An AI-integrated pharmacophore and transcriptomic framework for rapid discovery of therapeutic leads for ischemic stroke
4 affiliations
- 1State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
- 2Department of Computer Science, RWTH Aachen University, Aachen 52074, Germany
- 3Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
- 4School of Pharmacy, The University of Nottingham, University Park, Nottingham NG7 2RD, UK
Abstract
Ischemic stroke (IS) is one of the main reasons causing death and disability worldwide, but there are no effective pharmacological treatments available. There is an urgent need to rapidly discover translatable therapeutic candidates. Artificial intelligence (AI) can be applied to accelerate therapeutic lead identification and mechanistic characterization. In this paper, we present a multi-layered AI-based discovery approach that combines a multi-pathway machine learning (ML) model, a Latent Gene Expression Graph Neural Network (LGE-GNN) model, and the Drug Decompose Net (DDN) model to perform structural pharmacophore-level analysis. Using this framework, we identified cannabidiol (CBD) as a proof-of-concept repurposed lead and further validated its therapeutic relevance in IS. Mechanism studies involving LGE-GNN prediction, DeepD2V modeling, and CUT&Tag sequencing indicated that the compound restores antioxidant status and induces angiogenesis through the NRF2/BMAL1 signaling axis. Moreover, DDN-directed structure–activity relationship (SAR) analysis prioritized several putative favorable substructures, including the (+)-dipentene-containing region of CBD, thereby providing a hypothesis-generating framework for future rational chemical optimization. It is presented in the form of an innovative screening system that can be highly integrated with artificial intelligence, medicinal chemistry, and state-of-the-art biological techniques. As indicated by the proposed framework, it can be an efficient strategy for rapid drug repurposing in ischemic stroke. By enabling the efficient identification of repurposable therapeutic leads, the framework can substantially reduce the time, cost, and labor required for early-stage drug development and may be further extended to support future drug discovery efforts.
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BibTeX
@article{li2026aiintegrated,
title = {{An AI-integrated pharmacophore and transcriptomic framework for rapid discovery of therapeutic leads for ischemic stroke}},
author = {Jinran Li and Zheng Zhou and Yongjun Zhao and Sai Liu and Long Chen and Yiting Shi and Haotian Li and Yuan Sun and Tingting Yao and Wei Jiang and Qun Xue and Zheying Zhu and Guangji Wang and Xinuo Li},
journal = {Targetome},
year = {2026},
month = jul,
doi = {10.48130/targetome-0026-0034},
url = {https://doi.org/10.48130/targetome-0026-0034},
note = {CC BY},
}
RIS
TY - JOUR
TI - An AI-integrated pharmacophore and transcriptomic framework for rapid discovery of therapeutic leads for ischemic stroke
AU - Jinran Li
AU - Zheng Zhou
AU - Yongjun Zhao
AU - Sai Liu
AU - Long Chen
AU - Yiting Shi
AU - Haotian Li
AU - Yuan Sun
AU - Tingting Yao
AU - Wei Jiang
AU - Qun Xue
AU - Zheying Zhu
AU - Guangji Wang
AU - Xinuo Li
PY - 2026
DA - 2026/07/01
JO - Targetome
DO - 10.48130/targetome-0026-0034
UR - https://doi.org/10.48130/targetome-0026-0034
AB - Ischemic stroke (IS) is one of the main reasons causing death and disability worldwide, but there are no effective pharmacological treatments available. There is an urgent need to rapidly discover translatable therapeutic candidates. Artificial intelligence (AI) can be applied to accelerate therapeutic lead identification and mechanistic characterization. In this paper, we present a multi-layered AI-based discovery approach that combines a multi-pathway machine learning (ML) model, a Latent Gene Expression Graph Neural Network (LGE-GNN) model, and the Drug Decompose Net (DDN) model to perform structural pharmacophore-level analysis. Using this framework, we identified cannabidiol (CBD) as a proof-of-concept repurposed lead and further validated its therapeutic relevance in IS. Mechanism studies involving LGE-GNN prediction, DeepD2V modeling, and CUT&Tag sequencing indicated that the compound restores antioxidant status and induces angiogenesis through the NRF2/BMAL1 signaling axis. Moreover, DDN-directed structure–activity relationship (SAR) analysis prioritized several putative favorable substructures, including the (+)-dipentene-containing region of CBD, thereby providing a hypothesis-generating framework for future rational chemical optimization. It is presented in the form of an innovative screening system that can be highly integrated with artificial intelligence, medicinal chemistry, and state-of-the-art biological techniques. As indicated by the proposed framework, it can be an efficient strategy for rapid drug repurposing in ischemic stroke. By enabling the efficient identification of repurposable therapeutic leads, the framework can substantially reduce the time, cost, and labor required for early-stage drug development and may be further extended to support future drug discovery efforts.
SN - 3071-0596
SN - 3070-3808
ER -