Environmental Toxicity of Pesticides, and Its Modeling by QSAR Approaches

The aim of this work was to model multi-system dynamic adsorption using an artificial intelligence technique. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics on activated carbon, was used to build the artificial neural network (ANN). The studied parameters were molar mass, initial concentration, flow rate, bed height, particle diameter, BET surface area, average pore diameter, time, and concentration of dimensionless effluents. Results showed that the optimized ANN was obtained with a high correlation coefficient, R = 0.997, a root mean square error of RMSE = 0.029, and a mean absolute deviation of AAD (%) = 1.810 during the generalisation phase. Furthermore, a sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the dynamic adsorption. Also in this work, the traceability of the estimated results was conducted by developing a graphical user interface.

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“…The mathematical expression between the input vector X i and output vector Z j of this element (Fig. 1) can be defined as follows: 23,24 , 1…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial Neural Network Modelling of Multi-system Dynamic Adsorption of Organic Pollutants on Activated Carbon

et al. 2021

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“…A multilayer perceptron network (MLP) was chosen for modeling. This is an artificial neural network of anticipation, owing to its excellent nonlinear generalization capabilities (Rafei, Sorkhabi, & Mosavi, 2014;Wang, Zhang, Wang, Han, & Kong, 2014;Hamadache et al, 2016a).…”

Section: Elaboration Of the Ann Modelmentioning

confidence: 99%

“…In order to validate the model, two developed basic principles were commonly used, Internal validation and external validation (Hamadache et al, 2016b), they were judged by the following statistical parameters: RMSEext, RMSEint, Q 2 ext and Q 2 int (Zhou et al, 2015), and calculated using the following equations:…”

Section: Model Validationmentioning

confidence: 99%

Prediction of the antibacterial activity of garlic extract on E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks

Atsamnia

Hamadache

Hanini

et al. 2017

LWT - Food Science and Technology

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The aim of this study was to devise a model that predicts the inhibition zone diameter using artificial neural networks. The concentration, temperature and the exposure time of our extract were taken as input variables. The neural architecture model 3-13-3 and a learning algorithm Quasi-Newton (BFGS) revealed a positive correlation between the experimental results and those artificially predicted, which were measured according to a mean squared error (RMSE) and an R 2 coefficient of E.coli (RMSE=1.28; R 2 =0,96), S.aureus (RMSE=1.46; R 2 =0,97) and B.subtilis (RMSE=1.88; R 2 =0,96) respectively. Based on these results, an external and an internal model validation were attained. A neuronal mathematical equation was created to predict the inhibition diameters for experimental data not included in the basic learning. Consequently, a good correlation was observed between the values predicted by the equation and those obtained experimentally, as demonstrated by the R 2 and RMSE values. The results regarding the sensitivity analysis showed that the concentration was the most determinant parameter compared to Temperature and Time variables. Ultimately, the model developed in this study will be used reliably to predict the variation of garlic extract's inhibition diameter.

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“…Indeed, due to a growing need for toxicity assessment and the increasing variety and number of products, regulatory agencies and legislations, such as REACH (Registration, Evaluation and Authorisation of Chemical, the European legislation on chemicals) encourage the use of alternatives to animal testing. Computational approaches can accelerate advances in (eco)toxicological understanding as they support the experimental data with additional in silico studies and results (Mas et al 2010;Hamadache et al 2017;Villaverde et al 2017Villaverde et al , 2018. It was recently proposed to transpose QSAR models to nanomaterials to rapidly and cheaply screen and predict their toxicity (Pan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

et al. 2019

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Statistical analysis approaches have been developed to predict the biological response to nanoparticles, especially quantitative structure-activity relationship (QSAR) models. But one major limitation remains the quantitative lack of data to build accurate models. The aim of this study was to investigate if simple alternative mathematical models could rank nanoparticles in a very binary way (i.e. toxic or not) in case of small dataset. We synthesized and characterized 25 nanoparticles from 6 metal (hydr)oxide families with particle size and shape tuning. We assessed their toxicity on RAW 264.7 cells and investigated relationships with both physicochemical and dimensional descriptors. A simple partial least square (PLS) regression analysis allowed ranking nanoparticles with respect to their toxicity, without false-negative results. But this model was not predictive due to the specific response of each family to dimensional parameters variations. A classification tree extracted the same main bulk descriptor as PLS, but interestingly showed the relevance of dimensional descriptors for the second and third node. We thus recommend the development of family-specific models and propose the combination of these two simple methods as pre-screening tools, a compromise to bridge the gap between case-by-case studies (expensive and time-consuming) and sophisticated nano-QSAR models (not suitable for small datasets).

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Environmental Toxicity of Pesticides, and Its Modeling by QSAR Approaches

Cited by 12 publications

References 163 publications

Artificial Neural Network Modelling of Multi-system Dynamic Adsorption of Organic Pollutants on Activated Carbon

Artificial Neural Network Modelling of Multi-system Dynamic Adsorption of Organic Pollutants on Activated Carbon

Prediction of the antibacterial activity of garlic extract on E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

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