{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:09:02Z","timestamp":1788786542744,"version":"build-2803163510"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Chromatography A"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.chroma.2026.467335","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T15:39:07Z","timestamp":1785944347000},"page":"467335","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Quantitative structure\u2013retention relationships (QSRR): Effect of experimental variables on QSRR model performance in HPLC - A critical review"],"prefix":"10.1016","volume":"1786","author":[{"given":"Mayur","family":"Borase","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunal","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piyush","family":"Ghode","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0001","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s13065-024-01118-1","article-title":"Development of chromatographic methods to determine multivitamins formulation depending on their solubility and polarity: comparative study using three greenness assessment tools","volume":"18","author":"Galal","year":"2024","journal-title":"BMC. Chem."},{"issue":"16","key":"10.1016\/j.chroma.2026.467335_bib0002","doi-asserted-by":"crossref","first-page":"4819","DOI":"10.3390\/molecules26164819","article-title":"A new definition of the stationary phase volume in mixed-mode chromatographic columns in hydrophilic liquid chromatography","volume":"26","author":"Jandera","year":"2021","journal-title":"Molecules"},{"key":"10.1016\/j.chroma.2026.467335_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2024.465383","article-title":"Thermodynamic analysis of adsorption and retention behaviors in normal-phase liquid chromatography","volume":"1736","author":"Tsui","year":"2024","journal-title":"J. Chromatogr. A"},{"issue":"7","key":"10.1016\/j.chroma.2026.467335_bib0004","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1021\/cr068412z","article-title":"Quantitative structure-(Chromatographic) retention relationships","volume":"107","author":"Kaliszan","year":"2007","journal-title":"Chem. Rev."},{"issue":"6","key":"10.1016\/j.chroma.2026.467335_bib0005","doi-asserted-by":"crossref","first-page":"3674","DOI":"10.1021\/acs.chemrev.8b00246","article-title":"Column characterization and selection systems in reversed-phase high-performance liquid chromatography","volume":"119","author":"\u017duvela","year":"2019","journal-title":"Chem. Rev."},{"issue":"12","key":"10.1016\/j.chroma.2026.467335_bib0006","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1007\/s00216-024-05243-7","article-title":"Cross-column density functional theory\u2013based quantitative structure-retention relationship model development powered by machine learning","volume":"416","author":"Mazraedoost","year":"2024","journal-title":"Anal. Bioanal. Chem."},{"key":"10.1016\/j.chroma.2026.467335_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpba.2023.115690","article-title":"A multi-target QSRR approach to model retention times of small molecules in RPLC","volume":"236","author":"Kumari","year":"2023","journal-title":"J. Pharm. Biomed. Anal."},{"key":"10.1016\/j.chroma.2026.467335_bib0008","series-title":"q-RASAR","author":"Roy","year":"2024"},{"issue":"4","key":"10.1016\/j.chroma.2026.467335_bib0009","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.3390\/molecules28041696","article-title":"Quantitative structure retention-relationship modeling: towards an innovative general-purpose strategy","volume":"28","author":"Kumari","year":"2023","journal-title":"Molecules"},{"key":"10.1016\/j.chroma.2026.467335_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2021.462108","article-title":"Solvation parameter model: tutorial on its application to separation systems for neutral compounds","volume":"1645","author":"Poole","year":"2021","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcoa.2024.100158","article-title":"Application of Abraham\u2019s solvation parameter model to extractables and leachables studies in pharmaceutical and medical device industries: a tutorial","volume":"6","author":"Li","year":"2024","journal-title":"J. Chromat. Open"},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0012","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13321-023-00737-5","article-title":"PyL3dMD: python LAMMPS 3D molecular descriptors package","volume":"15","author":"Panwar","year":"2023","journal-title":"J. Cheminform."