A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents

Autor(es)

Santos, Genisson R. ^[1] ; Chiari, Laise P. A. ^[1] ; da Silva, Aldineia P. ^[1] ; Lipinski, Celio F. ^[1] ; Oliveira, Aline A. ^[1] ; Honorio, Kathia M. ^[2] ; de Sousa, Alexsandro Gama ^[3] ; da Silva, Alberico B. F. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Quim Sao Carlos, Dept Quim & Fis Mol, CP 780, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, BR-03828000 Sao Paulo, SP - Brazil

^[3] Univ Estadual Sudoeste Bahia UESB, Campus Itapetinga, Praca Primavera 40, BR-45700000 Itapetinga, BA - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Journal of Molecular Modeling; v. 27, n. 10 OCT 2021.
• Citações Web of Science: 0

Resumo

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-HT2a. 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-HT2a 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(50) 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. (AU)

• Processo FAPESP: 17/10118-0 - Estudo e aplicação da tecnologia eletroquímica para a análise e a degradação de interferentes endócrinos: materiais, sensores, processos e divulgação científica
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• Processo FAPESP: 18/06680-7 - Aplicação de ontologias em estudos de química medicinal computacional e relações entre estrutura química e atividade biológica
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• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
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• Processo FAPESP: 16/24524-7 - Análise estrutural e estudos de modelagem molecular para ligantes de origem natural e sintética relacionados a doenças negligenciadas
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