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通过综合深度学习和基于结构的药物重新定位策略,识别潜在的非毒性人类BTK抑制剂

Muhammad Waleed Iqbal^1,2, Xinxiao Sun^1,2, Qipeng Yuan^1,2

¹State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, P.R. China.

|December 1, 2025

概括

研究人员使用计算方法确定了用于自身免疫疾病的新型布鲁顿氨酸激酶 (BTK) 抑制剂. 三种有前途的候选药物 - - gozetotide,micafungin和candicidin - - 在 silico 中显示出安全性和有效性,需要进一步的临床研究.

科学领域:

计算机化药物发现.
药理学药理学是指药理学的学科.
免疫学免疫学免疫学

背景情况:

布鲁顿的氨酸激酶 (BTK) 通过B细胞信号过活化与自身免疫性疾病的发病有关.
现有的BTK抑制剂,如易布鲁替尼,由于功效低且毒性高,因此面临限制.
需要更安全,更有效的BTK向疗法来治疗自身免疫性疾病.

研究的目的:

开发一个in silico管道,用于识别新的,更安全的BTK抑制剂.
为了选FDA批准的药物潜在的BTK抑制活性.
评估已识别的候选药物的安全性和结合稳定性.

主要方法:

利用管道整合深度学习,基于结构的药物重新定位和毒性分析.
在对BTK向化合物的精选数据集上训练了一个人工神经网络 (ANN).
应用分子对接和模拟 (RMSD,RMSF,RoG,PCA,FEL,DCCM) 用于化合物选和稳定性评估,随后进行基于GNN的毒性分析.

主要成果:

从FDA批准的图书馆中确定了gozetotide,micafungin和candicidin作为顶级潜在的BTK抑制剂.
分子模拟证实了这些化合物在BTK活性部位内的稳定结合特征.
基于GNN的毒性分析表明,对于已识别的化合物,没有显著的安全问题.

结论:

计算方法成功地确定了gozetotide,micafungin和candicidin作为潜在的安全有效的BTK抑制剂.
这些化合物代表了对自身免疫性疾病有前途的治疗候选者.
需要进行进一步的临床试验,以验证这些重新定位的药物的疗效和安全性,作为BTK向疗法.

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