LIGAND-BASED DRUG DESIGN STUDIES ON SOME SELECTED CHEMICAL ACTIVATORS OF APOPTOSIS REGULATOR (BCL-2) FOR CANCEROUS CELL INHIBITION
Abstract
In spite of the substantial increase in the study of cancer treatment over the past decades, the disease is still a serious global health challenge and poses a considerable study. B-cell lymphoma 2 (BCL-2) apoptosis regulators were identified as potential targets in eliminating cancer cells, due to their high affinity for Bcl-2 Associated X-protein (BAX protein) which functions as a tool for bringing death to any cell. Hence, developing new BCL-2 activators with better therapeutic effect is strongly needed. In an attempt to overcome this challenge, this research is aimed at developing a mathematical model that can be used in designing novel potent chemical moieties with better BCL-2 apoptosis regulator via prediction of their activities using a newly developed Quantitative structure activity relationship (QSAR) model. The material in the study includes the chemical dataset collected from pubmed, while the methods used are the standard QSAR development method as stated in the Setubal records. The main program used in the study is the QSARIN. The QSAR model developed in the study was presented as pEC50 = - 0.0148(ALogP) - 0.0043(nO) + 0.0144(nHBint8) - 0.6533(ETA_dEpsilon_B) + 5.2861. The model was selected for its effective use in predicting novel chemical compounds or test set found within the applicability domain of the model because it has the best statistical parameters such as: coefficient of determination (R2 = 0.7836), cross validation coefficient (Q2loo = 0.7094), and adjusted R2 (R2 adj = 0.7475). The models was able to predict the activity of the compounds used in developing the models, the validation statistical parameter for the test set were not as significant as that for the training set, but the result suggest that the model can be improved upon by applying other nonlinear modeling techniques like that of machine learning algorithm and support vector machine or by replacing the molecules identified as outliers and influential molecules in the data set with other molecules of similar structure.
Keywords: Cancer, QSAR, BCL-2, Density functional theory, QSARIN.
https://doi.org/ 10.5281/zenodo.10580423
