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Two such p-chem properties are the octanol\u2013water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.<\/jats:p>","DOI":"10.1021\/acs.jcim.2c01373","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T17:43:12Z","timestamp":1678988592000},"page":"1906-1913","source":"Crossref","is-referenced-by-count":47,"title":["Using Machine Learning\nTo Predict Partition Coefficient\n(Log\n                    <i>P<\/i>\n                    ) and Distribution Coefficient (Log\n                    <i>D<\/i>\n                    ) with Molecular Descriptors and Liquid Chromatography\nRetention Time"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8884-697X","authenticated-orcid":true,"given":"Zaw-Myo","family":"Win","sequence":"first","affiliation":[{"name":"Centre for Eye and Vision Research , Hong Kong Science Park , ,","place":["New Territories, Hong Kong, 999077"]},{"name":"The Hong Kong Polytechnic University , , ,","place":["Kowloon, Hong Kong, 999077"]},{"name":"University of Waterloo , , 200 University Avenue West , , ,","place":["Waterloo, Ontario, Canada, N2L 3G1"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen M. Y.","family":"Cheong","sequence":"additional","affiliation":[{"name":"Centre for Eye and Vision Research , Hong Kong Science Park , ,","place":["New Territories, Hong Kong, 999077"]},{"name":"The Hong Kong Polytechnic University , , ,","place":["Kowloon, Hong Kong, 999077"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1617-9220","authenticated-orcid":true,"given":"W. Scott","family":"Hopkins","sequence":"additional","affiliation":[{"name":"Centre for Eye and Vision Research , Hong Kong Science Park , ,","place":["New Territories, Hong Kong, 999077"]},{"name":"University of Waterloo , , 200 University Avenue West , , ,","place":["Waterloo, Ontario, Canada, N2L 3G1"]},{"name":"Waterloo Institute for Nanotechnology, University of Waterloo , 200 University Avenue West , , ,","place":["Waterloo, Ontario, Canada, N2L 3G1"]},{"name":"WaterMine Innovation, Inc. , , ,","place":["Waterloo, Ontario, Canada, N0B 2T0"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"2026081804441448700_cit1","doi-asserted-by":"publisher","first-page":"901","DOI":"10.2174\/1568026615666150825142002","article-title":"Advances in Computational Structure-Based\nDrug Design and Application in Drug Discovery","volume":"16","author":"Wang","year":"2016","journal-title":"Curr. Top. Med. Chem."},{"key":"2026081804441448700_cit2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/psp4.12049","article-title":"Good Practices in Model-Informed\nDrug Discovery and Development: Practice, Application, and Documentation","volume":"5","author":"Marshall","year":"2016","journal-title":"CPT: Pharmacometrics Syst. Pharmacol."},{"key":"2026081804441448700_cit3","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","article-title":"Principles of Early\nDrug Discovery","volume":"162","author":"Hughes","year":"2011","journal-title":"Br. J. Pharmacol."},{"key":"2026081804441448700_cit4","first-page":"791","article-title":"Using Physicochemical\nMeasurements to Influence Better Compound Design","volume-title":"SLAS Discovery","author":"Bunally","year":"2019"},{"key":"2026081804441448700_cit5","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1517\/17460441.2012.714363","article-title":"The Influence of\nLipophilicity in Drug Discovery and\nDesign","volume":"7","author":"Arnott","year":"2012","journal-title":"Expert Opin. Drug Discovery"},{"key":"2026081804441448700_cit6","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1517\/17460441003605098","article-title":"Lipophilicity\nin Drug Discovery","volume":"5","author":"Waring","year":"2010","journal-title":"Expert Opin. Drug Discovery"},{"key":"2026081804441448700_cit7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","article-title":"Experimental and\nComputational Approaches to Estimate Solubility and Permeability in\nDrug Discovery and Development Settings","volume":"23","author":"Lipinski","year":"1997","journal-title":"Adv.\nDrug Delivery Rev."},{"key":"2026081804441448700_cit8","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.ddtec.2004.11.007","article-title":"Lead- and\nDrug-like Compounds: The Rule-of-Five Revolution","volume":"1","author":"Lipinski","year":"2004","journal-title":"Drug Discovery Today: Technol."},{"key":"2026081804441448700_cit9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/S1535-5535-04-00243-6","article-title":"High-Throughput Measurement\nof Drug PKa Values for ADME Screening","volume":"8","author":"Comer","year":"2003","journal-title":"JALA"},{"key":"2026081804441448700_cit10","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/nrd3367","article-title":"Probing the\nLinks between in Vitro Potency, ADMET and Physicochemical Parameters","volume":"10","author":"Gleeson","year":"2011","journal-title":"Nat. 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Toxicol."},{"key":"2026081804441448700_cit12","doi-asserted-by":"publisher","first-page":"177","DOI":"10.5599\/admet.1022","article-title":"Drug-like Properties and Fraction Lipophilicity Index\nas a Combined Metric","volume":"9","author":"Tsantili-Kakoulidou","year":"2021","journal-title":"ADMET DMPK"},{"key":"2026081804441448700_cit13","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1002\/elps.200410053","article-title":"Analysis\nof Anticancer Platinum\u00ad(II)-Complexes by Microemulsion Electrokinetic\nChromatography: Separation of Diastereomers and Estimation of Octanol-Water\nPartition Coefficients","volume":"26","author":"Rappel","year":"2005","journal-title":"Electrophoresis"},{"key":"2026081804441448700_cit14","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1021\/acs.jced.9b01129","article-title":"Experimental Determination of Octanol-Water\nPartition\nCoefficients of Selected Natural Toxins","volume":"65","author":"Scho\u0308nsee","year":"2020","journal-title":"J.\nChem. Eng. Data"},{"key":"2026081804441448700_cit15","first-page":"7","volume-title":"QSAR and Strategies\nin the Design of Bioactiue Compounds","author":"Dearden","year":"1988"},{"key":"2026081804441448700_cit16","doi-asserted-by":"publisher","first-page":"20594","DOI":"10.1039\/D2CP02540J","article-title":"The Hitchhiker\u2019s Guide to Dynamic Ion-Solvent\nClustering: Applications in Differential Ion Mobility Spectrometry","volume":"24","author":"Ieritano","year":"2022","journal-title":"Phys. Chem. Chem. Phys."},{"key":"2026081804441448700_cit17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1021\/acscentsci.6b00297","article-title":"Assessing\nPhysicochemical Properties\nof Drug Molecules via Microsolvation Measurements with Differential\nMobility Spectrometry","volume":"3","author":"Liu","year":"2017","journal-title":"ACS Cent. 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Chem."},{"key":"2026081804441448700_cit21","doi-asserted-by":"publisher","first-page":"2048","DOI":"10.1021\/ci0340916","article-title":"Drug Discovery\nUsing Support Vector Machines. The Case Studies of Drug-Likeness,\nAgrochemical-Likeness, and Enzyme Inhibition Predictions","volume":"43","author":"Zernov","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"2026081804441448700_cit22","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1021\/ci0341161","article-title":"Comparison\nof Support Vector Machine and Artificial Neural Network Systems for\nDrug\/Nondrug Classification","volume":"43","author":"Byvatov","year":"2003","journal-title":"J. Chem. Inf. 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F.; Vinyals, O.; Dahl, G.\nE. Neural Message Passing for Quantum\nChemistry. 2017, arXiv:1704.01212. https:\/\/doi.org\/10.48550\/arXiv.1704.01212."},{"key":"2026081804441448700_cit30","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10822-020-00292-3","article-title":"A Deep Learning\nApproach for the Blind LogP Prediction\nin SAMPL6 Challenge","volume":"34","author":"Prasad","year":"2020","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"2026081804441448700_cit31","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s10822-020-00279-0","article-title":"A Comparison\nof Molecular Representations\nfor Lipophilicity Quantitative Structure\u2013Property Relationships\nwith Results from the SAMPL6 LogP Prediction Challenge","volume":"34","author":"Lui","year":"2020","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"2026081804441448700_cit32","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s10822-021-00405-6","article-title":"Multitask Machine Learning Models\nfor Predicting Lipophilicity\n(LogP) in the SAMPL7 Challenge","volume":"35","author":"Lenselink","year":"2021","journal-title":"J. Comput.-Aided\nMol. Des."},{"key":"2026081804441448700_cit33","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s42004-021-00528-9","article-title":"Exploring\nthe Octanol\u2013Water\nPartition Coefficient Dataset Using Deep Learning Techniques and Data\nAugmentation","volume":"4","author":"Ulrich","year":"2021","journal-title":"Commun. Chem."},{"key":"2026081804441448700_cit34","doi-asserted-by":"publisher","DOI":"10.3390\/molecules26154475","article-title":"Prediction\nof Molecular Properties Using Molecular Topographic Map","author":"Yoshimori","year":"2021","journal-title":"Molecules"},{"key":"2026081804441448700_cit35","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13321-018-0263-1","article-title":"OPERA Models for\nPredicting Physicochemical Properties and Environmental Fate Endpoints","volume":"10","author":"Mansouri","year":"2018","journal-title":"Aust. J. Chem."},{"key":"2026081804441448700_cit36","doi-asserted-by":"publisher","first-page":"104309","DOI":"10.1016\/j.chemolab.2021.104309","article-title":"Efficient\nLipophilicity Prediction of Molecules Employing\nDeep-Learning Models","volume":"213","author":"Datta","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"2026081804441448700_cit37","doi-asserted-by":"publisher","first-page":"5811","DOI":"10.1038\/s41467-019-13680-7","article-title":"The METLIN Small Molecule Dataset\nfor Machine Learning-Based\nRetention Time Prediction","volume":"10","author":"Domingo-Almenara","year":"2019","journal-title":"Nat. Commun."},{"key":"2026081804441448700_cit38","doi-asserted-by":"publisher","first-page":"D1083","DOI":"10.1093\/nar\/gkt1031","article-title":"The ChEMBL\nBioactivity Database:\nAn Update","volume":"42","author":"Bento","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"2026081804441448700_cit39","unstructured":"Landrum, G.