Application of the LAD-LASSO as a dimensional reduction technique in the ANN-based QSAR study: Discovery of potent inhibitors using molecular docking simulation

In this study, the combination of the least absolute deviation-least absolute shrinkage and selection operator (LAD-LASSO) was introduced as a new variable selection method for the artificial neural network (ANN)-based quantitative structure-activity relationship (QSAR) studies. The biological activity of various chemical compounds was predicted using an ANN-based QSAR model combined with the efficient LAD-LASSO variable selection method. In this study, 3224 computed DRAGON descriptors were reduced to a smaller number using preprocessing methods. The descriptors with the most significant relevance to biological activities were chosen using the LAD-LASSO variable selection method. The selected descriptors were defined as ANN inputs and optimized the designed models. The biological activity of the test set compounds was predicted using the optimum ANN models. The coefficients of determination (R 2 ) for the test data in the different datasets were equal to 0.87, 0.84, and 0.87. Also, the MSE value of the test set is equal to 0.13, 0.07, and 0.11, respectively. The high R 2 and low MSE values demonstrate the good prediction ability of the constructed QSAR models. The applicability domain (AD) and Y-randomization test also proved the efficiency of the developed models. Finally, The performance of the QSAR model was evaluated by the identification of novel compounds with high potency. As a result, the weak structure of the dataset was identified and modified using the effect of selected descriptors on the biological activity, resulting in the establishment of new compounds with significant potency. The response value of the new suggested compounds was predicted using the optimum ANN models. Receptor-ligand interactions were extracted for all proposed compounds. The presence of different hydrophilic and hydrophobic interactions in the active site of the respective receptor indicates the high potential of suggested chemical compounds. • Discovery of potent inhibitors using molecular docking simulation and LAD-LASSO-ANN. • LAD-LASSO penalized method improves the interpretability of the developed QSAR model. • The robust LAD-LASSO-ANN method has high predictability and generalizability.