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ARKA descriptors in QSAR

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One of the most commonly used in silico approaches for assessing new molecules' activity/property/toxicity is the Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR), which generates predictive models for efficiently predicting query compounds .[1] QSAR/QSPR/QSTR uses numerical chemical information in the form of molecular descriptors and correlates these to the response activity/property/toxicity using statistical techniques.[2] While QSAR is essentially a similarity-based approach, the occurrence of activity/property cliffs may greatly reduce the predictive accuracy of the developed models.[3] The novel Arithmetic Residuals in K-groups Analysis (ARKA) approach is a supervised dimensionality reduction technique developed by the DTC Laboratory, Jadavpur University that can easily identify activity cliffs in a data set.[4] Activity cliffs are similar in their structures but differ considerably in their activity. The basic idea of the ARKA descriptors is to group the conventional QSAR descriptors based on a predefined criterion and then assign weightage to each descriptor in each group. ARKA descriptors have also been used to develop classification-based[5] and regression-based[6] QSAR models with acceptable quality statistics.

The ARKA descriptors have been used for the identification of activity cliffs in QSAR studies and/or model development by multiple researchers.[7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

A tutorial presentation on the ARKA descriptors is available. Recently a multi-class ARKA framework has been proposed for improved q-RASAR model generation.[23]

References

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  1. ^ Muratov, Eugene N.; et al. (June 8, 2020). "QSAR without borders". Chemical Society Reviews. 49 (11): 3525–3564. doi:10.1039/D0CS00098A. PMC 8008490. PMID 32356548.
  2. ^ Cherkasov, Artem; et al. (June 26, 2014). "QSAR Modeling: Where Have You Been? Where Are You Going To?". Journal of Medicinal Chemistry. 57 (12): 4977–5010. doi:10.1021/jm4004285. PMC 4074254. PMID 24351051.
  3. ^ Dablander, Markus; Hanser, Thierry; Lambiotte, Renaud; et al. (April 17, 2023). "Exploring QSAR models for activity-cliff prediction". Journal of Cheminformatics. 15 (1): 47. doi:10.1186/s13321-023-00708-w. PMC 10107580. PMID 37069675.
  4. ^ Qin, Li-Tang; Zhang, Jun-Yao; Nong, Qiong-Yuan; Xu, Xia-Chang-Li; Zeng, Hong-Hu; Liang, Yan-Peng; Mo, Ling-Yun (November 2024). "Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures". Environmental Pollution. 360: 124565. doi:10.1016/j.envpol.2024.124565. PMID 39033842.
  5. ^ Banerjee, Arkaprava; Roy, Kunal (2024). "ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data". Environmental Science: Processes & Impacts. 26 (6): 991–1007. doi:10.1039/D4EM00173G. PMID 38743054.
  6. ^ Sobańska, Anna W.; Banerjee, Arkaprava; Roy, Kunal (18 November 2024). "Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions—In Silico Studies of Drug-Likeness and Human Placental Transport". International Journal of Molecular Sciences. 25 (22): 12373. doi:10.3390/ijms252212373. PMC 11595199. PMID 39596438.
  7. ^ Qin, L.T.; Zhang, Z.Y.; Nang, Q.Y.; Xu, X.C.; Zeng, H.H.; Liang, Y.P.; Mo, L.Y. (1 November 2024). "Classification and Regression Machine Learning Models for Predicting the Combined Toxicity and Interactions of Antibiotics and Fungicides Mixtures". Environmental Pollution. 360: 124565. doi:10.1016/j.envpol.2024.124565. PMID 39033842.
  8. ^ Shan, Rongli; Zhang, Runqi; Gao, Ying; Wang, Wenxin; Zhu, Wenguang; Xin, Leilei; Liu, Tianxiong; Wang, Yinglong; Cui, Peizhe (2024). "Evaluating ionic liquid toxicity with machine learning and structural similarity methods". Green Chemical Engineering. 6 (2): 249–262. doi:10.1016/j.gce.2024.08.008.
  9. ^ Sankar Borah, Gori; Nagamani, Selvaraman (2024). "Development of a robust Machine learning model for Ames test outcome prediction". Chemical Physics Letters. 856. Bibcode:2024CPL...85641663S. doi:10.1016/j.cplett.2024.141663.
