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Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. | LitMetric

AI Article Synopsis

  • The study addresses the challenge of predicting the effectiveness of antiparasitic drugs across multiple parasite species using a unified model.
  • By applying Markov Chains theory, the researchers developed a multi-target QSAR model that analyzed 500 existing drugs against 16 parasite species, successfully classifying the majority of active and non-active compounds.
  • The model demonstrated high accuracy in both training and validation phases, outperforming traditional methods and laying the groundwork for future drug development against diverse parasites.

Article Abstract

There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.

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Source
http://dx.doi.org/10.1016/j.bmc.2010.01.068DOI Listing

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