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QSAR analysis and molecular docking simulation of norepinephrine transporter (NET) inhibitors as anti-psychotic therapeutic agents

机译:去甲肾上腺素转运蛋白(NET)抑制剂作为抗精神病药物的QSAR分析和分子对接模拟

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摘要

The norepinephrine transporter (NET) is a Na+/Cl coupled neurotransmitter transporter responsible for reuptake of released norepinephrine (NE) into nerve terminals in the brain, a key therapeutic used in the treatment of psychiatric disorders. A quantitative structural activity relationship (QSAR) study was performed on 50 compounds of NET inhibitors to investigate their inhibitory potencies against norepinephrine transporter as novel drugs for anti-psychotic disorders. The compounds 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 R2Train = 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 compound, Y-randomization and applicability domain techniques. The results of Molecular docking analysis by using two neurotransmitter transporters PDB ID (resolution = 1.65 Å) and PDB ID (resolution = 2.955 Å) showed that two of the ligands (compound 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 these studies would provide important new insight into the molecular basis and structural requirements to design more potent and more specific therapeutic anti-psychotic drugs/agents.
机译:去甲肾上腺素转运蛋白(NET)是Na + / Cl -偶联的神经递质转运蛋白,负责释放释放的去甲肾上腺素(NE)进入大脑神经末梢,这是一种关键的治疗方法在精神疾病的治疗中。对NET抑制剂的50种化合物进行了定量结构活性关系(QSAR)研究,以研究其对去甲肾上腺素转运蛋白作为抗精神病药物的抑制作用。通过使用B3LYP / 6-31G *基组的密度泛函理论(DFT)对化合物进行优化。使用遗传函数算法(GFA)方法生成具有良好相关系数R 2 Train = 0.952的具有高度预测性和统计学意义的模型交叉验证系数Q 2 cv = 0.870调整后的平方相关系数R 2 adj = 0.898。通过使用测试集化合物,Y随机化和适用性域技术进行外部验证,评估了开发模型的可预测性和准确性。使用两种神经递质转运蛋白PDB ID(分辨率= 1.65Å)和PDB ID(分辨率= 2.955Å)进行的分子对接分析的结果表明,观察到两个具有更高结合亲和力的配体(化合物12和44)抑制了靶标通过与两个受体的氨基酸分别形成氢键和疏水相互作用。这些研究结果将为分子基础和结构要求提供重要的新见识,以设计更有效和更具体的治疗性抗精神病药物。

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