Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems-Reference-Cited by-同舟云学术

Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems

Published:2023-12 Issue: Volume:500 Page:153676
ISSN:0300-483X
Container-title:Toxicology
language:en
Short-container-title:Toxicology

Author:

Pandey Sapna Kumari,Roy Kunal

Funder

Indian Council of Medical Research

Publisher

Elsevier BV

Subject

Toxicology

Reference43 articles.

1. Comparison of in silico models for prediction of mutagenicity;Bakhtyari;J. Environ. Sci. Health C. Environ. Carcinog. Ecotoxicol. Rev.,2013

2. Toward good read-across practice (GRAP) guidance;Ball;ALTEX 33,2016

3. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability;Banerjee;Mol. Divers,2022

4. Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset;Banerjee;Chemom. Intell. Lab. Syst.,2023

5. Prediction-inspired intelligent training for the development of classification read-across structure-activity relationship (c-RASAR) models for organic skin sensitizers: assessment of classification error rate from novel similarity coefficients;Banerjee;Chem. Res Toxicol.,2023

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3. DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction;Computers in Biology and Medicine;2024-08

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