{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T06:00:22Z","timestamp":1780898422648,"version":"3.54.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"DOI":"10.1007\/s10822-026-00854-x","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:43:01Z","timestamp":1780897381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8468-0784","authenticated-orcid":false,"given":"Arkaprava","family":"Banerjee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6809-7633","authenticated-orcid":false,"given":"Vinay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1596-8900","authenticated-orcid":false,"given":"Shubha","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2211-4795","authenticated-orcid":false,"given":"Prodipta","family":"Bhattacharyya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4796-3915","authenticated-orcid":false,"given":"Probir Kumar","family":"Ojha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3780-3466","authenticated-orcid":false,"given":"Indrasis","family":"Dasgupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3367-578X","authenticated-orcid":false,"given":"Shovanlal","family":"Gayen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6344-168X","authenticated-orcid":false,"given":"Yogendra","family":"Chandra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2357-3234","authenticated-orcid":false,"given":"Partha Pratim","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9073-8437","authenticated-orcid":false,"given":"Siyun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1603-6825","authenticated-orcid":false,"given":"Lu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9411-2091","authenticated-orcid":false,"given":"Supratik","family":"Kar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1500-7040","authenticated-orcid":false,"given":"Jyotsna","family":"Bhat-Ambure","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7244-7117","authenticated-orcid":false,"given":"Pravin","family":"Ambure","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4486-8074","authenticated-orcid":false,"given":"Kunal","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"854_CR1","doi-asserted-by":"publisher","first-page":"20859","DOI":"10.1021\/acs.jmedchem.5c00287","volume":"68","author":"M Ishikawa","year":"2025","unstructured":"Ishikawa M, Tomoshinge S, