基于增强多核功能SVR与PSO的乳腺癌PARP抑制剂的研究
Haohan Xue1, Ruixuan Zhang1, Xudong Yan1
1University, College of Computer Science and Technology, Qingdao, China.
Frontiers in pharmacology
|February 19, 2024
概括
研究人员开发了定量结构-活性关系模型,以抑制过度表达的PARP1,这是DNA修复中的酶. 使用三重内核函数的支持向量回归显示了设计新药分子的最强的预测能力.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 聚 (ADP-ribose) 聚合酶1 (PARP1) 对于易发生错误的微同质介导端结合 (MMEJ) DNA修复途径至关重要.
- 过度表达PARP1需要抑制治疗策略.
研究的目的:
- 通过使用定量结构-活性关系 (QSAR) 建模,研究化四环或五环二二二因多隆衍生物 (FTPDD) 的PARP1抑制作用.
- 确定指导新型PARP1抑制剂设计的关键因素.
主要方法:
- 使用启发式方法,基因表达编程,随机森林和支持向量回归 (SVR) 使用单个,双重和三重内核函数构建了六种QSAR模型.
- 支持矢量回归模型使用粒子群优化来进行多参数优化.
- 内核函数包括RBF,RBF+多项式和RBF+多项式+线性集成.
主要成果:
- 具有三重内核功能的SVR模型表现出卓越的预测准确性和稳定性,达到0.9353 (训练) 和0.9348 (测试) 的最佳R2和0.9090 (训练) 和0.8971 (测试) 的R2cv.
- 该模型确定了影响PARP1抑制的关键因素.
- 基于这些因素设计了六个新的FTPDD,并使用分子对接进行了验证.
结论:
- 使用三重内核函数的支持向量回归代表了QSAR的重大进步,增强了药物设计和选.
- 开发的QSAR模型有效地指导了新的PARP1抑制药物分子的设计.
- 新型FTPDD的成功设计突显了该模型在药物发现中的实用性.
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