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Markovian Chemicals “in silico” Design (MARCH-INSIDE), a Promising Approach for Computer-Aided Molecular Design III: 2.5D Indices for the Discovery of Antibacterials
* 1, 2 , 3 , 2 , 2 , 4, 5 , 2, 5 , 2 , 1
1  Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Spain
2  Chemical Bioactives Center, Central University of Las Villas, 54830, Cuba
3  Applied Chemistry Research Center, Central University of Las Villas, 54830, Cuba
4  Department of Chemistry, University of Granma, Cuba
5  Universität Rostock, FB Chemie, Albert-Einstein-Str. 3a, D-18059 Rostock, Germany

Abstract: The present work continues our series on the use of MARCH-INSIDE molecular descriptors [parts I and II: J. Mol. Mod. (2002) 8: 237-245 and (2003) 9: 395-407]. These descriptors encode information regarding to the distribution of electrons in the molecule based on a simple stochastic approach to the idea of electronegativity equalization (Sanderson’s principle). Here, 3D-MARCH-INSIDE molecular descriptors for 667 organic compounds are used as input for a Linear Discriminant Analysis. This 2.5D-QSAR model discriminates between antibacterial compounds and non-antibacterial ones with a 92.9 % of accuracy in training sets. On the other hand, the model classifies correctly 94.0 % of the compounds in test set. Additionally, the present QSAR performs similar-to-better than other methods reported elsewhere. Finally, the discovery of a novel compound illustrates the use of the method. This compound, 2-bromo-3-(furan-2-yl)-3-oxo-propionamide have MIC50 of 6.25 and 12.50 µg/mL against Ps. Aeruginosa ATCC 27853 and E. Coli ATCC 27853 respectively while ampicillim, amoxicillim, clindamycin, and metronidazole have, for instance, MIC50 values higher 250 µg/mL against E. Coli. Consequently, the present method may becomes a useful tool for the in silico discovery of antibacterials.
Keywords: antibacterials; 3D-QSAR, electronegativity equalization; Markov chains; discriminant analysis