Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides

Speck‐Planche, Alejandro

doi:10.1007/978-1-0716-0150-1_26

Cited by 14 publications

(23 citation statements)

References 72 publications

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“…To check as well as to demonstrate the utilities of the developed QSAR-Co-X toolkit, four case studies pertaining to previously compiled datasets [ 9 , 11 , 26 , 27 ] are examined in this section. For all of them, both the activity cut-off values and the descriptors employed in the original publications were used here (exact details about those can be found in the original papers).…”

Section: Resultsmentioning

confidence: 99%

“…The purpose of third case study is to disclose how different Box-Jenkins’s operators may have an impact on the statistical quality of the derived models. The dataset of CS3 was retrieved from a recently published work in which the toxicity of 260 pesticides have been targeted by mt-QSAR modelling with artificial neural networks (ANN) [ 27 ]. The dataset comprised a total of 3610 datapoints related to four primary experimental condition elements ( c j ), namely: m e (measure of toxicity), b s (bioindicator species), a g (assay guideline) and e p (exposure period).…”

Section: Resultsmentioning

confidence: 99%

“…For detailed information about the cut-off values employed for the different measures of toxicity effects, please refer to the Supplementary Information (Additional file 1 : Table S5). Further details about m e , b s , a g and e p can be obtained from the original work [ 27 ]. The dataset contained 1992 toxic (+ 1) and 1618 nontoxic (− 1) compounds.…”

Section: Resultsmentioning

confidence: 99%

“…Analogously, the elements of may be also standardised, as recently proposed by Speck-Planche [ 27 ], leading to the following expression for the modified descriptors: …”

Section: Methodsmentioning

confidence: 99%

“…Therefore, in these two methods, a descriptor pre-treatment is carried to remove constant descriptors. Finally, Method4 allows the user to apply its own proper scheme for establishing the values [ 27 , 28 ], and the resulting modified descriptors are thus represented as follows: where the term denotes the user-specific , whose values should be provided as inputs. Within that context, the values do not need to be always calculated since these may also be obtained from experimental and/or theoretical data.…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Halder

Cordeiro

2021

J Cheminform

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Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Analogously, the elements of may be also standardised, as recently proposed by Speck-Planche [ 27 ], leading to the following expression for the modified descriptors: …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Halder

Cordeiro

2021

J Cheminform

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Chemometric Modeling of Daphnia Toxicity

Halder

Cordeiro

2021

Chemometrics and Cheminformatics in Aquatic Toxicology

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Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

Pandey

2023

Chemistry & Biodiversity

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Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi‐target‐QSAR model established by a backpropagation neural network trained with the Levenberg‐Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt‐QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol‐water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat) and (LD50bird) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW‐MLR method, and the selected PCs were used for constructing the SW‐MLR‐PCA‐ANN model. The developed well‐trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2=0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2=0.61). The AD of the developed Mt‐QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt‐QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven′t been tested yet.

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Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides

Cited by 14 publications

References 72 publications

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Chemometric Modeling of Daphnia Toxicity

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi‐Target QSAR Approach

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