AI Summary & Key Takeaways
AI InsightThe results of this study are envisaged to provide very important new insights into the molecular basis and structural requirements that would help in designing more potent and more specific therapeutic anti-psychotic agents.
📌 3 Key Takeaways
Abstract
The Norepinephrine transporter (NET) is a Na+/Cl- coupled neurotransmitter transporter responsible for reuptake of released norepinephrine (NE) into neural terminals in the brain, an important therapeutic agent used in the treatment of psychiatric disorders. A quantitative structural activity relationship (QSAR) investigation was carried out on 50 Molecules of NET Inhibitors to investigate their inhibitory potencies against norepinephrine transporter as novel agents for anti-psychotic disorders. The molecules were optimized by employing Density functional theory (DFT) with basis set of B3LYP/6-31G*. The genetic function Algorithm (GFA) approach was used to generate a highly predictive and statistically significant model with good correlation coefficient R2 Train = 0.952, Cross validated coefficient Q2cv = 0.870 and adjusted squared correlation coefficient R2adj = 0.898. The predictability and accuracy of the developed model was evaluated through external validation using test set molecules, Y-randomization and applicability domain techniques. The results of Molecular docking simulation by using two neurotransmitter transporters PDB ID 2A65 (resolution = 1.65 Å ) and PDB ID 4M48 (resolution = 2.955 Å) showed that two of the ligands (compound numbers 12 and 44) having higher binding affinity were observed to inhibit the targets by forming hydrogen bonds and hydrophobic interactions with amino acids of the two receptors respectively. The results of this study are envisaged to provide very important new insights into the molecular basis and structural requirements that would help in designing more potent and more specific therapeutic anti-psychotic agents.