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The results showed that extreme gradient boosting (XGBoost) gave good prediction performance in handling complex and nonlinear patterns in the data. In all cases, the feature importance analysis showed a consistent ranking. It shows how molecular descriptors had an influence on property prediction. This study shows how molecular descriptors and machine learning may be used for predicting properties and computational drug design.<\/jats:p>","DOI":"10.1021\/acsomega.6c04218","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T01:01:53Z","timestamp":1788483713000},"page":"55465-55474","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Machine\nLearning Approaches and Topological\nIndices for Prediction of Electronic Properties in Anticancer Drugs"],"prefix":"10.1021","volume":"11","author":[{"given":"Simran","family":"Kour","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology , , , ,","place":["Vellore, Tamil Nadu, India, 632 014"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9094-503X","authenticated-orcid":true,"given":"J. 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