Aug-MIA-QSAR based strategy in bioactivity prediction of a series of flavonoid derivatives as HIV-1 inhibitors.

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.

MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors.

Alzheimer's disease is a common form of dementia, which considered to be a major health concern. Multivariate Image Analysis - Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of unstudied compounds based on 2D image analysis. This study focuses on constructing an efficient QSAR model using a dataset of 52 flavonoid derivatives (substituted with amino-alkyl, alkoxy, alkyl-amines, and piperidine groups) as active compounds against acetylcholinesterase inhibitors (AChE). The model was constructed by PLS (Partial Least Square) using NIPALS (Non-Linear iterative Partial Least Square) algorithm. The comparable values obtained from calibration of training set using five latent variables (R2 = 0.955) and external validation of test set (Q2 = 0.948) confirmed the precision in the prediction of bioactivities for the set of flavonoid derivatives used in designing the model.

In vitro inhibition potential of mono-n-octyl phthalate on Mycobacterium tuberculosis H37Ra: Possibility of binding to mycobacterial PknB-An in silico approach.

Fatty acids of specific chain lengths have been shown to inhibit the growth of Mycobacterium tuberculosis. In the present study, specific synthetic aromatic derivatives of n-octyl esters were investigated for their property to inhibit the growth of M. tuberculosis H37Ra. Agar well diffusion assay indicated that the crude synthetic derivatives obtained by the esterification of phthalic acid (PA) and n-octanol exhibited antimycobacterial activity. Further, the activity was authenticated with the Miroplate Alamar Blue Assay (MABA). Subsequently, the active component was purified by bioactivity guided chromatographic fractionation. The structure of the synthetic derivative was deduced by UV-Vis, FT-IR, LC-MS, GC-mass spectrometry, and NMR spectroscopy. Molecular docking and molecular dynamic simulation (MDS) were performed with Autodock 4.0 and GROMACS 5.1.2 softwares, respectively. It was found that mono-n-octyl phthalate (MOP) exhibited antimycobacterial activity with a MIC of 20 µg/mL, and not by any other related compounds, including di-n-octyl phthalate, PA, phthalic anhydride, and n-octanol. Binding of MOP with protein kinase B can participate in the binding cavity region, which was previously reported. Subsequently, we authenticate the stability with MDS. This is first report on the inhibition of M. tuberculosis growth by MOP.