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Unify QSAR approach to antibacterial activity of organic drugs against different species
Published: 30 November 2006 by MDPI in The 10th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
Abstract: There are many different kinds of pathogen bacteria species with very different susceptibility profile to different antibacterial drugs. One limitation of QSAR models are the biological activity of drugs against only one bacteria species. In previous paper we develop one unified Markov model to describe the biological activity of different drugs tested in the literature against some of the antimicrobial species. Consequently predicting the probability with which a drug is active against different bacteria species with a single unify model is a goal of the major importance. This work develops one unified Markov model to describe the biological activity of more than 70 drugs tested in the references against to 96 bacteria species. Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested bacteria species processed the data. The model correctly classifies 199 out of 237 active compounds (83.9%) and 168 out of 200 non-active compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carring out by means of external predicting series, classifying the model 202 out 243, 83.13% of compounds. In order to show how the model function in practice a virtual screening was carring out recognizing the model as active 84.5%, 480 out of 568 antibacterial compounds not used in training or predicting series. The present is an attempt to calculate withing a unify framework probabilities of antibacterial action of drugs against many different species.