The antiviral QSAR models today have an important limitation. Only they predict the biological activity of drugs against only one viral species. This is determined due the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this we use the Markov Chain theory to calculate new multi-target entropy to fit a QSAR model that predict by the first time a ms-QSAR model for 900 drugs tested in the literature against 40 viral species and other 207 drugs no tested in the literature using entropy QSAR. We used Linear Discriminant Analysis (LDA) to classify drugs into two classes as active or non-active against the different tested viral species whose data we processed. The model correctly classifies 31 188 out of 31 213 non-active compounds (99.92%) and 432 out of 434 active compounds (99.54%). Overall training predictability was 98.56%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 15 588 out of 15 606 non-active compounds and 213 out of 217 active compounds. Overall validation predictability was 98.54%. The present work report the first attempts to calculate within a unify framework probabilities of antiviral drugs against different virus species based on entropy analysis.
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Multi-viral targets entropy QSAR for antiviral drugs
Published: 04 December 2015 by MDPI in MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed. congress USEDAT-01: USA-Europe Data Analysis Training Congress, Cambridge, UK-Bilbao, Spain-Miami, USA, 2015
Keywords: Antiviral drugs; QSAR;; Entropy; Mutli-tasking Learning; Markov Chain model; Linear Discriminant Analysis.