforms including RetroPrime, Parrot, EasIFA, and ChemEnzyRetroPlanner. His work has been published in international journals including Nature Communications, Chemical Reviews, Nature Machine Intelligence, Chem, Research, and Chemical Engineering Journal. He has published 44 SCI-indexed papers, including 12 first-author or co-first-author papers, with 1500+ citations. He is the principal investigator of projects supported by the Young Scientists Fund of the National Natural Science Foundation of China and the National Postdoctoral Researcher Program, Category C, and has received special funding from the Ministry of Education to attract overseas postdoctoral talent.

Research and Education Experience

2026.10-present, Shenzhen University of Advanced Technology, School of Synthetic Biology, Assistant Professor (Teaching and Research), Specially Appointed Associate Professor, and PhD Supervisor

2024.09-2026.09, Zhejiang University, College of Pharmaceutical Sciences, Pharmaceutical Sciences, Postdoctoral researcher, advisor: Prof. Tingjun Hou

2022.06-2024.08, Zhejiang University, College of Pharmaceutical Sciences, Visiting student, advisor: Prof. Tingjun Hou

2021.09-2024.08, Macau University of Science and Technology, Faculty of Chinese Medicine, Chinese Medicine, PhD student, advisor: Prof. Xiaojun Yao

2019.11-2021.06, Tencent, Quantum Laboratory, Research intern, advisor: Dr. Chang-Yu Hsieh

2018.09-2021.06, Lanzhou University, College of Chemistry and Chemical Engineering, Chemoinformatics, Master’s student, advisor: Prof. Xiaojun Yao

2014.09-2018.06, Lanzhou University, College of Chemistry and Chemical Engineering, Chemistry, Undergraduate student

News

  • 2026.04 I received an intended appointment as Assistant Professor at the School of Synthetic Biology, Shenzhen University of Advanced Technology. The group recruits PhD students, master’s students, research assistant professors, and research assistants in AI for Chemistry, enzyme catalysis, and synthetic biology. Interested students and early-career researchers are welcome to contact me at wangxiaorui@suat-sz.edu.cn.
  • 2026.01 Principal investigator of the Young Scientists Fund project: AI-based hybrid enzymatic-organic synthesis planning algorithms and intelligent application platform development.
  • 2025.06 Invention patent CN116227485B was granted: “A training method and apparatus for reaction condition prediction models.”
  • 2025 Our work on a hybrid enzymatic-organic synthesis planning platform was published in Nature Communications.
  • 2024 Our work on multimodal annotation of enzymatic active sites was published in Nature Communications.

Research Interests

AI-Assisted Synthesis Planning

My research in AI-assisted synthesis planning focuses on reaction prediction, condition recommendation, multistep synthetic route design, and route plausibility evaluation. I aim to integrate chemical knowledge, experimental data, and intelligent algorithms to develop interpretable, generalizable, and practically useful synthesis planning methods for drug molecules, functional molecules, and complex organic compounds. This line of work also supports automated and intelligent synthesis platforms by connecting computational prediction, experimental decision-making, and outcome evaluation.

Enzyme Catalysis Prediction

My research in enzyme catalysis prediction and intelligent enzyme design focuses on enzyme function recognition, catalytic site analysis, enzymatic reaction matching, enzyme-substrate interaction modeling, catalytic mechanism analysis, and enzyme design and engineering. By combining biocatalysis knowledge, protein information, and data-driven methods, I aim to develop efficient and interpretable strategies for enzyme catalysis prediction, supporting enzyme function annotation, substrate screening, enzyme molecular design, and green synthesis applications.

Specialized Agents for Synthesis Planning and Enzyme Catalysis

I am interested in developing specialized scientific agents for synthesis planning and enzyme catalysis. This direction focuses on the role of large language models in task understanding, knowledge integration, tool coordination, and result evaluation. The goal is to connect domain algorithms, chemical and biocatalytic knowledge, database resources, and computational tools to improve automated analysis and decision support for complex scientific problems.

