Deimos:一种用于优化分组的新型自动化方法。应用于纳米信息学案例研究。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Molecular Informatics Pub Date : 2023-08-01 Epub Date: 2023-08-21 DOI:10.1002/minf.202300019
Dimitra-Danai Varsou, Haralambos Sarimveis
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引用次数: 0

摘要

在这项研究中,我们提出了一种优化分组的计算方法deimos,该方法应用于工程纳米材料(ENM)毒性相关特性的跨读预测。该方法基于混合整数优化程序(MILP)问题的公式化和求解,该问题自动同时执行特征选择,根据响应变量定义分组边界,并在每组中开发线性回归模型。对于每个组/区域,定义特征质心,以便将未测试的ENM分配给组。deimos MILP问题集成在更广泛的优化工作流程中,该工作流程在标准多元线性回归(MLR)、最小绝对收缩和选择算子(LASSO)模型和所提出的deimos多区域模型之间选择性能最佳的方法。通过基准ENM数据集的应用以及与其他预测建模方法的比较,证明了所建议方法的性能。然而,所提出的方法可以应用于ENM以外的化学实体的性质预测,并且不限于ENM毒性预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies.

In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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