Departamento de Química E Física Molecular, Instituto de Química de São Carlos, Universidade de São Paulo, C.P 780, São Carlos, SP, 13560-970, Brazil.
Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, C.P 03828-000, Brazil.
J Mol Model. 2021 Sep 24;27(10):297. doi: 10.1007/s00894-021-04906-x.
Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.
抑郁症影响着全球超过 3 亿人，并可能导致自杀。大约 30%的抑郁症患者因副作用或治疗效果的潜伏期而退出治疗。5-羟色胺受体，也被称为血清素，被认为是抑郁症治疗的关键。芳基哌嗪类化合物具有多种药理作用，被认为是血清素受体（如 5-HT 亚型）的配体。在这里，我们使用偏最小二乘法（PLS）和人工神经网络（ANN）进行了计算机模拟研究，以设计可与 5-HT 受体相互作用的新型芳基哌嗪化合物。首先，计算并从相关矩阵和遗传算法（GA）中选择分子和电子描述符。然后，使用选定的描述符构建 PLS 和 ANN 模型，这些模型表现出稳健和可预测性。最后，设计了新的芳基哌嗪化合物，并通过 PLS 和 ANN 模型预测了它们的生物活性值。值得强调的是，化合物 G5 和 G7（由 PLS 模型预测）和 G3 和 G15（由 ANN 模型预测），其预测的 pIC 值与这里研究的芳基哌嗪原始化合物集的三个最高值一样高。因此，可以断言，本文提出的两种模型（PLS 和 ANN）有望预测新型芳基哌嗪化合物的生物活性，并可能为治疗抑郁症的新药设计做出重大贡献。