},{"key":"10.1016\/j.chroma.2026.467335_bib0013","series-title":"Molecular Descriptors for Chemoinformatics","author":"Todeschini","year":"2009"},{"issue":"5","key":"10.1016\/j.chroma.2026.467335_bib0014","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.3390\/molecules28052270","article-title":"Prediction of the n-octanol\/water partition coefficients of basic compounds using multi-parameter QSRR models based on IS-RPLC retention behavior in a wide pH range","volume":"28","author":"Qiao","year":"2023","journal-title":"Molecules"},{"issue":"12","key":"10.1016\/j.chroma.2026.467335_bib0015","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3390\/a16120573","article-title":"On the development of descriptor-based machine learning models for thermodynamic properties: part 2\u2014Applicability domain and outliers","volume":"16","author":"Trinh","year":"2023","journal-title":"Algorithms"},{"issue":"27","key":"10.1016\/j.chroma.2026.467335_bib0016","doi-asserted-by":"crossref","first-page":"5214","DOI":"10.1016\/j.chroma.2009.05.006","article-title":"Prediction of retention in reversed-phase liquid chromatography by means of the polarity parameter model","volume":"1216","author":"L\u00e1zaro","year":"2009","journal-title":"J. Chromatogr. A"},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0017","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40543-022-00322-9","article-title":"Kralj Cigi\u0107 I. An exploration into the use of Hansen solubility parameters for modelling reversed-phase chromatographic separations","volume":"13","author":"Ribar","year":"2022","journal-title":"J. Anal. Sci. Technol."},{"key":"10.1016\/j.chroma.2026.467335_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2021.462108","article-title":"Solvation parameter model: tutorial on its application to separation systems for neutral compounds","volume":"1645","author":"Poole","year":"2021","journal-title":"J. Chromatogr. A"},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0019","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/jssc.202000905","article-title":"Recent applications of retention modelling in liquid chromatography","volume":"44","author":"den Uijl","year":"2021","journal-title":"J. Sep. Sci."},{"issue":"13","key":"10.1016\/j.chroma.2026.467335_bib0020","doi-asserted-by":"crossref","first-page":"6991","DOI":"10.1021\/acs.analchem.4c03466","article-title":"In Silico high-performance liquid chromatography method development via machine learning","volume":"97","author":"Marchetto","year":"2025","journal-title":"Anal. Chem."},{"issue":"6","key":"10.1016\/j.chroma.2026.467335_bib0021","doi-asserted-by":"crossref","first-page":"5993","DOI":"10.1021\/acsomega.4c09868","article-title":"Gradient retention time modeling in ion chromatography through ensemble machine learning-powered quantitative structure-retention relationships","volume":"10","author":"Lim","year":"2025","journal-title":"ACS. Omega"},{"issue":"10","key":"10.1016\/j.chroma.2026.467335_bib0022","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1016\/j.chroma.2008.09.051","article-title":"Retention models for isocratic and gradient elution in reversed-phase liquid chromatography","volume":"1216","author":"Nikitas","year":"2009","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0023","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.chroma.2016.09.062","article-title":"Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: use of retention factor ratio","volume":"1486","author":"Tyteca","year":"2017","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0024","series-title":"Absorption and Drug Development","author":"Avdeef","year":"2012"},{"issue":"23","key":"10.1016\/j.chroma.2026.467335_bib0025","doi-asserted-by":"crossref","first-page":"8306","DOI":"10.3390\/molecules27238306","article-title":"User-driven strategy for In silico screening of reversed-phase liquid chromatography conditions for known pharmaceutical-related small molecules","volume":"27","author":"Van Laethem","year":"2022","journal-title":"Molecules"},{"issue":"11","key":"10.1016\/j.chroma.2026.467335_bib0026","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1021\/ac961242d","article-title":"Chromatographic hydrophobicity index by fast-gradient RP-HPLC: a high-throughput alternative to log P\/log D","volume":"69","author":"Valk\u00f3","year":"1997","journal-title":"Anal. Chem."},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0027","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.chroma.2009.11.038","article-title":"Micelle to trapping solution stacking in micellar electrokinetic chromatography","volume":"1217","author":"Liu","year":"2010","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0028","doi-asserted-by":"crossref","unstructured":"Rutan, S., Kempen, T., Dahlseid, T., Kruger, Z., Pirok, B., Shackman, J., et al. Improved hydrophobic subtraction model of reversed-phase liquid chromatography selectivity based on a large dataset with a focus on isomer selectivity. 