Yang Zhang

Research Interests: AI for Microscopic Image Analysis, AI for Nucleic Acid Drug Design

About

Prof. Zhang is a Distinguished Professor at Tianjin University and a Visiting Professor at the University of Tokyo. He received his Ph.D. from the University of Cambridge and is a Fellow of the Royal Society of Chemistry (FRSC). His research focuses on the intersection of computational science and biology, with particular emphasis on artificial intelligence–driven biological computation, AI-powered super-resolution microscopy, and AI-based nucleic acid drug design.

In recent years, he has published more than 60 high-impact SCI papers in leading international journals, including Nature Biotechnology, Nature Methods, Nature Communications, Advanced Science (cover article), Med (Cell Press, cover article), Briefings in Bioinformatics, Bioinformatics, Journal of Biological Chemistry, Analytical Chemistry, and Journal of Medicinal Chemistry (cover article). He has been consecutively listed in the “World’s Top 2% Scientists” ranking from 2021 to 2023. He has also served as a reviewer for more than 60 internationally renowned journals, including Nature Biotechnology, Nature Methods, and Nature Communications.

He was invited to serve as the Editorial Board Member of BMC Biology, Communications Biology, PLOS Genetics, and the Lead Guest Editor of Trends in Analytical Chemistry.

Selected Publications

Accelerating drug discovery, development, and clinical trials by artificial intelligence

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Clinical Translation of Aptamers for COVID-19

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AI-powered microscopy image analysis for parasitology: integrating human expertise

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Biomolecular interaction prediction: the era of AI

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Generative AI powered by nucleic acid language modelenables one-round evolution of RNA aptamers

Zhang J, Zhang Y, Wang Y. (2026) Generative AI powered by nucleic acid language modelenables one-round evolution of RNA aptamers. Nature Biotechnology.

Single round evolution of RNA aptamers with GRAPE-LM

Zhang J1*, Zhang J1, Tang S, Liu C, Cai Y, Zeng H, Meng X, Zhang Y*, Wang Y*. (2026) Single round evolution of RNA aptamers with GRAPE-LM. Nature Biotechnology.

AI-empowered Super-Resolution Microscopy: A Revolution in Nanoscale Cellular Imaging

Li, S1., Meng, X1., Zhou, B., Tian, W., Chen, L*, & Zhang, Y*. (2025). AI-empowered Super-Resolution Microscopy: A Revolution in Nanoscale Cellular Imaging. Nature methods, 1-23

Rewired m⁶A epitranscriptomic networks link mutant p53 to neoplastic transformation

Xu, A., Liu, M., Huang, M. F., Zhang, Y., Hu, R., Gingold, J. A., ... & Lee, D. F. (2023). Rewired m6A epitranscriptomic networks link mutant p53 to neoplastic transformation. Nature communications, 14(1), 1694.

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Electrochemical approaches for breast cancer biomarkers: A voltammetric study of electrode potential scanning

Caiyv, L., Xiaokang H., Mohamed M., & Zhang, Y*. (2025). Electrochemical approaches for breast cancer biomarkers: A voltammetric study of electrode potential scanning. Cell Reports Physical Science, 6(5).

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(Cover) Biomolecular interaction prediction: the era of AI

Wang, H., Meng, X., & Zhang, Y*. (2025). Biomolecular interaction prediction: the era of AI. Advanced Science, e09501.

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(Cover) Accelerating drug discovery, development, and clinical trials by artificial intelligence

Zhang, Y*., Mastouri, M.. (2024). Accelerating drug discovery, development, and clinical trials by artificial intelligence. Med.

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Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19

Zhang, Y*., Juhas, M., & Kwok, C. K. (2023). Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19. Trends in Biotechnology, 41(4), 528-544.

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Deep learning for imaging and detection of microorganisms

Zhang, Y*., Jiang, H., Ye, T., & Juhas, M. (2021). Deep learning for imaging and detection of microorganisms. Trends in Microbiology, 29(7), 569-572.

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(Cover) AI-powered microscopy image analysis for parasitology: integrating human expertise

Feng, R., Li, S., & Zhang, Y*. (2024). AI-powered microscopy image analysis for parasitology: integrating human expertise. Trends in Parasitology, 40(7), 633-646.

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Predicting RNA structures and functions by artificial intelligence

Zhang, J., Lang, M., Zhou, Y., & Zhang, Y*. (2024). Predicting RNA structures and functions by artificial intelligence. Trends in Genetics, 40(1), 94-107.

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Biosensing detection of the SARS-CoV-2 D614G mutation

Zhang, Y*., Xi, H., & Juhas, M. (2021). Biosensing detection of the SARS-CoV-2 D614G mutation. Trends in Genetics, 37(4), 299-302.

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Discovery of G-quadruplex-forming sequences in SARS-CoV-2

Ji, D., Juhas, M., Tsang, C. M., Kwok, C. K., Li, Y., & Zhang, Y*. (2021). Discovery of G-quadruplex-forming sequences in SARS-CoV-2. Briefings in Bioinformatics, 22(2), 1150-1160.

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Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)

Li S., Yang Q., Jiang H., & Zhang, Y*. Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)[J]. Bioinformatics, 2020, 36(16): 4498-4505.

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A span-based joint model for extracting entities and relations of bacteria biotopes

Zuo, M., & Zhang, Y*. (2022). A span-based joint model for extracting entities and relations of bacteria biotopes. Bioinformatics, 38(1), 220-227.

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Dataset-aware multi-task learning approaches for biomedical named entity recognition

Zuo, M., & Zhang, Y*. (2020). Dataset-aware multi-task learning approaches for biomedical named entity recognition. Bioinformatics, 36(15), 4331-4338.

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Large language models for biomolecular analysis: From methods to applications

Feng, R., Zhang, C., & Zhang, Y*. (2024). Large language models for biomolecular analysis: From methods to applications. TrAC Trends in Analytical Chemistry, 171, 117540.

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Cell cycle-independent role of cyclin D3 in host restriction of influenza virus infection

Fan, Y., Mok, C. K. P., Chan, M. C. W., Zhang, Y., Nal, B., Kien, F., ... & Sanyal, S. (2017). Cell cycle-independent role of cyclin D3 in host restriction of influenza virus infection. Journal of Biological Chemistry, 292(12), 5070-5088.

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Proteomic and transcriptome profiling of G-quadruplex aptamers developed for cell internalization

Zhang, Y*., Wu, Y., Zheng, H., Xi, H., Ye, T., Chan, C. Y., & Kwok, C. K. (2021). Proteomic and transcriptome profiling of G-quadruplex aptamers developed for cell internalization. Analytical Chemistry, 93(14), 5744-5753.

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(Cover) Clinical Translation of Aptamers for COVID-19

Zhang, Y*., & Li, Y. (2023). Clinical Translation of Aptamers for COVID-19. Journal of Medicinal Chemistry, 66(24), 16568-16578.

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