Study of Quantitative Structure-Activity Relationship (QSAR) of Diarylaniline Analogues as in Vitro Anti-HIV-1 Agents in Pharmaceutical Interest

Bouakarai, Youness; Khalil, Fouad; Bouachrin, Mohammed

doi:10.48309/chemm.2023.53807

Document Type : Original Article

Authors

¹ LAC, Laboratory of Applied Chemistry, Faculty ofScience and Technology, University Sidi Mohammed Ben Abdellah, Fez, Morocco

² Equipe Matériaux, Environnement & Modélisation,ESTM, University Moulay Ismail, Meknes, Morocco

https://doi.org/10.48309/chemm.2023.53807

Abstract

A study of quantitative structure-activity relationship (QSAR) is applied to a set of 24 molecules derived from diarylaniline to predict the anti-HIV-1 biological activity of the test compounds and find a correlation between the different physic-chemical parameters (descriptors) of these compounds and its biological activity, using principal components analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN). We accordingly proposed a quantitative model (non-linear and linear QSAR models), and we interpreted the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed with ACD/ChemSketch and ChemBioOffice14.0 programs. A correlation was found between the experimental activity and those obtained by MLR and MNLR such as (R_train = 0.886 ; R²_train = 0.786) and (R_train = 0.925 ; R²_train = 0.857) for the training set compounds, and (R_MLR-test = 0.6) and (R_MNLR-test = 0.7) for a randomly chosen test set of compounds, this result could be improved with ANN such as (R = 0.916 and R² = 0.84) with an architecture ANN (6-1-1). To evaluate the performance of the neural network and the validity of our choice of descriptors selected by MLR and trained by MNLR and ANN, we used cross-validation method (CV) including (R = 0.903 and R² = 0.815) with the procedure leave-one-out (LOO). The results showed that the MLR and MNLR have served to predict activities, but when compared with the results given by a 6-1-1 ANN model. We realized that the predictions fulfilled by the latter model were more effective than the other models. The statistical results indicated that this model is statistically significant and showing a very good stability towards the data variation in leave-one-out (LOO) cross validation.

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Main Subjects

Physical and Theoretical chemistry

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Chemical Methodologies

Study of Quantitative Structure-Activity Relationship (QSAR) of Diarylaniline Analogues as in Vitro Anti-HIV-1 Agents in Pharmaceutical Interest

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Volume 7, Issue 9
September 2023
Pages 719-735

Study of Quantitative Structure-Activity Relationship (QSAR) of Diarylaniline Analogues as in Vitro Anti-HIV-1 Agents in Pharmaceutical Interest

Full Text

Full Text

References

References

Volume 7, Issue 9September 2023Pages 719-735

Volume 7, Issue 9
September 2023
Pages 719-735