Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems
Author:
Funder
Indian Council of Medical Research
Publisher
Elsevier BV
Subject
Toxicology
Reference43 articles.
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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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1. Tools, Applications, and Case Studies (q-RA and q-RASAR);SpringerBriefs in Molecular Science;2024
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