Publications

# denotes co-first authorship, and * denotes corresponding authorship.

First-Author and Co-First-Author Papers

  1. Wang, X., Yin, X., Jiang, D., Zhao, H., Wu, Z., Zhang, O., Wang, J., Li, Y., Deng, Y., Liu, H., Luo, P., Han, Y., Hou, T., Yao, X., & Hsieh, C.-Y.* (2024). Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites. Nature Communications, 15, 7348. (IF: 15.7, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  2. Wang, X.#, Yin, X.#, Zhang, X., Zhao, H., Gu, S., Wu, Z., Zhang, O., Qian, W., Huang, Y., Li, Y., Jiang, D., Wang, M., Liu, H., Yao, X., Hsieh, C.-Y., & Hou, T.* (2025). A Virtual Platform for Automated Hybrid Organic-Enzymatic Synthesis Planning. Nature Communications, 16, 10929. (IF: 15.7, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  3. Wang, X, Li Y., Qiu, J., Chen, G., Liu, H., Liao, B., Hsieh, C.-Y., & Yao, X.* (2021). RetroPrime: A diverse, plausible and transformer-based method for single-step retrosynthesis predictions. Chemical Engineering Journal, 420, 129845. (IF: 13.2, CAS Zone 1 TOP, JCR Q1)
  4. Wang, X., Hsieh, C.-Y., Yin, X., Wang, J., Li, Y., Deng, Y., Jiang, D., Wu, Z., Du, H., Chen, H., Li, Y., Liu, H., Wang, Y., Luo, P., Hou, T., & Yao, X.* (2023). Generic interpretable reaction condition predictions with open reaction condition datasets and unsupervised learning of reaction center. Research, 6, 0231. (IF: 10.7, CAS Zone 1 TOP, JCR Q1)
  5. Bian, Y.#; Wang, X.#; Ma, X.#; Zhong, H.#; Pardaev, A.; Sun, L.; Liu, S.; Wang, X.; Hou, T.; Yang, W. A triangle association strategy for de novo reaction discovery. Chem 2026. DOI: 10.1016/j.chempr.2026.103204. (Co-first autor, CAS Zone 1 TOP, JCR Q1)
  6. Wang, J.#, Wang, X.#, Sun, H., Wang, M., Zeng, Y., Jiang, D., Wu, Z., Liu, Z., Liao, B., Yao, X., Hsieh, C.-Y., Cao, D., Chen, X., & Hou, T. (2022). ChemistGA: A chemical synthesizable accessible molecular generation algorithm for real-world drug discovery. Journal of Medicinal Chemistry, 65(18), 12482-12496. (Co-first author, IF: 6.8, CAS Zone 1 TOP, JCR Q1)
  7. Zhang, O.#, Lin, H.#, Zhang, X.#, Wang, X.#, Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., Hsieh, C.-Y., & Hou, T. (2025). Graph Neural Networks in Modern AI-Aided Drug Discovery. Chemical Reviews, 125(20), 10001-10103. (Co-first author, IF: 55.8, CAS Zone 1 TOP, JCR Q1)
  8. Tian, Y.#, Wang, X.#, Yao, X., Liu, H., & Yang, Y. (2023). Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism. Briefings in Bioinformatics, 24(1), bbac534. (Co-first author, IF: 7.7, CAS Zone 1 TOP, JCR Q1)
  9. Yin, X.#, Wang, X.#, Li, Y., Wang, J., Wang, Y., Deng, Y., Hou, T., Liu, H., Luo, P., & Yao, X.* (2023). CODD-Pred: A web server for efficient target identification and bioactivity prediction of small molecules. Journal of Chemical Information and Modeling, 63(20), 6169-6176. (Co-first author, IF: 5.3, CAS Zone 2 TOP, JCR Q1)
  10. Qian, W., Wang, X.#, Huang, Y., Kang, Y., Pan, P., Hsieh, C., & Hou, T.* (2024). Deep learning-driven insights into enzyme-substrate interaction discovery. Journal of Chemical Information and Modeling, 65(1), 187-200. (Co-first author, IF: 5.3, CAS Zone 2 TOP, JCR Q1)
