MASSA算法:用于QSAR建模的训练和测试子集的自动合理采样。

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Gabriel Corrêa Veríssimo, Simone Queiroz Pantaleão, Philipe de Olveira Fernandes, Jadson Castro Gertrudes, Thales Kronenberger, Kathia Maria Honorio, Vinícius Gonçalves Maltarollo
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引用次数: 0

摘要

能够预测生物、毒性和药代动力学特性的QSAR模型被广泛用于在化学数据库中搜索潜在的生物活性分子。数据集构建这些模型的准备工作对生成的模型的质量有很大影响,采样要求将原始数据集分为训练集(用于模型训练)和测试集(用于统计评估)。这种抽样可以随机或合理地进行,但合理的划分更为优越。在本文中,我们介绍了MASSA,这是一种Python工具,可用于通过使用PCA、HCA和K模式探索分子的生物、物理化学和结构空间来自动采样数据集。当用于QSAR的变量不可用时,或者用相同的训练集和测试集构建多个QSAR模型时,所提出的算法非常有用,从而产生具有更低可变性和更好验证度量值的模型。即使QSAR/QSPR中使用的描述符与训练集和测试集分离时使用的描述符不同,也能获得这些结果,这表明该工具可用于建立多个QSAR/QS/PR技术的模型。最后,该工具还生成有用的图形表示,可以提供对数据的深入了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset’s preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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