Q-RASAR

{{Short description|Statistical modeling technology}}

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{{COI|date=August 2023}}

{{notability|date=August 2023}}

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The quantitative Read-Across Structure-Activity Relationship (q-RASAR) concept has been developed by the [https://sites.google.com/site/kunalroyindia/home/the-dtc-laboratory?authuser=0 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.{{cite journal |last1=Banerjee |first1=Arkaprava |last2=Roy |first2=Kunal |title=First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability |journal=Molecular Diversity |date=October 2022 |volume=26 |issue=5 |pages=2847–2862 |doi=10.1007/s11030-022-10478-6 |pmid=35767129 }}

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.

[https://sites.google.com/site/kunalroyindia/home/rasar?authuser=0 The q-RASAR approach] has been used by different research groups for different endpoints.{{cite journal |last1=Chen |first1=Shuo |last2=Sun |first2=Guohui |last3=Fan |first3=Tengjiao |last4=Li |first4=Feifan |last5=Xu |first5=Yuancong |last6=Zhang |first6=Na |last7=Zhao |first7=Lijiao |last8=Zhong |first8=Rugang |title=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 |journal=Science of the Total Environment |date=June 2023 |volume=876 |pages=162736 |doi=10.1016/j.scitotenv.2023.162736 |pmid=36907405 |bibcode=2023ScTEn.87662736C }}{{cite journal |last1=Sobańska |first1=Anna W. |title=In silico assessment of risks associated with pesticides exposure during pregnancy |journal=Chemosphere |date=July 2023 |volume=329 |pages=138649 |doi=10.1016/j.chemosphere.2023.138649 |pmid=37043889 |bibcode=2023Chmsp.32938649S }}{{cite journal |last1=Yang |first1=Lu |last2=Tian |first2=Ruya |last3=Li |first3=Zhoujing |last4=Ma |first4=Xiaomin |last5=Wang |first5=Hongyan |last6=Sun |first6=Wei |title=Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach |journal=Chemosphere |date=July 2023 |volume=328 |pages=138433 |doi=10.1016/j.chemosphere.2023.138433 |pmid=36963572 |bibcode=2023Chmsp.32838433Y }}{{cite journal |last1=Banerjee |first1=Arkaprava |last2=Roy |first2=Kunal |title=On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points |journal=Chemical Research in Toxicology |date=20 March 2023 |volume=36 |issue=3 |pages=446–464 |doi=10.1021/acs.chemrestox.2c00374 |pmid=36811528 }} 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.{{cite journal |last1=Banerjee |first1=Arkaprava |last2=Roy |first2=Kunal |title=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 |journal=Chemical Research in Toxicology |date=18 September 2023 |volume=36 |issue=9 |pages=1518–1531 |doi=10.1021/acs.chemrestox.3c00155 |pmid=37584642 }} 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 [https://www.way2drug.com/dr/bcadd2023/content/presentations/video/BCADD-2023-Roy_Kunal.mp4 tutorial presentation] on q-RASAR is available. Recently, the q-RASAR framework has been improved by its integration with the ARKA descriptors in QSAR. {{cite journal |last1=Banerjee |first1=Arkaprava |last2=Roy |first2=Kunal |title=The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints |journal=Environmental Science: Processes Impacts |date= 2 April 2025 |doi=10.1039/D5EM00068H |pmid=40227888 }}