Ligand-based modeling of semicarbazones and thiosemicarbazones derivatives as Cathepsin B, H, and L inhibitors: A multi-target approach

This work describes the development, validation, and comparison of three different multi-target artificial neural network models to explore quantitative structure-activity relationships (mt-QSAR) forin-silico design of compounds containing carbonyl and thiocarbonyl moiety, as inhibitors against the related cysteine proteases, cathepsin B, H, and L. The proposed three models are based on back propagation neural network (trained with LM and SCG algorithm) and radial basis neural network (RBNN). The validation of the model was carried out by using an external test set which showed (r (2) (ANN_LM) = 0.752, r (2) (ANN_SCG) = 0.661, r (2) (RBNN) = 0.45) for these mt-QSAR models. As per results, RBNN is good at the fitting stage but BPNN (LM) outperformed in predicting stage than the rest of the two. The applicability domain of the proposed models is evaluated by the leverage approach. Based on SAR outcome, some new inhibitors are also designed and their activities predicted using the best proposed model. In addition, molecular docking studies of the best selected inhibitors have been performed to determine the optimum binding conformation. Binding energy values are in accordance with inhibitory activity values. For the first time, molecular modeling studies are used to explore the bond rearrangement after ligandcathepsin interaction to display the interactions with amino acid residues of the receptor. The reported inhibitors also complied with Lipinski's rule of five. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This work presents the development, validation, and comparison of three different multi-target artificial neural network models (mt-QSAR) for exploring the quantitative structure-activity relationships of compounds containing carbonyl and thiocarbonyl groups as inhibitors against cysteine proteases. The models were validated using an external test set and showed promising predictive performance. Based on the results, new inhibitors were designed and their activities were predicted. Additionally, molecular docking studies and molecular modeling were conducted to determine the optimal binding conformations and interactions. The findings demonstrate the accuracy and reliability of the proposed models in theoretical predictions.