Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

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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PMID: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. 2006 Jun 25

The widespread production of esters combined with their ability to migrate in different compartments, makes their environmental toxicity important. In this background, the multivariate image analysis-quantitative structure-toxicity relationship (MIA-QSTR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) was applied to assess the toxicity of esters to Daphnia magna. In MIA-QSTR, pixels of chemical structures (2D images) stand for descriptors, and structural changes account for the variance in toxicities. The ANFIS procedure was capable of correlating the inputs (PCA scores) with the toxicities accurately. The PCA-ANFIS also was statistically validated for its predictive power using cross-validation, applicability domain and Y-scrambling evaluation procedures. The satisfactory results (R p (2) = 0.926, Q LOO (2) = 0.887, R L25%O (2) = 0.843, RMSELOO = 0.320 and RMSEL25%O = 0.379) suggests that the QSTR model could be proposed as an alternative method for aquatic toxicity assessment of esters allowing possible application in the European Union regulation REACH.
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PMID:Aquatic toxicity assessment of esters towards the Daphnia magna through PCA-ANFIS. 2388 70