2024. doi:10.26434\/chemrxiv-2024-mt2fp.","DOI":"10.26434\/chemrxiv-2024-mt2fp"},{"key":"10.1016\/j.chroma.2026.467335_bib0029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chroma.2018.01.053","article-title":"Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model","volume":"1541","author":"Wen","year":"2018","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0030","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.chroma.2017.06.026","article-title":"Understanding and manipulating the separation in hydrophilic interaction liquid chromatography","volume":"1523","author":"McCalley","year":"2017","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2021.461985","article-title":"Effect of the polydispersity on the dispersion of polymers through silicas having different morphologies (fully porous and core-shell particles and monoliths)","volume":"1641","author":"Nguyen","year":"2021","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0032","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.chroma.2018.11.051","article-title":"Investigation and prediction of retention characteristics of imidazoline and serotonin receptor ligands and their related compounds on mixed-mode stationary phase","volume":"1585","author":"Obradovi\u0107","year":"2019","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2025.466505","article-title":"Novel computational modeling of trimodal chromatographic retention using Box\u2013Cox and multivariate analysis","volume":"1764","author":"Komsta","year":"2025","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2023.463776","article-title":"The secret of reversed-phase\/weak cation exchange retention mechanisms in mixed-mode liquid chromatography applied for small drug molecule analysis","volume":"1690","author":"Svrkota","year":"2023","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0035","series-title":"Catalysis in the Refining of Fischer-Tropsch Syncrude","author":"de Klerk","year":"2010"},{"issue":"21","key":"10.1016\/j.chroma.2026.467335_bib0036","doi-asserted-by":"crossref","first-page":"5193","DOI":"10.1021\/ac000591b","article-title":"Retention of ionizable compounds on HPLC. 5. pH scales and the Retention of acids and bases with acetonitrile\u2212water mobile phases","volume":"72","author":"Espinosa","year":"2000","journal-title":"Anal Chem."},{"key":"10.1016\/j.chroma.2026.467335_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2022.463442","article-title":"Micellar and sub-micellar chromatography with a cocamidopropyl betaine surfactant","volume":"1681","author":"Wilson","year":"2022","journal-title":"J. Chromatogr. A"},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0038","doi-asserted-by":"crossref","first-page":"5811","DOI":"10.1038\/s41467-019-13680-7","article-title":"The METLIN small molecule dataset for machine learning-based retention time prediction","volume":"10","author":"Domingo-Almenara","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.chroma.2026.467335_bib0039","doi-asserted-by":"crossref","unstructured":"Bouwmeester, R., Martens, L., Degroeve, S. Comprehensive and empirical evaluation of machine learning algorithms for LC retention time prediction. 2018. doi:10.1101\/259168.","DOI":"10.1101\/259168"},{"key":"10.1016\/j.chroma.2026.467335_bib0040","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.chroma.2011.12.041","article-title":"Quality by design: multidimensional exploration of the design space in high performance liquid chromatography method development for better robustness before validation","volume":"1232","author":"Monks","year":"2012","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.trac.2021.116294","article-title":"Perspectives and recent advances in quantitative structure-retention relationships for high performance liquid chromatography. How far are we?","volume":"141","author":"Sagandykova","year":"2021","journal-title":"TrAC Trends Analyt. Chem."},{"key":"10.1016\/j.chroma.2026.467335_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2021.462547","article-title":"Ethyl chloroformate mediated gas chromatographic-mass spectrometric biomonitoring of acidic biomarkers of occupational exposure and endogenous metabolites in human urine","volume":"1656","author":"\u0158imn\u00e1\u010dov\u00e1","year":"2021","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2023.464439","article-title":"Deep graph convolutional network for small-molecule retention time prediction","volume":"1711","author":"Kang","year":"2023","journal-title":"J. Chromatogr. A"},{"issue":"6","key":"10.1016\/j.chroma.2026.467335_bib0044","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: a classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"11","key":"10.1016\/j.chroma.2026.467335_bib0045","doi-asserted-by":"crossref","first-page":"7515","DOI":"10.1021\/acs.analchem.9b05765","article-title":"Retip: retention time prediction for compound annotation in untargeted metabolomics","volume":"92","author":"Bonini","year":"2020","journal-title":"Anal. Chem."},{"issue":"15","key":"10.1016\/j.chroma.2026.467335_bib0046","doi-asserted-by":"crossref","first-page":"7698","DOI":"10.1021\/acs.analchem.5b01139","article-title":"Retention time prediction improves identification in nontargeted lipidomics approaches","volume":"87","author":"Aicheler","year":"2015","journal-title":"Anal. Chem."