Q-RASAR

The quantitative Read-Across Structure-Activity Relationship (q-RASAR) concept has been developed by the DTC Laboratory, Jadavpur University 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 combines 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.

A tutorial presentation on q-RASAR is available. Recently, the q-RASAR framework has been improved by its integration with the ARKA descriptors in QSAR. ^[7]

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.87662736C. 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. Bibcode:2023Chmsp.32938649S. 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.
7. Banerjee, Arkaprava; Roy, Kunal (2 April 2025). "The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints". Environmental Science: Processes Impacts. doi:10.1039/D5EM00068H. PMID   40227888.