Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, <i>Daphnia</i> and fish

Lunghini, Filippo; Marcou, Gilles; Azam, Philippe; Enrici, M.H.; Miert, E. Van; Varnek, Alexandre

doi:10.1080/1062936x.2020.1797872

Cited by 22 publications

(7 citation statements)

References 41 publications

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“…Specific functional groups, such as cyano, isothiocyanate, and halogens ,− enhance the toxicities of molecules . However, the extent of the increase appears to be dependent upon the molecular structure and position of the group in the molecule.…”

Section: Resultsmentioning

confidence: 99%

“…The collected knowledge suggests a consensus among researchers that acute toxicity is defined by the mode of toxicological action and the chemical characteristics . The higher toxicity values have often been associated with increased lipohilicity. ,, The most toxic compounds were hydrophobic and acted as hydrogen-bonding or electron acceptors (e.g., hydrophobic nitroaromatic compounds with halogen and amino substituents ,,,, ). Khan et al (2019) have advised that if a hydrophobic group is necessary during the design of a drug compound, a higher polarity substitution should be preferred.…”

Section: Resultsmentioning

confidence: 99%

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Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Shavalieva

Papadokonstantakis

Peters

2022

J. Chem. Inf. Model.

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Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Shavalieva

Papadokonstantakis

Peters

2022

J. Chem. Inf. Model.

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“…We do note that, by combining multiple toxicity targets for the same experiment, the intertest variability is no longer fully captured and noise is reduced. 37 Further, as aquatic toxicity values have been gathered over decades in various laboratories, causing variation among experimental values, Lunghini et al 8 reported that the ecotoxicological data set qualities heavily impact model performance�a concern also found in other work. 38,39 End Point.…”

Section: ■ Datamentioning

confidence: 92%

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Schlender

Viljanen

Rijn

et al. 2023

Environ. Sci. Technol.

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Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure–activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related taskseach predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.

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“…Skin sensitization data set is obtained from the recently published, publicly available GPMT (guinea pig maximization test) data by Alves et al 15 The data is composed of 1237 compounds assembled from multiple sources, the activity is binary, and the set contains 411 sensitizers and 826 nonsensitizers. The Daphnia toxicity data set was assembled from multiple publications (Moosus et al, 16 von der Ohe et al, 17 Furuhama et al, 18 Bouhedjar et al, 19 and Lunghini et al 20 ) and contains 2357 compounds. The toxicity labels were in the form of LC 50 as −Log(mol/L).…”

Section: ■ Data and Methodsmentioning

confidence: 99%

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Chakravarti

2023

Chem. Res. Toxicol.

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Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

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Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish

Cited by 22 publications

References 41 publications

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

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