},{"key":"10.1016\/j.chroma.2026.467335_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2019.460690","article-title":"Utility of linear and nonlinear models for retention prediction in liquid chromatography","volume":"1613","author":"Gilar","year":"2020","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2020.460951","article-title":"Novel computational approaches to retention modeling in dual hydrophilic interactions\/reversed phase chromatography","volume":"1619","author":"Obradovi\u0107","year":"2020","journal-title":"J. Chromatogr. A"},{"issue":"8","key":"10.1016\/j.chroma.2026.467335_bib0049","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"10.1016\/j.chroma.2026.467335_bib0050","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/separations11020054","article-title":"A new ultra high-performance liquid chromatography-tandem mass spectrometry method to monitor 1-amino-4-methyl-piperazine and 1-methyl-4-nitroso-piperazine in rifampicin hydrolysis: a standard addition approach to quantify nitrosamine in rifampicin","volume":"11","author":"Panusa","year":"2024","journal-title":"Separations"},{"issue":"17","key":"10.1016\/j.chroma.2026.467335_bib0051","doi-asserted-by":"crossref","first-page":"2950","DOI":"10.1016\/j.chroma.2010.02.063","article-title":"Determination of 4-nonylphenol in water samples using 4-(2,6-dimethylhept-3-yl)phenol as new internal standard","volume":"1217","author":"Fischer","year":"2010","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0052","doi-asserted-by":"crossref","unstructured":"Maran, U., Sild, S., Tulp, I., Takkis, K., Moosus, M. Chapter 6. Molecular descriptors from two-dimensional chemical structure. In. 2010. p. 148\u201392. doi:10.1039\/9781849732093-00148.","DOI":"10.1039\/9781849732093-00148"},{"issue":"3","key":"10.1016\/j.chroma.2026.467335_bib0053","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae084","article-title":"RT-transformer: retention time prediction for metabolite annotation to assist in metabolite identification","volume":"40","author":"Xue","year":"2024","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.chroma.2026.467335_bib0054","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-7439(02)00085-0","article-title":"Transfer of multivariate calibration models: a review","volume":"64","author":"Feudale","year":"2002","journal-title":"Chem. Intellig. Laborat. Syst."},{"issue":"6\u20137","key":"10.1016\/j.chroma.2026.467335_bib0055","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol Inform."},{"issue":"2","key":"10.1016\/j.chroma.2026.467335_bib0056","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.chroma.2026.467335_bib0057","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2015.04.013","article-title":"On a simple approach for determining applicability domain of QSAR models","volume":"145","author":"Roy","year":"2015","journal-title":"Chem. Intellig. Laborat. Syst."},{"issue":"1","key":"10.1016\/j.chroma.2026.467335_bib0058","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/10826070701665626","article-title":"Current State of the art in HPLC methodology for lipophilicity assessment of basic drugs. A review","volume":"31","author":"Giaginis","year":"2007","journal-title":"J. Liq. Chromatogr. Relat. Technol."},{"issue":"6","key":"10.1016\/j.chroma.2026.467335_bib0059","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1097\/01.ftd.0000179845.53213.39","article-title":"METLIN","volume":"27","author":"Smith","year":"2005","journal-title":"Ther. Drug Monit."},{"issue":"2","key":"10.1016\/j.chroma.2026.467335_bib0060","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/s41592-023-02143-z","article-title":"RepoRT: a comprehensive repository for small molecule retention times","volume":"21","author":"Kretschmer","year":"2024","journal-title":"Nat. Methods"},{"key":"10.1016\/j.chroma.2026.467335_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2024.464731","article-title":"A comparative study of the predictive performance of different descriptor calculation tools: molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database","volume":"1719","author":"Obradovi\u0107","year":"2024","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2021.462792","article-title":"Deep learning for retention time prediction in reversed-phase liquid chromatography","volume":"1664","author":"Fedorova","year":"2022","journal-title":"J. Chromatogr. A"},{"key":"10.1016\/j.chroma.2026.467335_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.chroma.2025.465761","article-title":"Finding potentially erroneous entries in METLIN SMRT","volume":"1745","author":"Khrisanfov","year":"2025","journal-title":"J. Chromatogr. A"}],"container-title":["Journal of Chromatography A"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0021967326006631?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0021967326006631?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:22:56Z","timestamp":1788783776000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0021967326006631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":63,"alternative-id":["S0021967326006631"],"URL":"https:\/\/doi.org\/10.1016\/j.chroma.2026.467335","relation":{},"ISSN":["0021-9673"],"issn-type":[{"value":"0021-9673","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quantitative structure\u2013retention relationships (QSRR): Effect of experimental variables on QSRR model performance in HPLC - A critical review","name":"articletitle","label":"Article Title"},{"value":"Journal of Chromatography A","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.chroma.2026.467335","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"467335"}}