  11. Qian, W.#, Wang, X.#, Kang, Y., Pan, P., Hou, T., & Hsieh, C.-Y. (2024). A general model for predicting enzyme functions based on enzymatic reactions. Journal of Cheminformatics, 16(1). (Co-first author, IF: 5.7, CAS Zone 2, JCR Q1)
  12. Li, S.#, Wang, X.#, Zhu, Y., Ge, J., Zhao, D., Xu, H., Hou, T., & Hsieh, C.* (2025). PepBAN: A deep learning framework with bilinear attention and adversarial learning for peptide-protein interaction prediction. Journal of Chemical Information and Modeling, 65(17), 9061-9074. (Co-first author, IF: 5.3, CAS Zone 2, JCR Q1)
Other Co-Authored Papers (updated periodically)
  1. Wu, Z., Zhang, O., Wang, X., Fu, L., Zhao, H., Wang, J., Du, H., Jiang, D., Deng, Y., Cao, D., Hsieh, C.-Y., & Hou, T.* (2024). Leveraging language model for advanced multiproperty molecular optimization via prompt engineering. Nature Machine Intelligence, 6(11). (IF: 23.9, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  2. Wang, J., Hsieh, C.-Y., Wang, M., Wang, X., Wu, Z., Jiang, D., Liao, B., Zhang, X., Yang, B., He, Q., Cao, D., Chen, X., & Hou, T.* (2021). Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nature Machine Intelligence, 3(10), 914-922. (IF: 23.9, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  3. Li, Y., Hsieh, C.-Y., Lu, R., Gong, X., Wang, X., Li, P., Liu, S., Tian, Y., Jiang, D., Yan, J., Bai, Q., Liu, H., Zhang, S., & Yao, X.* (2022). An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence, 4(7). (IF: 23.9, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  4. Zhao, H., Zhang, O., Jiang, D., Wu, Z., Du, H., Wang, X., Zhao, Y., Huang, Y., Ge, J., Hou, T.* & Yu, K.* (2025). Protein-peptide docking with a rational and accurate diffusion generative model. Nature Machine Intelligence, 7, 1308-1321. (IF: 23.9, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  5. Zhang, O., Wang, T., Weng, G., Jiang, D., Wang, N., Wang, X., Zhao, H., Wu, J., Wang, E., Chen, G., Deng, Y., Pan, P., Kang, Y., Hsieh, C.-Y., & Hou, T.* (2023). Learning on topological surface and geometric structure for 3D molecular generation. Nature Computational Science, 3(10), 849-859. (IF: 18.3, Nature Portfolio, JCR Q1)
  6. Zhu, Y., Li, S., Chen, J., Zhao, D., Wang, X., Li, Y., Liu, Y., Kong, Y., Zhang, B., Liu, C., Hou, T.* & Hsieh, C.-Y.* (2025). SynGFN: learning across chemical space with generative flow-based molecular discovery. Nature Computational Science, doi: 10.1038/s43588-025-00902-w. (IF: 18.3, Nature Portfolio, JCR Q1)
  7. Tian, Y., Lu, R., Gong, X., Zhao, W., Li, Y., Wang, X., Jia, X., Li, Q., Yang, Y., Tong, H. H. Y., Arrais, J. P., Yao, X., & Liu, H. (2025). Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning. Nature Communications, 16, 10860. (IF: 15.7, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  8. Wang, M., Li, S., Wang, J., Zhang, O., Du, H., Jiang, D., Wu, Z., Deng, Y., Kang, Y., Pan, P., Li, D., Wang, X., Yao, X., Hou, T., & Hsieh, C.-Y. (2024). ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning. Nature Communications, 15(1). (IF: 15.7, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  9. Deng, Y., Zhao, X., Sun, H., Chen, Y., Wang, X., Xue, X., Li, L., Song, J., Hsieh, C.-Y., Hou, T., Pan, X., Alomar, T., Ji, X., & Wang, X. (2025). RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints. Nature Communications, 16(1), 7012. (IF: 15.7, Nature Portfolio, Nature Index, CAS Zone 1 TOP, JCR Q1)
