Predicting biological activity and design of 5-HT₆ antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease

Autor(es)

de Sousa, Daniel S. ; da Silva, Aldineia P. ; Chiari, Laise P. A. ; de Angelo, Rafaela M. ; de Sousa, Alexsandro G. ; Honorio, Kathia M. ; da Silva, Alberico B. F.

• Tipo de documento: Artigo Científico
• Fonte: Journal of Molecular Modeling; v. 30, n. 10, p. 13-pg., 2024-10-01.

Resumo

Context Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. Methods Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD. (AU)

• 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