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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
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]
Total Authors: 8
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: Journal of Molecular Modeling; v. 27, n. 10 OCT 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 17/10118-0 - Study and application of electrochemical technology for the analysis and degradation of endocrine interferents: materials, sensors, processes and scientific dissemination
Grantee:Marcos Roberto de Vasconcelos Lanza
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 18/06680-7 - Application of ontologies in computational medicinal chemistry studies and relations between chemical structure and biological activity
Grantee:Rafaela Molina de Angelo
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/24524-7 - STRUCTURAL ANALYSIS AND MOLECULAR MODELING STUDIES FOR NATURAL AND SYNTHETIC LIGANTS RELATED TO NEGLECTED DISEASES
Grantee:Kathia Maria Honorio
Support Opportunities: Regular Research Grants