  10. ^ Kar, Supratik; Gallagher, Andrea (2024). "Comparative QSAR and q-RASAR Modeling for Aquatic Toxicity of Organic Chemicals to Three Trout Species: O. Clarkii, S. Namaycush, and S. Fontinalis". Journal of Hazardous Materials. 480. Bibcode:2024JHzM..48036060K. doi:10.1016/j.jhazmat.2024.136060. PMID 39393319.
  11. ^ Rahimi-Soujeh, Zaniar; Safaie, Naser; Moradi, Sajad; Abbod, Mohsen; Sharifi, Rouhalah; Mojerlou, Shideh; Mokhtassi-Bidgoli, Ali (2024). "New binary mixtures of fungicides against Macrophomina phaseolina: machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation". Chemosphere. 366. Bibcode:2024Chmsp.36643533R. doi:10.1016/j.chemosphere.2024.143533.
  12. ^ Abdellatif, Hayet; Laidi, Maamar; Si-Moussa, Cherif; Amrane, Abdeltif; Euldji, Imane; Benmouloud, Widad (2024). "Contributions to the development of prediction models for the toxicity of ionic liquids". Structural Chemistry. 36 (3): 865–886. doi:10.1007/s11224-024-02411-4.
  13. ^ Sun, Ting; Wei, Chongzhi; Liu, Yang; Ren, Yueying (2024). "Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus)". Science of the Total Environment. 957. Bibcode:2024ScTEn.95777399S. doi:10.1016/j.scitotenv.2024.177399. PMID 39521088.
  14. ^ Banjare, Purusottam; Murmu, Anjali; Matore, Balaji Wamanrao; Singh, Jagadish; Papa, Ester; Roy, Partha Pratim (2024). "Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report". Toxicology Research. 13 (6): tfae191. doi:10.1093/toxres/tfae191. PMC 11569388. PMID 39559274.
  15. ^ Qin, Li-Tang; Tian, Xue-Fang; Zhang, Jun-Yao; Liang, Yan-Peng; Zeng, Hong-Hu; Mo, Ling-Yun (2024). "A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)". Environmental International. 194. Bibcode:2024EnInt.19409162Q. doi:10.1016/j.envint.2024.109162. PMID 39612747.
  16. ^ Yang, Xianhai; Yang, Yue; Watson, Peter; Liu, Huihui (2025). "Development of Quantitative Structure Property Relationship Models and Tool for Predicting the Soil Adsorption Coefficient (logKOC)". Environmental Pollution. 368. doi:10.1016/j.envpol.2025.125703. PMID 39824331.
  17. ^ Li, Na; Chen, Zhaoyang; Zhang, Wenhui; Li, Yan; Huang, Xin; Li, Xiao (2025). "Web Server-based Deep Learning-Driven Predictive Models for Respiratory Toxicity of Environmental Chemicals: Mechanistic Insights and Interpretability". Journal of Hazardous Materials. 489. Bibcode:2025JHzM..48937575L. doi:10.1016/j.jhazmat.2025.137575. PMID 39954423.
  18. ^ Gao, Yuchen; Qiu, Yu; Wan, Fang; Cui, Shixuan; Zhao, Qiming; Zhao, Yaxuan; Zhang, Dirong; Zhang, Chunlong; Zhou, Jianhong; Liu, Weiping; Zhuang, Shulin (2025). "PBScreen: A Server for the High-Throughput Screening of Placental Barrier–Permeable Contaminants Based on Multifusion Deep Learning". Environmental Pollution. 370. doi:10.1016/j.envpol.2025.125858. PMID 39954759.
  19. ^ Yin, Zhipeng; Zhang, Min; Liu, Runzeng; Cai, Yong (2025). "Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation". Water Research. 280. Bibcode:2025WatRe.28023500Y. doi:10.1016/j.watres.2025.123500. PMID 40107212.
  20. ^ Sun, Zhiqi; Huo, Donghui; Guo, Jiangyu; Yan, Aixia (2025). "Modeling and Interpretability Study of the Structure–Activity Relationship for Multigeneration EGFR Inhibitors". ACS Omega. 10 (11): 11176–11187. doi:10.1021/acsomega.4c10464. PMC 11947818. PMID 40160792.
  21. ^ Bhattacharyya, Prodipta; Das, Shubha; Ojha, Probir Kumar (2025). "Risk Assessment of Industrial Chemicals Towards Salmon Species Amalgamating QSAR, q-RASAR, and ARKA Framework". Toxicology Reports. 14. Bibcode:2025ToxR...1402017B. doi:10.1016/j.toxrep.2025.102017. PMID 40255415.
  22. ^ Yu, Xinliang (2025). "Predicting chemical toxicity towards Raphidocelis subcapitata with quantum chemical descriptors". Algal Research. 89. Bibcode:2025AlgRe..8904055Y. doi:10.1016/j.algal.2025.104055.
  23. ^ Banerjee, Arkaprava; Roy, Kunal (2025). "The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints". Environmental Science: Processes and Impacts. 27 (5): 1229–1243. doi:10.1039/D5EM00068H. PMID 40227888.


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