Drug design of new 5-HT6R antagonists aided by artificial neural networks

Autor(es)

da Silva, Aldineia P. ^[1] ; Chiari, Laise P. A. ^[1] ; Guimaraes, Amanda R. ^[1] ; Honorio, Kathia M. ^[2] ; 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 Ciencia & Humanidades, BR-03828000 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF MOLECULAR GRAPHICS & MODELLING; v. 104, MAY 2021.
• Citações Web of Science: 0

Resumo

Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age relatedneurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT6R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT6R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT6 antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pKi above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed. (C) 2021 Published by Elsevier Inc. (AU)

• Processo FAPESP: 16/24524-7 - Análise estrutural e estudos de modelagem molecular para ligantes de origem natural e sintética relacionados a doenças negligenciadas
• Beneficiário: Kathia Maria Honorio
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• 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
• Beneficiário: Marcos Roberto de Vasconcelos Lanza
• Modalidade de apoio: Auxílio à Pesquisa - Temático