Abstract
A combination of ligand–receptor interactions and drug-like indexes have been used to develop a quantitative structure–activity relationship model to predict anti-HIV activity (pEC50) of 73 azine derivatives as non-nucleoside reverse transcriptase inhibitors. Ligand–receptor interactions were derived from the best position (best pose) of studied compounds, as ligands, in the active site of receptors using Autodock 4.2 software and named as molecular docking descriptors. The drug-like indexes were calculated using DRAGON 5.5 software. Two groups of descriptors were mixed, and the stepwise regression method was used for the selection of the most relevant descriptors. Four selected descriptors were subsequently used to construct the quantitative structure–activity relationship model using the Levenberg–Marquardt artificial neural network method. Dataset was randomly divided into the train (53 compounds), validation (10 compounds) and test set (10 compounds). The best model was selected according to the lowest mean square error value of the validation set. The accuracy and predictability of the model were evaluated using test and validation sets and the leave-one-out technique. According to the predicted results, the coefficient of determination of the test set (R2 = 0.86) and all data ( $${Q}_{LOO}^{2}$$ = 0.73) were acceptable. The mean square error value for the test set was equal to 0.11. The obtained results emphasized the good prediction ability and generalizability of the developed model in the prediction of pEC50 values for new compounds.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Network Modeling Analysis in Health Informatics and Bioinformatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.