Zothantluanga J H, Chetia D, Rajkhowa S, Umar A K
Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India.
Centre for Biotechnology and Bioinformatics, Faculty of Biological Sciences, Dibrugarh University, Dibrugarh, India.
SAR QSAR Environ Res. 2023 Feb;34(2):117-146. doi: 10.1080/1062936X.2023.2169347. Epub 2023 Feb 6.
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 IC against . 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 IC (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.
采用传统实验室方法鉴定先导化合物既昂贵又耗时。如今，计算机模拟技术已成为一种颇具前景的先导化合物鉴定方法。在本研究中，我们旨在通过预测针对……的IC来开发强大且具有预测性的二维定量构效关系（2D-QSAR）模型，以鉴定黄酮类先导化合物。我们应用机器学习算法（主成分分析后接K均值聚类）和皮尔逊相关分析来选择9个分子描述符（MDs）用于模型构建。在使用不同MDs组合对多元线性回归（MLR）执行100次后，我们选择并验证了三个最佳QSAR模型。所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。该研究的结果是一种可靠且可持续的计算机模拟IC（平均值±标准差）预测方法，通过减少从黄酮类化合物中鉴定潜在抗疟先导化合物所需的资金和时间，将对抗疟药物研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），为所开发的QSAR模型提供一个易于访问的平台。
