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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.[1]

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.[2][3][4][5] Among different RASAR descriptors, RA function, Average Similarity and gm (Banerjee-Roy concordance coefficient) have shown high importance in modeling in some studies.[5] In 2023, Banerjee-Roy similarity coefficients sm1 and sm2 have also been proposed to identify potential activity cliffs in a data set.[6] 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 1, 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. S2CID 250115457 – via Springer Link.
  2. ^ Chen, Shuo; Sun, Guohui; Fan, Tengjiao; Li, Feifan; Xu, Yuancong; Zhang, Na; Zhao, Lijiao; Zhong, Rugang (June 10, 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. S2CID 257489922 – via ScienceDirect.
  3. ^ Sobańska, Anna W. (July 1, 2023). "In silico assessment of risks associated with pesticides exposure during pregnancy". Chemosphere. 329: 138649. Bibcode:2023Chmsp.329m8649S. doi:10.1016/j.chemosphere.2023.138649. PMID 37043889. S2CID 258077023 – via ScienceDirect.
  4. ^ Yang, Lu; Tian, Ruya; Li, Zhoujing; Ma, Xiaomin; Wang, Hongyan; Sun, Wei (July 1, 2023). "Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach". Chemosphere. 328: 138433. Bibcode:2023Chmsp.328m8433Y. doi:10.1016/j.chemosphere.2023.138433. PMID 36963572. S2CID 257704060 – via ScienceDirect.
  5. ^ a b Banerjee, Arkaprava; Roy, Kunal (March 20, 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. S2CID 257100289 – via CrossRef.
  6. ^ Banerjee, Arkaprava; Roy, Kunal (May 22, 2023). "Prediction-inspired intelligent training for the development of c-RASAR models for organic skin sensitizers: Assessment of classification error rate from novel similarity coefficients". doi:10.26434/chemrxiv-2023-20v0k – via Cambridge Engage Preprints. ((cite journal)): Cite journal requires |journal= (help)
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