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Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species\u2019 solubility using data for over 8400 compounds. Molecular-descriptors, the most used method in previous studies, and Morgan fingerprint, a circular-based hash of the molecules' structures, were applied to produce water solubility estimates. We trained all models on 80% of the total datasets using the Random Forest (RFs) technique as the regressor and tested the prediction performance using the remaining 20%, resulting in coefficient of determination (R\n                    <jats:sup>2<\/jats:sup>\n                    ) test values of 0.88 and 0.81 and root-mean-square deviation (RMSE) test values 0.64 and 0.80 for the descriptors and circular fingerprint methods, respectively. We interpreted the produced ML models and reported the most effective features for aqueous solubility measures using the Shapley Additive exPlanations (SHAP) and thermodynamic analysis. Low error, ability to investigate the molecular-level interactions, and compatibility with thermodynamic quantities made the fingerprint method a distinct model compared to other available computational tools. However, it is worth emphasizing that physicochemical descriptor model outperformed the fingerprint model in achieving better predictive accuracy for the given test set.\n                  <\/jats:p>","DOI":"10.1186\/s13321-023-00752-6","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T02:02:19Z","timestamp":1697594539000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models"],"prefix":"10.1186","volume":"15","author":[{"given":"Arash","family":"Tayyebi","sequence":"first","affiliation":[]},{"given":"Ali S","family":"Alshami","sequence":"additional","affiliation":[]},{"given":"Zeinab","family":"Rabiei","sequence":"additional","affiliation":[]},{"given":"Xue","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Nadhem","family":"Ismail","sequence":"additional","affiliation":[]},{"given":"Musabbir Jahan","family":"Talukder","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Power","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"752_CR1","doi-asserted-by":"publisher","first-page":"5753","DOI":"10.1038\/s41467-020-19594-z","volume":"11","author":"S Boobier","year":"2020","unstructured":"Boobier S, Hose DRJ, Blacker AJ, Nguyen BN (2020) Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. 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