小分子机器学习中的覆盖偏差
Fleming Kretschmer1, Jan Seipp2, Marcus Ludwig1,3
1Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Nature communications
|January 9, 2025
概括
对于小分子的机器学习模型往往缺乏对生物分子结构的覆盖. 本研究引入了一种评估数据集覆盖范围的新方法,通过指导未来的数据创建来改善模型性能.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习是机器学习.
背景情况:
- 小分子机器学习预测结构的特性,用于诸如毒性和药物发现等应用.
- 端到端模型是趋势,但往往忽视了适用性和数据覆盖偏差的领域.
研究的目的:
- 研究用于机器学习的大型数据集中生物分子结构空间的覆盖范围.
- 开发评估数据集代表性和指导未来数据集创建的方法.
主要方法:
- 提出了一种基于最大共同边缘子图 (MCES) 问题的新型距离测量方法,以量化化学相似性.
- 开发了一种高效的计算方法,将整数线性编程和启发性边界结合起来,以解决MCES问题.
主要成果:
- 发现许多广泛使用的数据集对生物分子结构的覆盖范围不均.
- 这种缺乏统一的覆盖范围限制了在这些数据集上训练的机器学习模型的预测能力.
结论:
- 数据集覆盖范围是小分子机器学习的一个关键,经常被忽视的因素.
- 提出的基于MCES的距离和分歧评估方法可以指导创建更具代表性的数据集,提高模型性能.
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