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Updated: Jun 9, 2025

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基于机器学习和分子对接,对BindingDB数据库连接体对EGFR,HER2,雌激素,孕激素和NF-κB受体进行查

Parham Rezaee¹, Shahab Rezaee², Malik Maaza³

¹Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran; UNESCO-UNISA-iTLABS Africa Chair in Nanoscience and Nanotechnology (U2ACN2), College of Graduate Studies, University of South Africa (UNISA), Pretoria, South Africa.

Computers in biology and medicine

|October 26, 2024

概括

这项研究引入了GA-SVM-SVM:GA-SVM-SVM模型用于乳腺癌药物发现,识别了针对EGFR和ER等关键蛋白的有希望的配体,用于新疗法.

科学领域:

计算化学是一种计算化学.
生物信息学是一种生物信息学.
药物发现药物发现

背景情况:

乳腺癌是全球女性的主要癌症,需要新的治疗方法.
针对特定的子组 (例如,激素受体阳性/阴性,HER2阳性/阴性) 需要抑制EGFR,HER2,ER,NF-κB和PR等关键蛋白质.

研究的目的:

评估乳腺癌候选药物的虚拟查分类方法.
识别具有高精度和针对特定乳腺癌标的活性的新型配体.

主要方法:

使用二进制和多类分类模型,选择GA-SVM-SVM:GA-SVM-SVM.
从BindingDB数据库中对联体进行虚拟选.
应用分子对接和药物化学规则用于连接物优先排序.

主要成果:

该GA-SVM-SVM:GA-SVM-SVM模型实现了0.74准确度,0.73 F1得分和0.92 AUC.
为EGFR+HER2,ER,NF-κB和PR目标确定了成千上万的高精度配体.
分子对接揭示了约束能量在-15到-5kcal/mol之间.

结论:

该研究成功地确定并优先考虑了乳腺癌治疗的新型候选药物.
开发的模型和树图有助于探索针对性治疗的化学空间.
选择的配体显示出进一步临床前研究的巨大潜力.

关键词:

乳腺癌乳腺癌乳腺癌机器学习是机器学习.分子对接是分子对接.虚拟选是一个虚拟的选.

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