Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials
The present work continues our series on the use of MARCH-INSIDE molecular descriptors ( parts I and II: J Mol Mod 8: 237 - 245, [ 2002] and 9: 395 - 407, [ 2003]). These descriptors encode information pertaining 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 92.9% accuracy in training sets. On the other hand, the model classifies 94.0% of the compounds in test set correctly. 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 has MIC50 of 6.25 and 12.50 mu g/mL against Pseudomonas aeruginosa ATCC 27853 and Escherichia coli ATCC 27853, respectively while ampicillim, amoxicillim, clindamycin, and metronidazole have, for instance, MIC50 values higher than 250 mu g/mL against E. coli. Consequently, the present method may becomes a useful tool for the in silico discovery of antibacterials.
antibacterials electronegativity equalization discriminant analysis 2.5d-qsar markov chains