The main objective of this study was to develop quantitative structure-activity relationships (QSAR) for the classification and prediction of anti-inflammatory activity. To this end, the ToSS-MoDE approximation was applied for the calculation of the spectral moments of the adjacency matrix between edges of the molecular graph with suppressed hydrogens, weighted on the main diagonal with moments of link dipoles, link distance, Van radius der Waals, polarizability and hydrophobicity to 509 active and inactive compounds. The calculated descriptors were used in the design of a training series and a prediction series. With the training series, a discriminant function was developed for the anti-inflammatory activity and another function to characterize the potential of these drugs using the Multivariate Linear Discriminant analysis, obtaining a good total classification of 96.07%. The model was validated by using the external prediction series, obtaining a good classification of 92.59%.
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Obtaining a computer-assisted QSAR model for the prediction of anti-inflammatory activity
Published: 24 October 2019 by MDPI in MOL2NET'19, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 5th ed. congress CHEMBIOINFO-05: Chem-Bioinformatics Congress München, Germany-Chapell Hill, USA, 2019
Keywords: quantitative structure-activity relationships anti-inflammatory activity