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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.)

Drug design of new sigma-1 antagonists against neuropathic pain: A QSAR study using partial least squares and artificial neural networks

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Chiari, Laise P. A. [1] ; da Silva, Aldineia P. [1] ; de Oliveira, Aline A. [1] ; Lipinski, Celio F. [1] ; Honorio, Kathia M. [2] ; da Silva, Alberico B. F. [1]
Total Authors: 6
[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 - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Journal of Molecular Structure; v. 1223, JAN 5 2021.
Web of Science Citations: 0

Neuropathic pain is a cureless syndrome and affects considerably the life quality of people stricken by it. Drugs currently used for its treatment do not significantly reduce the symptoms and/or have many side effects. In the search for other therapeutic approaches, the sigma-1 receptor has been pointed out as a promising drug target for the treatment of neuropathic pain. As part of our effort to help the development of new therapeutic agents against neuropathic pain, we have applied techniques of quantitative structure-activity relationships (QSAR) to a series of compounds having the pyrimidine as scaffold using Partial Least Squares (PLS) and Artificial Neural Networks (ANN) to design new sigma-1R antagonists. Next, we have calculated a plethora of descriptors, which were selected from correlation matrix and genetic algorithm (AG). The selected descriptors were used to construct PLS and ANN models and, from them, various results were used to design new antagonists. At last, the designed compounds were subjected to the our QSAR models to predict their biological activity values. The new compounds exhibited significant biological affinity values, and among them we can highlight the compounds L2, L4, L14, L17, and L18 with excellent predicted pK i values confirmed by both PLS and ANN models. Therefore, the predictive ability of the PLS and ANN models here presented and their robustness allowed to extract important information that can be used in the design of new compounds as well as to predict their biological activity values. (C) 2020 Published by Elsevier B.V. (AU)

Grantee:Kathia Maria Honorio
Support type: Regular Research 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 type: Scholarships in Brazil - Master
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
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 type: Research Projects - Thematic Grants