Paradox of ‘ideal correlations’: improved model for air half-life of persistent organic pollutants

Toropova, Alla P.; Toropov, Andrey A.; Lombardo, Anna; Lavado, Giovanna J.; Benfenati, Emilio

doi:10.1080/09593330.2021.1882588

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…CW(X) demonstrates the correlation weight for the SMILES and graph attributes (X = SSS k , BOND, HALO, NOSP, HARD, or e1 k , p2 k , p3 k , VS2 k and nn k ). 52–54…”

Section: Methodsmentioning

confidence: 99%

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Lotfi,

Ahmadi,

Azimi

et al. 2023

New J. Chem.

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“…CW(X) demonstrates the correlation weight for the SMILES and graph attributes (X = SSS k , BOND, HALO, NOSP, HARD, or e1 k , p2 k , p3 k , VS2 k and nn k ). 52–54…”

Section: Methodsmentioning

confidence: 99%

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Lotfi,

Ahmadi,

Azimi

et al. 2023

New J. Chem.

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show abstract

“…In QSPR modeling, the commonly used molecular representations include the qualitative and quantitative molecular descriptors, − molecular graph, − and sequential representation. − Qualitative and quantitative descriptor (QQD)-based QSPR models provide excellent interpretability for two reasons: on one hand, each descriptor has its physical, biological, and chemical meanings; on the other hand, the weight for each descriptor indicates how much it contributes to the target property. However, QQD-based QSPR models have high requirements for feature engineering (i.e., descriptor selection and processing) to reach a decent prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach

Zhou

Eden

Shen

2023

Ind. Eng. Chem. Res.

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Quantitative structure−property relationship (QSPR) modeling is an implementation for estimating molecular properties based on structural information, which is widely applied in exploring new solvents, pharmaceuticals, and materials with desired properties. In QSPR modeling, "simplified molecular input line-entry system" (SMILES) is a popular molecular representation with specific vocabulary and syntax. Herein, SMILES is considered a chemical language, and each SMILES notation is treated as a sentence. A deep pyramid convolutional neural network architecture is constructed for extracting the information from SMILES "sentences", and the feed-forward neural network is used for the property correlation. A case study of predicting the logarithm values of the octanol−water partition coefficient is conducted to prove the effectiveness of the proposed philosophy. Compared with a precedent reference model, the outperformance of the developed QSPR models provides fascinating insights for applying natural language processing technologies for molecular information mining and exploration of chemical property space.

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“…Outliers related to the chemical space have been identified, also providing tools for building (Q)SAR models that cover the AD [ 6 ]. In some cases, the SMILES format is used to examine rare features of the molecule [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models

Alberto

Colombo

Raitano

et al. 2023

IJMS

Self Cite

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A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users’ confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.

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Paradox of ‘ideal correlations’: improved model for air half-life of persistent organic pollutants

Cited by 7 publications

References 30 publications

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach

The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models

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