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通过计算对接,药模拟和机器学习发现新的HER2抑制剂

Aseel Yasin Matrouk¹, Haneen Mohammad¹, Safa Daoud²

¹Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, 11942, Jordan.

Molecular informatics

|February 20, 2025

概括

研究人员开发了一种计算方法,以发现新的人类表皮生长因子受体2 (HER2) 抑制剂. 这种方法确定了三种新的抗HER2化合物,包括一种具有显著的实验活性,为与HER2相关的癌症提供新的治疗途径.

科学领域:

计算化学和药物发现
瘤学和癌症治疗学
药理学和药物化学药理学和药物化学

背景情况:

人类表皮生长因子受体2 (HER2) 是乳腺癌等侵袭性癌症中的关键瘤基因.
开发新的HER2抑制剂对于改善HER2驱动的恶性瘤治疗结果至关重要.

研究的目的:

创建一个用于识别新型HER2抑制剂的计算工作流.
使用机器学习生成和验证用于HER2抑制的药理.
选一个化学数据库,寻找新的潜在的抗HER2药物候选者.

主要方法:

灵活的对接被用来产生来自15个已知的HER2抑制剂的药.
机器学习-定量结构-活性关系 (ML-QSAR) 模型使用1,272种HER2抑制剂构建.
遗传功能算法 (GFA) 用于特征选择,结合Bagging和J48Graft分类器.

主要成果:

最好的ML-QSAR模型确定了两种关键的药 (Hypo_1和Hypo_2) 对HER2抑制剂活性至关重要.
国家癌症研究所 (NCI) 数据库的查产生了三个有前途的新型抗HER2线索.
最顶级的化合物显示了3.85μM的实验性反HER2 IC50,并具有独特的化学支架.

结论:

开发的计算工作流可以有效地识别新的HER2抑制剂.
已识别的线索代表了对抗HER2相关癌症的进一步发展的有希望的候选人.
这些新型抑制剂具有独特的化学结构,有可能克服与现有疗法相关的抗性机制.

关键词:

包装包装包装包装包装包装包装在HER2中,HER2是HER2.J48 剧本剧本剧本剧本剧本 ML-QSAR的使用方法灵活的对接灵活的对接

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