MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives

Mohammad Goodarzi, Matheus P. Freitas

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

The activities of a series of HIV reverse transcriptase inhibitor TIBO derivatives were recently modeled by using genetic function approximation (GFA) and artificial neural networks (ANN) on topological, structural, electronic, spatial and physicochemical descriptors. The prediction results were found to be superior to those previously established. In the present work, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS), which accounts for non-linearities, was applied on the same set of compounds previously reported. Additionally, partial least squares (PLS) and multilinear partial least squares (N-PLS) regressions were used for comparison with the MIA-QSAR/PCA-ANFIS model. The ANFIS procedure was capable of accurately correlating the inputs (PCA scores) with the bioactivities. The predictive performance of the MIA-QSAR/PCA-ANFIS model was significantly better than the MIA-QSAR/PLS and N-PLS models, as well as than the reported models based on CoMFA, CoMSIA, OCWLGI and classical descriptors, suggesting that the present methodology may be useful to solve other QSAR problems, specially those involving non-linearities.

Original languageEnglish (US)
Pages (from-to)1352-1358
Number of pages7
JournalEuropean Journal of Medicinal Chemistry
Volume45
Issue number4
DOIs
StatePublished - Apr 1 2010

Fingerprint

HIV Reverse Transcriptase
Quantitative Structure-Activity Relationship
Fuzzy inference
Principal Component Analysis
Least-Squares Analysis
Principal component analysis
Image analysis
Multivariate Analysis
Derivatives
Reverse Transcriptase Inhibitors
Bioactivity
Passive Cutaneous Anaphylaxis
Neural networks

Keywords

  • Anti-HIV reverse transcriptase activities
  • MIA-QSAR
  • PCA-ANFIS
  • TIBO derivatives

ASJC Scopus subject areas

  • Organic Chemistry
  • Drug Discovery
  • Pharmacology

Cite this

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title = "MIA-QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives",
abstract = "The activities of a series of HIV reverse transcriptase inhibitor TIBO derivatives were recently modeled by using genetic function approximation (GFA) and artificial neural networks (ANN) on topological, structural, electronic, spatial and physicochemical descriptors. The prediction results were found to be superior to those previously established. In the present work, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS), which accounts for non-linearities, was applied on the same set of compounds previously reported. Additionally, partial least squares (PLS) and multilinear partial least squares (N-PLS) regressions were used for comparison with the MIA-QSAR/PCA-ANFIS model. The ANFIS procedure was capable of accurately correlating the inputs (PCA scores) with the bioactivities. The predictive performance of the MIA-QSAR/PCA-ANFIS model was significantly better than the MIA-QSAR/PLS and N-PLS models, as well as than the reported models based on CoMFA, CoMSIA, OCWLGI and classical descriptors, suggesting that the present methodology may be useful to solve other QSAR problems, specially those involving non-linearities.",
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AB - The activities of a series of HIV reverse transcriptase inhibitor TIBO derivatives were recently modeled by using genetic function approximation (GFA) and artificial neural networks (ANN) on topological, structural, electronic, spatial and physicochemical descriptors. The prediction results were found to be superior to those previously established. In the present work, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS), which accounts for non-linearities, was applied on the same set of compounds previously reported. Additionally, partial least squares (PLS) and multilinear partial least squares (N-PLS) regressions were used for comparison with the MIA-QSAR/PCA-ANFIS model. The ANFIS procedure was capable of accurately correlating the inputs (PCA scores) with the bioactivities. The predictive performance of the MIA-QSAR/PCA-ANFIS model was significantly better than the MIA-QSAR/PLS and N-PLS models, as well as than the reported models based on CoMFA, CoMSIA, OCWLGI and classical descriptors, suggesting that the present methodology may be useful to solve other QSAR problems, specially those involving non-linearities.

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