Predictive modelling of the LD₅₀ activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT

Peer review under responsibility of Taibah University.

Abstract

A study of structure–activity relationship (QSAR) was performed on a set of 30 coumarin-based molecules. This study was performed using multiple linear regressions (MLRs) and an artificial neural network (ANN). The predicted values of the antioxidant activities of coumarins were in good agreement with the experimental results. Several statistical criteria, such as the mean square error (MSE) and the correlation coefficient (R), were studied to evaluate the developed models. The best results were obtained with a network architecture [8-4-1] (R = 0.908, MSE = 0.032), activation functions (tansig–purelin) and the Levenberg–Marquardt learning algorithm. The model proposed in this study consists of large electronic descriptors that are used to describe these molecules. The results suggested that the proposed combination of calculated parameters may be useful for predicting the antioxidant activities of coumarin derivatives.

Keywords:

• Predicted
• QSAR
• MLR
• ANN
• Learning algorithm
• Levenberg–Marquardt

Acknowledgements

We are grateful to the Association Marocaine des Chimistes Théoriciens (AMCT) for its pertinent help concerning the programs.

Notes

Peer review under responsibility of Taibah University.