  10. Chen, Z., Gu, C., Tan, S., Wang, X., Li, Y., He, M., Lu, R., Sun, S., Hsieh, C., Yao, X., Liu, H., & Heng P.-A. (2025). Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework. Advanced Science, e08138. (IF: 14.1, CAS Zone 1 TOP, JCR Q1)
  11. Zhang, X., Jiang, L., Weng, G., Shen, C., Zhang, O., Liu, M., Zhang, C., Gu, S., Wang, J., Wang, X., Du, H., Zhang, H., Zhang, K., Wang, E.* & Hou, T.* (2025). HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes. Nucleic Acids Research, 53(W1), W306-W315. (IF: 13.1, CAS Zone 1 TOP, JCR Q1)
  12. Li, Y., Li, P., Yang, X., Hsieh, C.-Y., Zhang, S., Wang, X., Lu, R., Liu, H., & Yao, X.* (2021). Introducing block design in graph neural networks for molecular properties prediction. Chemical Engineering Journal, 414, 128817. (IF: 13.3, CAS Zone 1 TOP, JCR Q1)
  13. Yin, X., Hsieh, C.-Y., Wang, X., Wu, Z., Ye, Q., Bao, H., Deng, Y., Chen, H., Luo, P., Liu, H., Hou, T., & Yao, X.* (2024). Enhancing generic reaction yield prediction through reaction condition-based contrastive learning. Research, 7, 0292. (IF: 10.7, CAS Zone 1, JCR Q1)
  14. Su, H., He, L., Yu, X., Wang, Y., Yang, L., Wang, X., Yao, X., Luo, P.* & Zhang, Z.* (2024). Structural characterization and mechanisms of macrophage immunomodulatory activity of a novel polysaccharide with a galactose backbone from the processed Polygonati Rhizoma. Journal of Pharmaceutical Analysis, 14(7), 100974. (IF: 8.9, CAS Zone 1, JCR Q1)
  15. Wu, J., Zhu, Y., Wang, X., Li, Y., Yin, M., Wang, T., Han, Y., Kang, Y., Deng, Y., Wu, J., Hsieh, C. & Hou, T.* (2025). HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model. JACS Au, 5(7), 3140-3155. (IF: 8.7, CAS Zone 1 TOP, JCR Q1)
  16. Jiang, D., Zhao, H., Du, H., Kang, Y., Pan, P., Wu, Z., Zeng, Y., Zhang, O., Wang, X., Wang, J., Huang, Y., Zhao, Y., Hsieh, C.-Y., Cao, D., Sun, H., & Hou, T.* (2025). Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions. Acta Pharmacologica Sinica, 1-15. (IF: 8.4, CAS Zone 1, JCR Q1)
  17. Jiang, D., Ye, Z., Hsieh, C.-Y., Yang, Z., Zhang, X., Kang, Y., Du, H., Wu, Z., Wang, J., Zeng, Y., Zhang, H., Wang, X., Wang, M., Yao, X., Zhang, S., Wu, J., & Hou, T.* (2023). MetalProGNet: A structure-based deep graph model for metalloprotein-ligand interaction predictions. Chemical Science, 14(8), 2054-2069. (IF: 7.6, Nature Index, CAS Zone 1 TOP, JCR Q1)
  18. Wang, T., Wang, L., Zhang, X., Shen, C., Zhang, O., Wang, J., Wu, J., Jin, R., Zhou, D., Chen, S., Liu, L., Wang, X., Hsieh, C.-Y., Chen, G., Pan, P., Kang, Y., & Hou, T.* (2023). Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency. Briefings in Bioinformatics, 25(1). (IF: 7.7, CAS Zone 1, JCR Q1)
  19. Du, H., Jiang, D., Zhang, O., Wu, Z., Gao, J., Zhang, X., Wang, X., Deng, Y., Kang, Y., Li, D., Pan, P., Hsieh, C.-Y., & Hou, T.* (2023). A flexible data-free framework for structure-based de novo drug design with reinforcement learning. Chemical Science, 14(43), 12166-12181. (IF: 7.6, Nature Index, CAS Zone 1 TOP, JCR Q1)
