How to correctly develop q-RASAR models for predictive cheminformatics
Author:
Affiliation:
1. Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
Funder
Defence Research and Development Organisation
Life Sciences Research Board
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/17460441.2024.2376651
Reference40 articles.
1. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment
2. Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use
3. Principles of QSAR Modeling
4. First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability
5. 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
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1. The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset;Scientific Reports;2024-09-06
2. Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning;Critical Reviews in Toxicology;2024-09-03
3. QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments;Environmental Science: Nano;2024
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