MASSA算法:用于QSAR建模的训练和测试子集的自动化理性抽样
Gabriel Corrêa Veríssimo1, Simone Queiroz Pantaleão2, Philipe de Olveira Fernandes1
1Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil.
Journal of computer-aided molecular design
|October 7, 2023
概括
本研究介绍了MASSA,这是一个Python工具,用于定量结构-活动关系 (QSAR) 建模中的理性数据集采样. MASSA通过智能地将数据分为训练和测试集来提高模型可靠性和验证指标.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 定量结构-活性关系 (QSAR) 模型对于识别生物活性分子至关重要.
- 有效的数据集准备,包括合理抽样进入培训和测试集,显著影响QSAR模型的质量.
- 目前用于数据集采样的方法可能是不理想的,特别是当描述符的可用性有限时.
研究的目的:
- 介绍MASSA,一个用于QSAR/QSPR建模中的理性数据集采样的自动化Python工具.
- 为了证明MASSA能够探索分子空间以改善数据集的划分.
- 为构建具有降低可变性和增强验证指标的QSAR模型提供一种方法.
主要方法:
- 使用主要组件分析 (PCA),层次集群分析 (HCA) 和K模式来探索分子空间.
- 实现一个自动化算法,根据分子性质将数据集划分为训练和测试集.
- 为数据洞察生成图形表示.
主要成果:
- 马萨允许自动,理性数据集采样,优于随机方法.
- 该工具可使用一致的训练/测试集构建多个QSAR模型,从而降低可变性.
- 即使QSAR描述符不同于用于数据集分离的描述符,也观察到更好的验证指标.
- 马萨的适用性跨越了不同的QSAR/QSPR技术.
结论:
- MASSA是提高QSAR/QSPR模型可靠性和性能的一个有价值的工具.
- 理性抽样方法提高了模型的一致性和预测准确性.
- MASSA提供了灵活性,并为化学信息应用提供了有价值的数据可视化.
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