  20. Yin, X., Wang, X., Wu, Z., Li, Q., Kang, Y., Deng, Y., Luo, P., Liu, H., Shi, G., Wang, Z., Yao, X., Hsieh, C.-Y., & Hou, T.* (2025). Syn-MolOpt: A synthesis planning-driven molecular optimization method using data-derived functional reaction templates. Journal of Cheminformatics, 17(1), 27. (IF: 7.1, CAS Zone 2, JCR Q1)
  21. Gu, S., Yang, Y., Zhao, Y., Qiu, J., Wang, X., Tong, H. H. Y., Liu, L., Wan, X., Liu, H., Hou, T. & Kang, Y.* (2024). Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios. Journal of Chemical Information and Modeling, 64(9), 3630-3639. (IF: 5.3, CAS Zone 2, JCR Q1)
  22. Zhao, H., Jiang, D., Shen, C., Zhang, J., Zhang, X., Wang, X., Nie, D., Hou, T.* & Kang, Y.* (2024). Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes. Journal of Chemical Information and Modeling, 64(6), 2112-2124. (IF: 5.3, CAS Zone 2, JCR Q1)
  23. Wang, J., Zeng, Y., Sun, H., Wang, J., Wang, X., Jin, R., Wang, M., Zhang, X., Cao, D., Chen, X., Hsieh, C. & Hou, T.* (2023). Molecular Generation with Reduced Labeling through Constraint Architecture. Journal of Chemical Information and Modeling, 63(11), 3319-3327. (IF: 5.3, CAS Zone 2, JCR Q1)
  24. Jiang, D., Zhao, H., Du, H., Deng, Y., Wu, Z., Wang, J., Zeng, Y., Zhang, H., Wang, X., Wu, J., Hsieh, C. & Hou, T.* (2023). How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation. Journal of Chemical Theory and Computation, 19(16), 5633-5647. (IF: 5.5, CAS Zone 1, JCR Q1)
  25. Zhang, W., Bai, H., Wang, Y., Wang, X., Jin, R., Guo, H., Lai, H., Tang, Y. & Wang, Y.* (2024). Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations. Pharmaceuticals, 17(3), 336. (IF: 4.8, CAS Zone 2, JCR Q1)
  26. Bai, H., Cheng, Y., Jia, S., Wang, X., Jin, R., Guo, H., Tang, Y., & Wang, Y.* (2025). Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products. Journal of Computer-Aided Molecular Design, 39(1), 44. (IF: 3.2, CAS Zone 2, JCR Q2)
  27. Ge, H., Mao, L., Zhao, J., Wang, Y., Shi, D., Yang, X., Wang, X., Liu, H., & Yao, X.* (2021). Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays. Journal of Computer-Aided Molecular Design, 35(5), 679-694. (IF: 3.2, CAS Zone 2, JCR Q2)
  28. Jiang, D., Du, H., Zhao, H., Deng, Y., Wu, Z., Wang, J., Zeng, Y., Zhang, H., Wang, X., Wang, E., Hou, T., & Hsieh, C.-Y. (2024). Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes. Physical Chemistry Chemical Physics, 26(13), 10323-10335. (IF: 2.9, CAS Zone 3, JCR Q2)

Funded Projects

  • 2026.01-2028.12 Young Scientists Fund, 22503082, Principal Investigator, RMB 300,000. Project title: AI-based hybrid enzymatic-organic synthesis planning algorithms and intelligent application platform development.
  • 2024.10-2026.10 National Postdoctoral Researcher Program, Category C, GZC20252369, Principal Investigator, RMB 240,000.

Tools and Platforms

Granted Invention Patent

  • Xiaorui Wang, Yu Kang, Tingjun Hou, Chang-Yu Hsieh, Dongsheng Cao, Yafeng Deng, Hui Shi. A training method and apparatus for reaction condition prediction models. Chinese invention patent, CN116227485B. Granted on 2025.06.24.

Honors

  • 2021-2024 Full Academic Scholarship for Postgraduate Students, Macau University of Science and Technology.
  • 2018-2021 Graduate Academic Scholarship, Lanzhou University.
  • 2017.10 Third Prize Undergraduate Scholarship, Lanzhou University.
  • 2017.09 Outstanding Graduate Training Scholarship, Lanzhou University.