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New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists

  • Aline A. Oliveira
  • Célio F. Lipinski
  • Estevão B. Pereira
  • Kathia M. Honorio
  • Patrícia R. Oliveira
  • Karen C. Weber
  • Roseli A. F. Romero
  • Alexsandro G. de Sousa
  • Albérico B. F. da SilvaEmail author
Original Paper
Part of the following topical collections:
  1. QUITEL 2016

Abstract

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2 training = 0.761, q2 = 0.656, r2 test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2 test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2 test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment.

Graphical abstract

Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Keywords

Sigma-1 receptor 1-arylpyrazole QSAR PLS MLP-ANN Consensus modeling 

Notes

Acknowledgements

The authors would like to thank CNPq, CAPES, and FAPESP (Brazilian agencies) for the financial support.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.

Supplementary material

894_2017_3444_MOESM1_ESM.docx (28 kb)
ESM 1 (DOCX 27 kb)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Aline A. Oliveira
    • 1
    • 2
  • Célio F. Lipinski
    • 1
  • Estevão B. Pereira
    • 1
  • Kathia M. Honorio
    • 2
  • Patrícia R. Oliveira
    • 2
  • Karen C. Weber
    • 3
  • Roseli A. F. Romero
    • 4
  • Alexsandro G. de Sousa
    • 5
  • Albérico B. F. da Silva
    • 1
    Email author
  1. 1.Instituto de Química de São CarlosUniversidade de São PauloSão CarlosBrazil
  2. 2.Escola de Artes, Ciências e HumanidadesUniversidade de São PauloSão PauloBrazil
  3. 3.Centro de Ciências Exatas e da NaturezaUniversidade Federal da Paraíba, Cidade UniversitáriaJoão PessoaBrazil
  4. 4.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  5. 5.Universidade Estadual do Sudoeste da BahiaItapetingaBrazil

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