Abstract

There are many parasite species with very different antiparasite drugs susceptibility. Computational methods in biology and chemistry prediction of the biological activity based on Quantitative Structure-Activity Relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model (ms-QSAR). 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 ms-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using entropy type indices. The data was processed by Artificial Neural Network (ANN) classifying drugs as active or non-active against the different tested parasite species. The best ANN found was MLP 23:23-18-1:1. Overall model classification accuracy was 85.65% (211/244 cases) in training. Validation of the model was carried out by means of external predicting series. In this serie, the model classified correctly 81.85% (275/357 cases).

Highlights

  • There is a high interest on the search of rational approaches for antiparasite drugs discovery

  • QSAR studies are generally based on databases considering only structurally parent compounds acting against one single microbial species [1,2,3,4,5,6,7]

  • The present study develops a single linear equation based on these previous ideas to predict the antiparasite activity of drugs against different species

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Summary

Introduction

There is a high interest on the search of rational approaches for antiparasite drugs discovery. To predict the antiparasite activity for a given series of compounds one have to use/seek as many QSAR models as microbial species drugs susceptibility is desirable to predict. The method was named the MARCH-INSIDE approach, MARkovian CHemicals IN SIlico Design It allowed us introducing matrix invariants such as stochastic entropies and spectral moments for the study of molecular properties. The entropy like molecular descriptors has demonstrated flexibility in many bioorganic and medicinal chemistry problems such as: estimation of anticoccidial activity, modeling the interaction between drugs and HIV-packaging-region RNA, and predicting proteins and virus activity [11,12,13]. In recent studies the MARCH-INSIDE method has been extended to encompass molecular environment interesting information in addition to molecular structure This new interpretation allows calculating molecular thermodynamic entropy for many physicochemical and biological processes. The present study develops a single linear equation based on these previous ideas to predict the antiparasite activity of drugs against different species

Markov model for drug-target step-by-step interaction
ANN models
Results and discussion
Findings
Conclusion
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