Molecular Docking studies of D2 Dopamine receptor with Risperidone derivatives.

In this work, 3D model of D2 dopamine receptor was determined by comparative homology modeling program MODELLER. The computed model's energy was minimized and validated using PROCHECK and Errat tool to obtain a stable model structure and was submitted in Protein Model Database (PMDB-ID: PM0079251). Stable model was used for molecular docking against Risperidone and their 15 derivatives using AutoDock 4.2, which resulted in energy-based descriptors such as Binding Energy, Ligand Efficiency, Inhib Constant, Intermol energy, vdW + Hbond + desolv Energy, Electrostatic Energy, Total Internal Energy and Torsional Energy. After that, we have built quantitative structure activity relationship (QSAR) model, which was trained and tested on Risperidone and their 15 derivatives having activity value pKi in µM. For QSAR modeling, Multiple Linear Regression model was engendered using energy-based descriptors yielding correlation coefficient r2 of 0.513. To assess the predictive performance of QSAR models, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for D2 employing QSAR modeling seems to be a promising approach for prediction of pKi value of novel antagonists against D2 receptor.