Computer-Aided Methods for Molecular Classification

Alina Bărbulescu, Lucica Barbeș and Cristian Ștefan Dumitriu
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Alina Bărbulescu: Department of Civil Engineering, Transilvania University of Brașov, 5 Turnului Street, 900152 Brașov, Romania
Lucica Barbeș: Department of Chemisty and Chemical Engineering, Faculty of Applied Sciences and Engineering, Ovidius University of Constanța, 124 Mamaia Bd., 900527 Constanta, Romania
Cristian Ștefan Dumitriu: SC Utilnavorep SA, 55 Aurel Vlaicu Av., 900055 Constanta, Romania

Mathematics, 2022, vol. 10, issue 9, 1-19

Abstract: The study aims to analyze the degree of similarity of some molecules belonging to two subgroups of Aminoalkylindoles. After extracting the molecules’ characteristics using Cheminformatics methods, and the computation of the Tanimoto coefficients, dendrograms and heatmaps were built to reveal the degree of similarity of the analyzed drugs. Some atom-pair similarities between the molecules in the same group were detected. The clusters determined by the k-means method divided the Benzoylindoles into two subgroups but kept all the Phenylacetylindoles together in the same set. The activity spectrum of the elements in each group was also analyzed, and similarities have been emphasized. The clustering has been validated using the Kruskal–Wallis test on the series of computed probabilities of the main effects.

Keywords: similarity; clusters; molecular fingerprint; Cheminformatics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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