无监督机器学习,QSAR建模和网络工具开发,以简化抗疟类黄酮的先导物识别过程。

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
J H Zothantluanga, D Chetia, S Rajkhowa, A K Umar
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引用次数: 2

摘要

用传统的实验室方法鉴定先导化合物既昂贵又费时。如今,硅技术已经成为一种很有前途的铅识别方法。在这项研究中,我们的目标是建立稳健和可预测的2D-QSAR模型,通过预测对恶性疟原虫的IC50来鉴定类黄酮铅。我们应用机器学习算法(主成分分析和K-means聚类)和Pearson相关分析选择9个分子描述符(MDs)进行模型构建。采用不同的MDs组合进行100次多元线性回归(MLR),选出3个最佳的QSAR模型并进行验证。开发的模型符合经济合作与发展组织规定的QSAR模型的五项原则。该研究结果是一种可靠且可持续的IC50 (Mean±SD)预测方法,通过减少从类黄酮中识别潜在抗疟先导化合物所需的资金和时间,将对抗疟药物开发过程产生积极影响。我们还开发了一个网络工具(JazQSAR, https://etflin.com/news/4),为已开发的QSAR模型提供一个易于访问的平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids.

Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC50 against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC50 (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.

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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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