利用机器学习技术预测马来西亚克卢卢蜂蜜中使用立法会tof - ms检测到的化合物的血脑屏障通透性。

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
R Edros, T W Feng, R H Dong
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引用次数: 0

摘要

目前的硅建模技术,如分子动力学,通常集中在色谱分析中具有最高浓度的化合物,用于生物活性筛选。因此,它们减少了对劳动密集型体外研究的需要,但限制了广泛的色谱数据和化合物分类分子多样性的利用。化合物通过血脑屏障(BBB)的渗透性是中枢神经系统(CNS)药物开发中的一个关键问题,这一限制可以通过应用化学信息学和无代码机器学习(ML)来解决。在本研究开发的4个模型中,选择了在内部和外部验证中性能最稳健的随机森林(Random Forest, RF)算法进行模型构建,准确率(ACC)分别为87.5%和86.9%,曲线下面积(AUC)分别为0.907和0.726。利用射频模型对Kelulut蜂蜜中285种采用液相色谱四极杆飞行时间质谱法(立法会tof - ms)检测到的化合物进行分类;其中,通过94个描述符筛选出140个化合物。17种化合物被预测能渗透血脑屏障,显示出它们作为治疗神经退行性疾病药物的潜力。我们的结果强调了利用ML模式识别从整个色谱数据池中识别具有神经保护潜力的化合物的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey.

Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood-brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.

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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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