QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors

Khan, Kabiruddin; Khan, Pathan Mohsin; Lavado, Giovanna J.; Valsecchi, Cecile; Pasqualini, Julia; Baderna, Diego; Marzo, Marco; Lombardo, Anna; Roy, Kunal; Benfenati, Emilio

doi:10.1016/j.chemosphere.2019.04.204

Cited by 84 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, it is quite probable that the present model is more robust and has a larger AD than the previous ones. We also noticed that the model by Khan et al [34] is specific for biocides, and this might explain the lower R 2 , and the other model [33] is for pharmaceuticals. If we consider older models, their performance was less satisfactory [35].…”

Section: Discussionmentioning

confidence: 64%

“…Recent studies on Daphnia magna acute toxicity had similar difficulties to those reported above and achieved R 2 values of about 0.60-0.68 for the validation set [33,34]. However, Khan and colleagues used 175 [33] and 133 [34] compounds; therefore, our model used a much larger set of compounds: 428. Thus, it is quite probable that the present model is more robust and has a larger AD than the previous ones.…”

Section: Discussionmentioning

confidence: 66%

See 1 more Smart Citation

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments

Toma

Cappelli

Manganaro³

et al. 2021

Molecules

Self Cite

View full text Add to dashboard Cite

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software.

show abstract

Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 66%

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments

Toma

Cappelli

Manganaro³

et al. 2021

Molecules

Self Cite

View full text Add to dashboard Cite

show abstract

“…Notwithstanding, removal of all sucrose-based DES from the modelling dataset only slightly improved the external predictivity of the model (MAE test = 0.032, R 2 Pred = 0.758, %AARD test = 2.897). Therefore, these structural outliers were retained in the modelling dataset along with all other structural outliers predicted well by the model [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

et al. 2021

View full text Add to dashboard Cite

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

show abstract

“…The two-dimensional quantitative structure-activity relationship (2D-QSAR) method has been applied to build prediction models of toxicity by determining the physical and chemical properties of chemical compounds from their chemical structures [29][30][31][32][33]. However, in conventional QSAR analysis, there are some problems concerning limited prediction performance [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

et al. 2020

View full text Add to dashboard Cite

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

show abstract

QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors

Cited by 84 publications

References 33 publications

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

Contact Info

Product

Resources

About