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对比大规模多任务回归算法用于药物发现.

Eric J Martin1, Xiang-Wei Zhu2, Patrick Riley3

  • 1Novartis Biomedical Research, Emeryville, CA, 94608, USA. eric.martin@novartis.com.

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概括
此摘要是机器生成的。

与单任务模型相比,大规模多任务回归模型 (MMRMs) 显著改善了药物发现活动预测. 然而,当使用大型测试集时,它们的性能被高估了,这凸显了现实数据分割对于准确评估的重要性.

关键词:
算法比较的算法比较药物发现 药物发现计入算法是指指计入算法.多任务回归的多任务回归.在QSAR中使用QSAR.虚拟选是一个虚拟的选.

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科学领域:

  • 计算化学计算化学
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 大规模多任务回归模型 (MMRMs) 已经成为预测药物发现中的化合物生物活性的强大工具.
  • 这些模型在广泛的数据集上进行训练,提供与实验测量可比的准确性.

研究的目的:

  • 为了比较六个主要的MMRM (pQSAR,Alchemite,MT-DNN,MetaNN,澳门,IMC) 在生物活性概况归算方面的表现.
  • 评估不同培训/测试组分离对MMRM性能和准确性估计的影响.

主要方法:

  • 六名MMRM受过专家对相同的数据集进行培训,包括159个酶和4276个ChEMBL测定.
  • 模型使用75/25和99+/<1%的训练/测试集分割进行评估,以评估在不同数据可用性场景下的性能.
  • 对比分析包括定性评估和统计严谨性,与单任务随机森林回归 (ST-RFR) 进行基准测试.

主要成果:

  • 在生物活性概况归算方面,MMRM显著优于ST-RFR模型.
  • 根据训练/测试分割,性能差异很大;与99+/<1%分割相比,75/25分割导致模型准确性的大幅低估.
  • 虽然MMRMs擅长在训练数据分布中归因配置,但对于与训练集不同的化合物,它们的优势会减少.

结论:

  • 在训练数据的化学空间内,MMRM对于诸如命中发现,目标外预测和药物再利用等任务非常有效.
  • 数据分割策略的选择对MMRM的感知准确性产生了重大影响,需要使用现实的,较小的测试集来进行可靠的评估.
  • 对于探索已知的化学空间而言,MMRM的实用性最大,而它们对新型化学实体的性能则需要进一步调查.