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Xutong Li

As seen in: Nature, Wiley Online Library, Gut Journal, MDPI, American Chemical Society, Science Magazine, Cell Press, Aging, Future Medicine
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Articles

Deep residual learning for molecular force fields

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By Xinyu yu Jiang, Mingan Chen, Duanhua Cao, Jie Yu, Runze Zhang, Zunyun Fu, Zhehuan Fan, Jiacheng Xiong, Xutong Li, Xiaomin Luo, Dan Teng, Mingyue Zheng, Chuanlong Zeng
| Nature Verified
Abstract Accurate descriptions of interactions between atoms are essential for molecular simulations used to study biology and support drug discovery. Existing force fields often face a trade-off between physical reliability, computational efficiency, and accuracy across unfamiliar molecules.
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Molecular-Anion Interfacial Engineering for Solid Electrolyte Interphase to Obtain High-Performance Aqueous Zinc-Ion Batteries

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By Rong Li, Xutong Li, Huanqian Shi
| Wiley Online Library Verified
Rong Li and Huanqian Shi contributed equally to this work. , , , et al. “Molecular-Anion Interfacial Engineering for Solid Electrolyte Interphase to Obtain High-Performance Aqueous Zinc-Ion Batteries.” Chemistry – A European Journal (2026): e71309. https://doi.org/10.1002/chem.71309 Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis

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By Ning Qu, Xiaochu Tong, Zhaokun Wang, Lehan Zhang, Xiaoya Zhang, Yuxin Xing, Jin Liu, Yitian Wang, Sulin Zhang, Mingyue Zheng, Xutong Li, Panpan Shao
| Nature Verified
Abstract Precision oncology faces critical challenges in interpreting complex cellular signals and predicting drug responses across heterogeneous cancer environments. Here, we present BioGDR, a multimodal interpretable deep learning framework that integrates structure-based predicted biological features, including differential gene expression and kinase inhibition profiles, eliminating the need for experimental measurements.
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