Misplaced Pages

Q-RASAR

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.
Statistical modeling technology
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages)
A major contributor to this article appears to have a close connection with its subject. It may require cleanup to comply with Misplaced Pages's content policies, particularly neutral point of view. Please discuss further on the talk page. (August 2023) (Learn how and when to remove this message)
The topic of this article may not meet Misplaced Pages's general notability guideline. Please help to demonstrate the notability of the topic by citing reliable secondary sources that are independent of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be merged, redirected, or deleted.
Find sources: "Q-RASAR" – news · newspapers · books · scholar · JSTOR (August 2023) (Learn how and when to remove this message)
(Learn how and when to remove this message)

The quantitative Read-Across Structure-Activity Relationship (q-RASAR) concept has been developed by merging Read-Across and QSAR. It is a statistical modeling approach that uses the similarity and error-based measures as descriptors in addition to the usual structural and physicochemical descriptors, and it has been shown to enhance the external predictivity of QSAR/QSPR models.

The novel quantitative read-across structure-activity relationship (q-RASAR) approach clubs the advantages of both QSAR and read-across, thus resulting in enhanced predictivity for the same level of chemical information used. This approach utilizes similarity-based considerations yet can generate simple, interpretable, and transferable models. This approach may be used for any type of structural and physicochemical descriptors and with any modeling algorithms.

The q-RASAR approach has been used by different research groups for different endpoints. Among different RASAR descriptors, RA function, Average Similarity and gm (Banerjee-Roy concordance coefficient) have shown high importance in modeling in some studies. In 2023, Banerjee-Roy similarity coefficients sm1 and sm2 have also been proposed to identify potential activity cliffs in a data set. The q-RASAR approach has the potential in data gap filling in predictive toxicology, materials science, medicinal chemistry, food sciences, nano-sciences, agricultural sciences, etc.

References

  1. Banerjee, Arkaprava; Roy, Kunal (October 2022). "First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability". Molecular Diversity. 26 (5): 2847–2862. doi:10.1007/s11030-022-10478-6. PMID 35767129.
  2. Chen, Shuo; Sun, Guohui; Fan, Tengjiao; Li, Feifan; Xu, Yuancong; Zhang, Na; Zhao, Lijiao; Zhong, Rugang (June 2023). "Ecotoxicological QSAR study of fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs): Assessment and priority ranking of the acute toxicity to Pimephales promelas by QSAR and consensus modeling methods". Science of the Total Environment. 876: 162736. Bibcode:2023ScTEn.876p2736C. doi:10.1016/j.scitotenv.2023.162736. PMID 36907405.
  3. Sobańska, Anna W. (July 2023). "In silico assessment of risks associated with pesticides exposure during pregnancy". Chemosphere. 329: 138649. doi:10.1016/j.chemosphere.2023.138649. PMID 37043889.
  4. Yang, Lu; Tian, Ruya; Li, Zhoujing; Ma, Xiaomin; Wang, Hongyan; Sun, Wei (July 2023). "Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach". Chemosphere. 328: 138433. Bibcode:2023Chmsp.32838433Y. doi:10.1016/j.chemosphere.2023.138433. PMID 36963572.
  5. ^ Banerjee, Arkaprava; Roy, Kunal (20 March 2023). "On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points". Chemical Research in Toxicology. 36 (3): 446–464. doi:10.1021/acs.chemrestox.2c00374. PMID 36811528.
  6. Banerjee, Arkaprava; Roy, Kunal (18 September 2023). "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". Chemical Research in Toxicology. 36 (9): 1518–1531. doi:10.1021/acs.chemrestox.3c00155. PMID 37584642.
Category: