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Consequently, a wide range of in silico models based on deductive and inductive reasoning have been developed. However, the General Solubility Equation (GSE) stands out as a simple, thermodynamically derived equation to predict intrinsic solubility (log\u202fS0, in mol\/L) of molecules from their melting point and n-octanol\/water partition coefficient (log\u202fPN). Despite its widespread use, the applicability of this equation has not been rigorously evaluated to establish when it can or cannot produce accurate predictions. Therefore, a large-scale evaluation of the GSE was conducted using an extensive database of fully experimental melting point and log\u202fPN data for 2740, mostly drug-like compounds. A systematic analysis based on cheminformatics descriptors was conducted to identify the primary factors that allow the GSE to achieve predictions within the experimental uncertainty range (\u00b11 log\u202fS unit). This information has been used to create GSESolver: a supervised machine learning classification model that, based on the selected descriptors, can predict whether the GSE will perform accurately solely from its SMILES string. GSESolver tool can be used by anyone in the scientific community to quickly assess molecule solubility and it was developed under the FAIR principles (Findable, Accessible, Interoperable, and Reusable) in a Google Colab notebook to ensure ease of use. Finally, the utility of the GSESolver tool was exploited in medicinal chemistry applications for the in silico prediction of the Maximum Absorbable Dose (MAD) of potential drugs, and in a rational agrochemical design application where solubility is a key factor.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01182","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T19:14:05Z","timestamp":1788203645000},"page":"11330-11347","source":"Crossref","is-referenced-by-count":0,"title":["Data-Driven Critical\nEvaluation of the General Solubility\nEquation"],"prefix":"10.1021","volume":"66","author":[{"given":"Esteban","family":"Bertsch-Aguilar","sequence":"first","affiliation":[{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]},{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahomy","family":"Quiro\u0301s","sequence":"additional","affiliation":[{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederick","family":"Schosinsky","sequence":"additional","affiliation":[{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvana","family":"Pinheiro","sequence":"additional","affiliation":[{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4940-1107","authenticated-orcid":true,"given":"Jose\u0301 L.","family":"Medina-Franco","sequence":"additional","affiliation":[{"name":"Universidad Nacional Auto\u0301noma de Me\u0301xico , , ,","place":["Mexico City, Mexico, 04510"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-4528","authenticated-orcid":true,"given":"William J.","family":"Zamora","sequence":"additional","affiliation":[{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]},{"name":"Universidad de Costa Rica , , San Pedro , ,","place":["San Jose\u0301, Costa Rica, 11501"]},{"name":"National High Technology Center (CeNAT) , , Pavas , ,","place":["San Jose\u0301, Costa Rica, 11501"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"2026091409065434800_cit1","unstructured":"The IUPAC Compendium of Chemical Terminology.\n          . 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Pharmaceutics"},{"key":"2026091409065434800_cit5","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.addr.2007.05.008","article-title":"Solubility\nof sparingly-soluble ionizable drugs","volume":"59","author":"Avdeef","year":"2007","journal-title":"Adv. Drug\nDelivery Rev."},{"key":"2026091409065434800_cit6","doi-asserted-by":"publisher","first-page":"328","DOI":"10.2174\/138620711795508331","article-title":"Recent Advances\non Aqueous Solubility Prediction","volume":"14","author":"Wang","year":"2011","journal-title":"CCHTS"},{"key":"2026091409065434800_cit7","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-025-01106-0","article-title":"Improved estimation\nof intrinsic solubility of drug-like molecules through multi-task\ngraph transformer","volume":"17","author":"Zhao","year":"2025","journal-title":"J. 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Cheminform"},{"key":"2026091409065434800_cit48","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1039\/D3DD00217A","article-title":"Predicting small\nmolecules solubility on endpoint devices\nusing deep ensemble neural networks","volume":"3","author":"Ramos","year":"2024","journal-title":"Digital\nDiscovery"},{"key":"2026091409065434800_cit49","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1002\/1520-6017(200102)90:2&lt;234::AID-JPS14&gt;3.0.CO;2-V","article-title":"Estimation\nof the aqueous solubility I: Application\nto organic nonelectrolytes","volume":"90","author":"Jain","year":"2001","journal-title":"J. Pharm. Sci."},{"key":"2026091409065434800_cit50","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1002\/qsar.200390020","article-title":"Estimation\nof Aqueous Solubility By The General Solubility Equation (GSE) The\nEasy Way","volume":"22","author":"Sanghvi","year":"2003","journal-title":"QSAR Comb. 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Sci."},{"key":"2026091409065434800_cit85","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1021\/ci00028a014","article-title":"Electrotopological\nState Indices for Atom Types: A\nNovel Combination of Electronic, Topological, and Valence State Information","volume":"35","author":"Hall","year":"1995","journal-title":"J. Chem. Inf. Comput. 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Chem."},{"key":"2026091409065434800_cit89","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1214\/ss\/1177011454","article-title":"A Survey of\nExact Inference for Contingency Tables","volume":"7","author":"Agresti","year":"1992","journal-title":"Stat.\nSci."},{"key":"2026091409065434800_cit90","doi-asserted-by":"publisher","first-page":"289","DOI":"10.2307\/2346101","article-title":"Controlling the False\nDiscovery Rate: A Practical and\nPowerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. Royal\nStat. Soc., Ser. B: Stat. Methodol."},{"key":"2026091409065434800_cit91","unstructured":"scikit-learn: Machine Learning in Python\n\u2500 Scikit-learn 1.7.2\nDocumentation. https:\/\/scikit-learn.org\/stable\/."},{"key":"2026091409065434800_cit92","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1&lt;32::AID-CNCR2820030106&gt;3.0.CO;2-3","article-title":"Index for\nrating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"2026091409065434800_cit93","doi-asserted-by":"crossref","unstructured":"Chen, T.; Guestrin, C. 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Eng."},{"key":"2026091409065434800_cit98","doi-asserted-by":"publisher","first-page":"37934","DOI":"10.1021\/acsomega.4c04474","article-title":"Identification\nof Optimal Machine\nLearning Algorithms and Molecular Fingerprints for Explainable Toxicity\nPrediction Models Using ToxCast\/Tox21 Bioassay Data","volume":"9","author":"Kim","year":"2024","journal-title":"ACS Omega"},{"key":"2026091409065434800_cit99","doi-asserted-by":"crossref","unstructured":"Fenogli, J.; Grimaud, L.; Vuilleumier, R.\n          Constructing and\nExplaining Machine Learning Models for Chemistry: Example of the Exploration\nand Design of Boron-based Lewis Acids.\n          2025, arXiv:2501.01576. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2501.01576.","DOI":"10.1039\/D5DD00212E"},{"key":"2026091409065434800_cit100","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and its\nSolutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. 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Chem."},{"key":"2026091409065434800_cit103","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1002\/9780470125793.ch9","article-title":"The Molecular\nConnectivity Chi Indexes and Kappa Shape Indexes in Structure-Property\nModeling","volume-title":"Reviews in Computational Chemistry","author":"Hall","year":"1991"},{"key":"2026091409065434800_cit104","unstructured":"FractionalPSADescriptor (cdk 1.5.15 API). https:\/\/cdk.github.io\/cdk\/1.5\/docs\/api\/org\/openscience\/cdk\/qsar\/descriptors\/molecular\/FractionalPSADescriptor.html."},{"key":"2026091409065434800_cit105","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1021\/ci00001a012","article-title":"The electrotopological\nstate: structure information at the atomic level for molecular graphs","volume":"31","author":"Hall","year":"1991","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"2026091409065434800_cit106","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"Bickerton","year":"2012","journal-title":"Nat. Chem."},{"key":"2026091409065434800_cit107","doi-asserted-by":"publisher","first-page":"3762","DOI":"10.1021\/jp980230o","article-title":"Prediction\nof Hydrophobic (Lipophilic) Properties of Small Organic Molecules\nUsing Fragmental Methods: An Analysis of ALOGP and CLOGP Methods","volume":"102","author":"Ghose","year":"1998","journal-title":"J. Phys. Chem. 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Sci."},{"key":"2026091409065434800_cit114","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1002\/etc.5439","article-title":"Bioaccumulation\nScreening of Neutral Hydrophobic Organic Chemicals in Air-Breathing\nOrganisms Using In Vitro Rat Liver S9 Biotransformation Assays","volume":"41","author":"Lee","year":"2022","journal-title":"Environ. Toxicol. Chem."},{"key":"2026091409065434800_cit115","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical\nTheory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. 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