BACKGROUND Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs. OBJECTIVE There are two major classes, such as TCAs and phenoxyphenylpropylamines, which have been proven to be broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. However, there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling attempted yet. METHOD In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Furthermore, several five new congeners have been designed which are subjected to the prediction of biological activities in terms of serotonin receptor affinity utilizing validated QSAR models developed by us. RESULTS An important pharmacophoric feature point C, followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine, has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are comparable with the existed drugs. CONCLUSION The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.

中文翻译：

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利用 QSAR 和药效团建模对 TCA 和苯氧基苯丙胺进行抗抑郁药物设计。

背景 抑郁症是一种由重要的神经递质如血清素 (5-HT) 和去甲肾上腺素 (NE) 失衡引起的精神疾病。这是一种严重的神经系统疾病，可以通过抗抑郁药物治疗。目的 有两大类，如 TCA 和苯氧基苯丙胺，已被证明是广谱抗抑郁化合物。进行了几次尝试来设计、合成和发现对血清素和去甲肾上腺素转运蛋白具有最低毒性和最高选择性的强效抗抑郁化合物。然而，几乎没有任何基于定量构效关系（QSAR）和药效团模型的药物设计尝试。方法 在本研究中，许多 TCA（二苯并氮杂）和苯氧基苯丙胺衍生物被考虑用于药效团特征生成，然后是基于药效团远距离相关描述符的 QSAR 建模。此外，还设计了几种新的同系物，利用我们开发的经过验证的 QSAR 模型，对血清素受体亲和力方面的生物活性进行预测。结果 预测了一个重要的药效团特征点 C，然后是 TCA 和苯氧基苯丙胺的形貌生成。开发的基于药效团特征的 QSAR 可以解释 64.2% 的 5-HT 受体拮抗变化。最佳训练模型已通过测试集化合物的预测得到统计验证。该训练模型已用于预测一些新设计的与现有药物相当的同类化合物。结论 新设计的化合物可作为抗抑郁药的进一步合成和生物筛选。