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Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

PloS one, 2023-05, Vol.18 (5), p.e0282924-e0282924

2023

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Quantitative structure − skin permeability relationships

Toxicology (Amsterdam), 2017-07, Vol.387, p.27-42

2017

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In Silico Identification of Chemicals Capable of Binding to the Ecdysone Receptor

Environmental toxicology and chemistry, 2020-07, Vol.39 (7), p.1438-1450

2020

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Free radicals and antioxidants in normal physiological functions and human disease

The international journal of biochemistry & cell biology, 2007, Vol.39 (1), p.44-84

2007

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Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

Nanotechnology, 2015-01, Vol.26 (1), p.015701-9

2015

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(Q)SARs to predict environmental toxicities: current status and future needs

Environmental science--processes & impacts, 2017-03, Vol.19 (3), p.213-22

Cronin, Mark T. D

2017

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Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction

Regulatory toxicology and pharmacology, 2019-08, Vol.106, p.90-104

2019

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Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis

Chemosphere (Oxford), 2002-12, Vol.49 (10), p.1201-1221

2002

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Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs

Environmental health perspectives, 2003-08, Vol.111 (10), p.1361-1375

2003

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Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context

Molecular informatics, 2019-03, Vol.38 (3), p.e1800084-n/a

2019

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Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert

Journal of chemical information and modeling, 2018-03, Vol.58 (3), p.673-682

2018

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The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

Toxicology (Amsterdam), 2017-12, Vol.392, p.140-154

2017

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Workshop report on the evaluation of the updated and expanded carcinogen database to support derivation of threshold of toxicological concern values for DNA-reactive carcinogens

ALTEX, 2023-01, Vol.40 (2), p.341

2023

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Optimizing drug discovery by Investigative Toxicology: Current and future trends

ALTEX, alternatives to animal experimentation, 2019-01, Vol.36 (2), p.289

2019

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In silico resources to assist in the development and evaluation of physiologically-based kinetic models

Computational toxicology, 2019-08, Vol.11, p.33-49

2019

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Pharmaceuticals in the environment: Good practice in predicting acute ecotoxicological effects

Toxicology letters, 2009-03, Vol.185 (2), p.85-101

2009

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Use of QSARs in International Decision-Making Frameworks to Predict Health Effects of Chemical Substances

Environmental health perspectives, 2003-08, Vol.111 (10), p.1391-1401

2003

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Chemoinformatic Consideration of Novel Psychoactive Substances: Compilation and Preliminary Analysis of a Categorised Dataset

Molecular informatics, 2019-08, Vol.38 (8-9), p.n/a

2019

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Towards a qAOP framework for predictive toxicology - Linking data to decisions

Computational toxicology, 2022-02, Vol.21, p.100195, Article 100195

2022

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Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships

Alternatives to laboratory animals, 2005, Vol.33 (2), p.155-173

2005

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Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

Computational toxicology, 2022-02, Vol.21, p.100204, Article 100204

2022

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Consensus QSAR Models: Do the Benefits Outweigh the Complexity?

Journal of chemical information and modeling, 2007-07, Vol.47 (4), p.1460-1468

2007

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In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

한국독성학회지, 2017, 33(3), , pp.173-182

2017

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The role of a molecular informatics platform to support next generation risk assessment

Computational toxicology, 2023-05, Vol.26, p.100272, Article 100272

2023

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Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)

Quantitative structure − skin permeability relationships

In Silico Identification of Chemicals Capable of Binding to the Ecdysone Receptor

Free radicals and antioxidants in normal physiological functions and human disease

Novel approach for efficient predictions properties of large pool of nanomaterials based on limited set of species: nano-read-across

(Q)SARs to predict environmental toxicities: current status and future needs

Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis

Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context

Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

Workshop report on the evaluation of the updated and expanded carcinogen database to support derivation of threshold of toxicological concern values for DNA-reactive carcinogens

Optimizing drug discovery by Investigative Toxicology: Current and future trends

In silico resources to assist in the development and evaluation of physiologically-based kinetic models

Pharmaceuticals in the environment: Good practice in predicting acute ecotoxicological effects

Use of QSARs in International Decision-Making Frameworks to Predict Health Effects of Chemical Substances

Chemoinformatic Consideration of Novel Psychoactive Substances: Compilation and Preliminary Analysis of a Categorised Dataset

Towards a qAOP framework for predictive toxicology - Linking data to decisions

Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships

Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

Consensus QSAR Models: Do the Benefits Outweigh the Complexity?

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

The role of a molecular informatics platform to support next generation risk assessment

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