深度学习驱动的天然LSD1抑制剂发现与优化用于阿尔茨海默病治疗
Deep learning-driven discovery and optimization of natural LSD1 inhibitors for the treatment of Alzheimer's disease.
摘要(中文译文)
阿尔茨海默病（AD）是一种常见的神经退行性疾病，目前有效的疾病修饰治疗手段有限。赖氨酸特异性去甲基化酶1（LSD1）已成为AD治疗的一个有前景的靶点。然而，现有的用于AD的LSD1抑制剂仍存在脑渗透性差、脱靶毒性和化学骨架匮乏等问题。在此，我们开发了一种多模态深度学习模型（PLM-CAFT-DTA）用于药物-靶标亲和力（DTA）预测。该模型整合了ChemBERTa、ESM-2、图注意力和交叉注意力融合，实现了高预测精度。利用该模型结合虚拟筛选和分子模拟，我们从超过70,000种天然产物的化合物库中鉴定出水飞蓟宾为先导化合物。经过合理修饰，获得了化合物S3，其LSD1抑制活性显著提高（IC₅₀ = 2.30 μM），效价约为水飞蓟宾的7倍。体外实验表明，S3具有良好的神经保护和抗氧化活性。在APP/PS1小鼠中，S3上调了海马H3K9me2，抑制了神经炎症和Aβ沉积，并改善了认知功能。本研究满足了AI辅助抗AD先导化合物发现的未满足需求，为早期药物开发提供了一种通用的DTA工具，并鉴定出S3作为一种具有强效抗AD疗效的新型LSD1抑制剂。
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder with limited effective disease-modifying treatments. Lysine-specific demethylase 1 (LSD1) has emerged as a promising target for AD therapy. However, current LSD1 inhibitors for AD still suffer from poor brain permeability, off-target toxicity, and chemical-scaffold scarcity. Herein, we developed a multimodal deep learning model (PLM-CAFT-DTA) for drug-target affinity (DTA) prediction. This model integrates ChemBERTa, ESM-2, graph attention, and cross-attention fusion to achieve high prediction precision. Using this model combined with virtual screening and molecular simulation, we identified silybin as a hit compound from a library of over 70,000 natural products. After rational modification, compound S3 was obtained with significantly improved LSD1 inhibition (IC₅₀ = 2.30 μM), approximately 7-fold more potent than the silybin. In vitro assays showed that S3 exhibited favorable neuroprotective and antioxidant activities. In APP/PS1 mice, S3 upregulated hippocampal H3K9me2, suppressed neuroinflammation and Aβ deposition, and improved cognitive function. By addressing unmet demands for AI-assisted anti-AD lead discovery, this study provides a generalized DTA tool for early-stage drug development, and identifies S3 as a novel LSD1 inhibitor with potent anti-AD efficacy.
如何引用
AMA
Zhonghua Li, Tiancheng Sun, Mengyu Han, Bingyu Xiao, Sijia Liu, Jiaxin Zhang, et al. Deep learning-driven discovery and optimization of natural LSD1 inhibitors for the treatment of Alzheimer's disease.. Bioorganic chemistry. 2026; doi:10.1016/j.bioorg.2026.110396.
APA
Zhonghua Li, Tiancheng Sun, Mengyu Han, Bingyu Xiao, Sijia Liu, Jiaxin Zhang, et al (2026). Deep learning-driven discovery and optimization of natural LSD1 inhibitors for the treatment of Alzheimer's disease.. Bioorganic chemistry. https://doi.org/10.1016/j.bioorg.2026.110396
GB/T 7714
Zhonghua Li, Tiancheng Sun, Mengyu Han, Bingyu Xiao, Sijia Liu, Jiaxin Zhang, et al. Deep learning-driven discovery and optimization of natural LSD1 inhibitors for the treatment of Alzheimer's disease.[J]. Bioorganic chemistry, 2026 doi:10.1016/j.bioorg.2026.110396.
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