Stephen Eyije Abechi

In silico modelling of quantitative structure–activity relationship of multi-target anticancer compounds on k-562 cell line

The pGI50 cytotoxicity values of 112 compounds on K-562 cancer cell line were modeled to illustrate the quantitative structure–activity relationship (QSAR) of the compounds. The dataset were divided into training and test set through Kennard-stone algorithm, while the pool of molecular descriptors calculated with paDEL descriptor metric program was subjected to the genetic functional algorithm (GFA) for selection of descriptor to be modeled. The best QSAR model developed was then subjected to a rigorous statistical test. The statistical significance of the model was verified by calculating the values of Q2LOO (0.845), Q2F1 (0.9397), Q2F2 (0.6862) and R2pred (0.6862) needed to evaluate the strength and robustness of the model. The result of the internal and external validation of the model indicates that the model is good and could be used to predict the GI50 of anticancer compounds on K-562 leukemia cell line. The model developed was used in designing new anticancer drugs with higher activity or more potent and less toxic in nature when compared to the lead compound. These compounds significant activities were found to correlate to with some of the molecular descriptors such as the number of hydrogen bond acceptors present in the surface of the molecule.

Mechanism and rate constant of proline-catalysed asymmetric aldol reaction of acetone and p-nitrobenzaldehyde in solution medium: Density-functional theory computation

Graphical abstract

Evaluation of the antioxidant properties of curcumin derivatives by genetic function algorithm

Graphical abstract

Insilico Modelling of Quantitative Structure-Activity Relationship of Pgi50 Anticancer Compounds on k-562 Cell Line

Abstract The pGI50 cytotoxicity values of 112 compounds on K-562 cancer cell line were modelled in order to illustrate the quantitative structure–activity relationship of the compounds. The data set were divided into training and test set through Kennard-stone algorithm, while the pool of molecular descriptors calculated with paDEL descriptor metric program was subjected to genetic functional algorithm for selection of descriptor to be modeled. The statistical significance of the model was verified by calculating the values of Q2LOO (0.845), Q2F1 (0.9397), Q2F2 (0.6862) and R2pred (0.6862) needed to evaluate the strength and robustness of the model. The result of the internal and external validation of the model indicates that the model is good and could be used to predict the GI50 of anticancer compounds on K-562 leukemia cell line.