González Díaz, HumbertoCruz Monteagudo, MaykelTorres Gómez, Luis A.Guevara, YaimaAlmeida, Manuel S.Molina Ruiz, ReinaldoCastañedo, NiloSantana Penín, María Lourdes2021-08-192021-08-192005Proceedings of the 9th International Electronic Conference on Synthetic Organic Chemistry, 1–30 November 2005, MDPI: Basel, Switzerland, doi:10.3390/ecsoc-9-016573-906980-16-2http://hdl.handle.net/10347/26889The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThe 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 antibacterialseng© 2005 The author(s). Published by MDPI, Basel, Switzerland. Open AccessAntibacterials3D-QSARElectronegativity equalizationMarkov chainsDiscriminant analysisbook part10.3390/ecsoc-9-01657open access