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Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1\/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD50 of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log\u00ad(S), predictions, our approach outperformed the fingerprint-based model.<\/jats:p>","DOI":"10.1021\/acs.jcim.3c00050","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T12:16:36Z","timestamp":1678191396000},"page":"2073-2083","source":"Crossref","is-referenced-by-count":9,"title":["Quantifying Functional-Group-like\nStructural Fragments\nin Molecules and Its Applications in Drug Design"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2422-1601","authenticated-orcid":true,"given":"Goutam","family":"Mukherjee","sequence":"first","affiliation":[{"name":"PharmCADD Co. 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In Under-Sampling\nApproaches for Improving Prediction of the Minority Class in an Imbalanced\nDataset, Intelligent Control and Automation: International\nConference on Intelligent Computing, ICIC 2006 Kunming, China, August\n16\u201319; Huang, D.-S.; Li, K.; Irwin, G. W., Eds.; Springer Berlin Heidelberg: Berlin,\nHeidelberg, 2006; pp 731\u2013740.","DOI":"10.1007\/978-3-540-37256-1_89"},{"key":"2026081804443781800_cit41","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"2026081804443781800_cit42","unstructured":"Buitinck, L.; Louppe, G.; Blondel, M.; Pedregosa, F.; Mueller, A.; Grisel, O.; Niculae, V.; Prettenhofer, P.; Gramfort, A.; Grobler, J.; Layton, R.; VanderPlas, J.; Joly, A.; Holt, B.; Varoquaux, G. In API Design for Machine Learning Software:\nExperiences from the Scikit-Learn Project, ECML PKDD Workshop:\nLanguages for Data Mining and Machine Learning, 2013;\npp 108\u2013122."},{"key":"2026081804443781800_cit43","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"2026081804443781800_cit44","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely\nRandomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"2026081804443781800_cit45","unstructured":"Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.-Y.\n          Lightgbm: A Highly Efficient Gradient Boosting Decision Tree. Adv. Neural Inf. Process. 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