Multi-target QSAR modelling for identification of novel inhibitors of class I HDACs.

In this study, the multi-target QSAR (mt-QSAR) models were constructed which can predict the inhibitory activity of compounds against various class I HDACs isoforms under different experimental conditions. Models based on mt-QSAR classification (a linear model and six non-linear models) were constructed using 1215 compounds obtained from the ChEMBL database by the Box - Jenkins moving average method using 13 deviation descriptors. The high predictive performance was found in the non-linear models based on Support Vector Classification, Random Forest and Gradient Boosting with accuracies exceeding 90% for the sub-training, test and validation sets. Additionally, virtual screening was performed using the ZINC library to identify a potential hit compound. The SwissADME was used for in silico predictions to assess drug-likeness of the identified virtual hit. Finally, docking and molecular dynamics simulations were performed to study the interactions of target proteins with the hit compound. It was found that coordination of the ligand with the catalytic zinc ion is essential for inhibitory activity. The results offer important insights for the search for new inhibitors of class I HDACs as potential therapeutic agents.