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通过使用CLPSO增强的混合核SVM预测EGFR^{L858R/ T790M/ C797S}的抑制作用

Shaokang Li¹, Wenzhe Dong¹, Aili Qu²

¹College of Computer Science and Technology, Qingdao University, Qingdao 266071, China.

Pharmaceuticals (Basel, Switzerland)

|August 28, 2025

概括

对EGFR耐药性突变 (EGFR^{L858R/ T790M/ C797S}) 预测Osimertinib衍生物的疗效对于新的癌症药物来说至关重要. 混合核支载体机 (MIX-SVM) 模型准确预测了抑制作用,指导了新型EGFR抑制剂的设计.

科学领域:

计算化学
药物发现
癌症学

背景情况:

表皮生长因子受体 (EGFR) 突变,特别是EGFR^{L858R/ T790M/ C797S},对奥西默蒂尼布产生抗性.
在癌症治疗中,开发新型抑制剂至关重要.

研究的目的:

对EGFR^{L858R/ T790M/ C797S}突变的抑制作用进行预测.
为设计和选更有效的EGFR抑制剂提供指导.

主要方法:

开发了六种预测模型,包括启发式方法 (HM),随机森林 (RF),基因表达编程 (GEP),梯度增强决策树 (GBDT) 和两个支持向量机 (SVM) 变体 (多项式和混合内核).
模型描述器使用启发式方法或XGBoost进行选择,超参数通过全面学习粒子群优化器进行优化.
内部和外部验证采用一次性交叉验证 (QLOO2),五倍交叉验证 (Q5-fold2),一致性相关系数 (CCC),QF12和QF22.
分子对接分析探索了新型EGFR抑制剂的特性.

主要成果:

混合内核SVM (MIX-SVM) 模型表现出卓越的性能,达到高R2 (0.9445训练,0.9490测试) 和低RMSE (0.1659训练,0.1814测试).
获得了优秀的验证指标:QLOO2 (0. 9107),Q5-fold2 (0. 8621),CCC (0. 9835),QF12 (0. 9689) 和QF22 (0. 9680).
HM模型预测了162种新化合物的IC50值,最好的候选物通过PEA得到验证.

结论:

MIX-SVM模型提供了一个可靠的平台来预测Osimertinib衍生品的疗效.
这种方法为合理设计和有效选新型EGFR^{L858R/ T790M/ C797S}抑制剂提供了宝贵的指导.
这些发现有助于开发下一代EGFR抑制剂治疗耐药癌症.

关键词:

其他 EGFR 抑制剂一个小小的故事.综合学习粒子群优化器分子对接其他非小细胞肺癌支持向量的回归

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