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.
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Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
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The authors are thankful to the Shahrood University of Technology Research Council for supporting this work.
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MB: conceptualization, methodology, validation, writing original, draft, writing-review, visualization. NG: supervision, project administration, editing. DS: software, validation, methodology. MAC: conceptualization, investigation, writing-editing. ZM: software, validation, resources.
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Beglari, M., Goudarzi, N., Shahsavani, D. et al. LM-ANN-based QSAR model for the prediction of pEC50 for a set of potent NNRTI using the mixture of ligand–receptor interaction information and drug-like indexes. Netw Model Anal Health Inform Bioinforma 9, 53 (2020). https://doi.org/10.1007/s13721-020-00259-2
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DOI: https://doi.org/10.1007/s13721-020-00259-2