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全面的机器学习促进了基于结构的PARP1抑制剂虚拟查.

Klaudia Caba1, Viet-Khoa Tran-Nguyen2, Taufiq Rahman3

  • 1Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.

Journal of cheminformatics
|April 6, 2024
PubMed
概括

研究人员开发了一种高度预测的机器学习评分功能,以发现用于癌症治疗的新型Poly ADP-ribose聚合酶1 (PARP1) 抑制剂. 这种新方法在识别潜在的候选药物方面明显优于传统方法.

关键词:
机器学习的评分功能是机器学习的评分功能.分子对接是分子对接.这是一种PARP1抑制剂.基于结构的虚拟选.针对特定目标的评分功能.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 在药理学中的机器学习.

背景情况:

  • 聚ADP-ribose聚合酶1 (PARP1) 是癌症治疗中的一个关键治疗标.
  • 机器学习 (ML) 评分功能为识别新型PARP1抑制剂提供了一个有前途的途径.

研究的目的:

  • 为了研究和开发尖端的,PARP1特定的ML评分功能,以提高药物发现.
  • 通过使用具有挑战性的数据集,严格评估这些函数的预测性能.

主要方法:

  • 利用了半合成训练数据,包括已知的PARP1抑制剂,由图形神经网络生成的与属性匹配的诱,以及已确认的不活跃物.
  • 构建了与训练数据不相似的分子的困难测试集.
  • 应用了五种监督学习算法,并分析了来自对接位置的蛋白质-连接体指纹 (PLEC) 和仅连接体特征.

主要成果:

  • 使用PLEC指纹识别了一种高度预测性的PARP1特定支向量机 (SVM) 基回归器.
  • 这种SVM模型在最具挑战性的测试集中在前1% (NEF1% = 0.588) 实现了高的规范化丰富系数.
  • 开发的评分函数与其他研究的函数和经典的基线评分函数相比,显示出更高的预测能力.

结论:

  • 一个新的PARP1特定的基于SVM的ML评分功能,利用PLEC指纹显示在虚拟选中的异常性能.
  • 这种方法显著推进了用于癌症治疗的强效PARP1抑制剂的发现.
  • 该研究强调了精心策划的数据集和先进的ML技术在药物发现中的有效性.