Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation

Ischemic stroke’s complex pathophysiology demands
therapeutic
approaches targeting multiple pathways simultaneously, yet current
treatments remain limited. We developed an innovative drug discovery
pipeline combining a deep learning approach with experimental validation
to identify natural compounds with comprehensive neuroprotective properties.
Our computational framework integrated SELFormer, a transformer-based
deep learning model, and multiple deep learning algorithms to predict
NC bioactivity against seven crucial stroke-related targets (ACE, GLA, MMP9, NPFFR2, PDE4D, and eNOS). The pipeline encompassed IC50 predictions, clustering analysis,
quantitative structure–activity relationship (QSAR) modeling,
and uniform manifold approximation and projection (UMAP)-based bioactivity
profiling followed by molecular docking studies and experimental validation.
Analysis revealed six distinct NC clusters with unique molecular signatures.
UMAP projection identified 11 medium-activity (6 < pIC50 ≤
7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking
confirming strong correlations between binding energies and predicted
pIC50 values. In vitro studies using NGF-differentiated
PC12 cells under oxygen-glucose deprivation demonstrated significant
neuroprotective effects of four high-activity compounds: feruloyl
glucose, l-hydroxy-l-tryptophan, mulberrin, and
ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase
activity and lipid peroxidation, suppressed TNF-α
expression, and upregulated BDNF mRNA levels. Notably,
mulberrin and ellagic acid showed superior efficacy in modulating
oxidative stress, inflammation, and neurotrophic signaling. This study
establishes a robust deep learning-driven framework for identifying
multitarget natural therapeutics for ischemic stroke. The validated
compounds, particularly mulberrin and ellagic acid, are promising
for stroke treatment development. Our findings demonstrate the effectiveness
of integrating computational prediction with experimental validation
in accelerating drug discovery for complex neurological disorders.

缺血性脑卒中复杂的病理生理学机制要求同时靶向多条通路的治疗策略，但现有治疗手段仍存在局限。我们开发了一款创新性药物发现管线，将深度学习方法与实验验证相结合，以筛选具备全面神经保护活性的天然化合物。本计算框架整合了基于Transformer的深度学习模型SELFormer与多种深度学习算法，用于预测天然化合物（NC）针对7个关键脑卒中相关靶点——血管紧张素转换酶（ACE）、α-半乳糖苷酶（GLA）、基质金属蛋白酶9（MMP9）、神经肽FF受体2（NPFFR2）、磷酸二酯酶4D（PDE4D）及内皮型一氧化氮合酶（eNOS）——的生物活性。该管线涵盖IC50预测、聚类分析、定量构效关系（QSAR）建模、基于均匀流形近似与投影（UMAP）的生物活性谱分析，随后开展分子对接实验与实验验证。分析结果显示，可划分为6个具备独特分子特征的天然化合物簇。UMAP投影共筛选出11个中等活性（6 < pIC50 ≤7）化合物与57个高活性（pIC50 >7）化合物，分子对接实验证实结合能与预测的pIC50值之间存在显著相关性。采用经神经生长因子（NGF）诱导分化的PC12细胞开展氧糖剥夺体外实验，结果显示4种高活性化合物——阿魏酰葡萄糖、L-羟基色氨酸、桑黄素及鞣花酸——具备显著的神经保护作用。上述化合物可提升细胞存活率、降低乙酰胆碱酯酶活性与脂质过氧化水平、抑制肿瘤坏死因子-α（TNF-α）的表达，并上调脑源性神经营养因子（BDNF）的mRNA表达水平。值得注意的是，桑黄素与鞣花酸在调控氧化应激、炎症反应及神经营养信号通路方面展现出更优的功效。本研究建立了一套稳健的深度学习驱动框架，用于筛选针对缺血性脑卒中的多靶点天然治疗药物。经验证的化合物，尤其是桑黄素与鞣花酸，有望用于脑卒中治疗药物的研发。本研究结果证实，将计算预测与实验验证相结合可有效加速复杂神经系统疾病的药物发现进程。