Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (VIT) University, Vellore, Tamil Nadu, India.
Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology (VIT) University, Vellore, Tamil Nadu, India.
J Theor Biol. 2019 May 21;469:18-24. doi: 10.1016/j.jtbi.2019.02.019. Epub 2019 Feb 28.
Multivariate image analysis-quantitative structure-activity relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of compounds based on two-dimensional image analysis. Aug-MIA-QSAR is a modified version of multivariate image analysis, where the atoms in 2D chemical structures were augmented (labelled by assigning specific colours). This study focuses on efficiently constructing such prediction models using a dataset of flavonoid derivatives possessing human immunodeficiency virus - 1 inhibition. The models were constructed by partial least square regression using non-linear iterative partial least square (NIPALS) algorithm and linearized by identifying an optimum number of seven latent variables. A leave-one-out cross validation (LOOCV) helped to verify the actual and predicted data. The two multivariate methods were compared and analysed to identify the most suitable method.
多元图像分析 - 定量构效关系（MIA-QSAR）是一种简单且相当容易使用的 QSAR 方法，可基于二维图像分析来预测化合物的生物活性。Aug-MIA-QSAR 是多元图像分析的一种改进版本，其中二维化学结构中的原子被扩充（通过分配特定颜色进行标记）。本研究专注于使用具有人类免疫缺陷病毒-1 抑制作用的黄酮类衍生物数据集来高效构建此类预测模型。通过使用非线性迭代偏最小二乘（NIPALS）算法的偏最小二乘回归构建模型，并通过确定最佳的七个潜在变量数来线性化模型。通过留一法交叉验证（LOOCV）有助于验证实际数据和预测数据。比较并分析了两种多元方法，以确定最合适的方法。