\n          \n          Rdk. Open-Source. Cheminformatics; 2012; http:\/\/www.rdkit.org. (Accessed 30 July 2021)."},{"key":"2026081804441448700_cit40","unstructured":"RDKit: open-source cheminformatics\nsoftware."},{"key":"2026081804441448700_cit41","first-page":"2825","article-title":"Scikit-Learn:\nMachine Learning in\nPython","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"2026081804441448700_cit42","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations\nby Back-Propagating\nErrors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"2026081804441448700_cit43","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"1999 Reitz\nLecture Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. 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Chem."},{"key":"2026081804441448700_cit49","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1021\/acs.jcim.6b00591","article-title":"Extreme\nGradient Boosting as a Method for Quantitative Structure-Activity\nRelationships","volume":"56","author":"Sheridan","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"2026081804441448700_cit50","doi-asserted-by":"publisher","first-page":"4150","DOI":"10.1021\/acs.jcim.9b00633","article-title":"LightGBM:\nAn Effective\nand Scalable Algorithm for Prediction of Chemical Toxicity-Application\nto the Tox21 and Mutagenicity Data Sets","volume":"59","author":"Zhang","year":"2019","journal-title":"J.\nChem. Inf. Model."},{"key":"2026081804441448700_cit51","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1021\/ci010243q","article-title":"Differential\nShannon Entropy Analysis\nIdentifies Molecular Property Descriptors That Predict Aqueous Solubility\nof Synthetic Compounds with High Accuracy in Binary QSAR Calculations","volume":"42","author":"Stahura","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"2026081804441448700_cit52","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A Classification and\nRegression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"2026081804441448700_cit53","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1021\/ci100104j","article-title":"Estimation\nof ADME Properties with Substructure Pattern Recognition","volume":"50","author":"Shen","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"2026081804441448700_cit54","doi-asserted-by":"publisher","first-page":"4463","DOI":"10.1021\/jm0303195","article-title":"Classification of Kinase\nInhibitors Using a Bayesian Model","volume":"47","author":"Xia","year":"2004","journal-title":"J. Med. Chem."},{"key":"2026081804441448700_cit55","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1080\/1062936X.2016.1229691","article-title":"Predicting the Aquatic Toxicity Mode of Action Using\nLogistic Regression and Linear Discriminant Analysis","volume":"27","author":"Ren","year":"2016","journal-title":"SAR QSAR Environ. Res."},{"key":"2026081804441448700_cit56","volume-title":"The Generation of\na Unique Machine Description for Chemical Structures\ue5f8A Technique\nDeveloped at Chemical Abstracts Service","author":"Morgan","year":"1964"},{"key":"2026081804441448700_cit57","unstructured":"Kingma, D. P.; Ba, J. Adam:\nA Method for Stochastic Optimization. 2014, arXiv:1412.6980. https:\/\/doi.org\/10.48550\/arXiv.1412.6980."},{"key":"2026081804441448700_cit58","unstructured":"Lundberg, S. M.; Lee, S.-I.\n          A Unified Approach\nto Interpreting Model Predictions. In Proceedings\nof the 31st International Conference on Neural Information Processing\nSystems; 2017; pp 4768\u20134777."},{"key":"2026081804441448700_cit59","unstructured":"Chemical Computing\nGroup Inc.\n          \n          Molecular Operating Environment\n(MOE); Canada; 2008."},{"key":"2026081804441448700_cit60","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1023\/A:1027232610247","article-title":"Novel Software Tools for Chemical Diversity","volume":"9","author":"Pearlman","year":"1998","journal-title":"Perspect. Drug Discovery Des."},{"key":"2026081804441448700_cit61","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","author":"Pan","year":"2010","journal-title":"IEEE Trans.\nKnowl. Data Eng."},{"key":"2026081804441448700_cit62","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1021\/es9600946","article-title":"Modeling\nthe Desorption of Organic Contaminants from Long-Term Contaminated\nSoil Using Distributed Mass Transfer Rates","volume":"31","author":"Culver","year":"1997","journal-title":"Environ. Sci. Technol."}],"container-title":["Journal of Chemical Information\nand Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.2c01373","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/63\/7\/1906\/8908988\/ci2c01373.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/63\/7\/1906\/8908988\/ci2c01373.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:03:50Z","timestamp":1787043830000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/63\/7\/1906\/850310\/Using-Machine-Learning-To-Predict-Partition"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":62,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,3,16]]},"published-print":{"date-parts":[[2023,4,10]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.2c01373","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,16]]}}}