Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data

Daré, Joyce K.; Silva, Cristina Ferreira; Freitas, Matheus P.

doi:10.1016/j.ecoenv.2017.06.072

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Initially, a traditional MIA-QSAR modelling was performed. The detailed procedure for building MIA-QSAR models has been described elsewhere [5][6][7][8][9][10]; thus, herein, only the main steps are explained. First, the compounds were drawn in Gauss-View program [16], maintaining the congeneric centre (the disulfide bond) at the same exactly position in all molecules.…”

Section: Traditional Mia-qsar Modellingmentioning

confidence: 99%

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Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Daré

Silva

Ramalho

et al. 2020

Molecular Simulation

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Multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) has proved to be a high-performance 2D tool for drug design purposes. Nonetheless, MIA-QSAR strategy does not efficiently incorporate conformational information. Therefore, understanding the implications of including this type of data into the MIA-QSAR model, in terms of predictability and interpretability, seems a crucial task. Conformational information was included considering the optimised geometries and the docked structures of a series of disulfide compounds potentially useful as SARS-CoV protease inhibitors. The traditional analysis (based on flat-shape molecules) proved itself as the most effective technique, which means that, despite the undeniable importance of conformation for biomolecular behaviour, this type of information did not bring relevant contributions for MIA-QSAR modelling. Consequently, promising drug candidates were proposed on the basis of MIA-plot analyses, which account for PLS regression coefficients and variable importance in projection scores of the MIA-QSAR model.

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Section: Traditional Mia-qsar Modellingmentioning

confidence: 99%

“…Commonly, the different approaches are classified from 1D to 7D [3]; each of them has its own advantages and drawbacks [4]. MIA-QSAR (Multivariate Image Analysis applied to QSAR) is a relevant 2D approach that has been employed in many occasions in the last decade, yielding satisfactory outcomes, comparable to 3D results [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Daré

Silva

Ramalho

et al. 2020

Molecular Simulation

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“…To better manage soil phosphorus, gaining precise knowledge of available soil P content and planning an efficient fertilization process are strongly needed [16,17]. There are also other dynamics to be considered, namely, transport and source factors, which should be considered to conserve water quality.…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

et al. 2020

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Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

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“…The advantages of 3D‐QSAR are that models can be interpreted in terms of chemical regions in a 3D space most affecting the biological property. However, the model development is complex due to the exhaustive conformational search and structure alignment required in such analysis (Daré et al., ). Among the various methods of conformational searching, it is remarkable for the systematic search, random search, Monte Carlo, molecular dynamics, simulated annealing, distance geometry algorithm, and genetic and evolutionary algorithm (Verma et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…3D QSAR methodologies, in turn, are as popular as 2D QSAR. The foundations of this approach lie on the idea that biological selectivity results from each target forming highly specific interactions, such as hydrogen bonds with a ligand and that the binding preferences emerge primarily from non‐covalent field effects exerted in its spatial vicinity (Daré, Silva, & Freitas, ). In other words, 3D‐QSAR is a broad term that covers all those QSAR techniques based on computed descriptors derived from spatial representation of the molecular structures (Verma, Khedkar, & Coutinho, ).…”

Section: Introductionmentioning

confidence: 99%

3D perspective into MIA‐QSAR: A case for anti‐HCV agents

2018

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Quantitative structure–activity relationship (QSAR) is a molecular modeling technique widely used in the discovery of novel drugs. Currently, there are many approaches for performing such analysis, which are commonly classified from 1D to 6D. 2D and 3D techniques are among the most exploited ones. Multivariate image analysis applied to QSAR (MIA‐QSAR) is an example of 2D methodology that has presented a satisfactory performance in the generation of effective prediction models for biological/physicochemical properties. However, once this is a 2D method, conformational information is not explicitly considered, despite the well‐known role of such type of information in explaining the biochemical behavior. Thus, the importance of conformation is undeniable, but the requirement of this information for QSAR analysis still needs to be studied. Therefore, this work aimed to provide a method for encoding 3D information into MIA‐QSAR descriptors and analyze the consequences of this inclusion on this methodology. The strategy consisted in fully optimizing the molecular geometries of anti‐HCV compounds and three‐dimensionally align them before performing the MIA‐QSAR procedure. As a result, it was possible to verify that this type of information does not improve the MIA‐QSAR modeling performance; instead, the traditional procedure consisting of maximally congruent substructures generated a more reliable prediction model.

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Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data

Cited by 7 publications

References 20 publications

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

3D perspective into MIA‐QSAR: A case for anti‐HCV agents

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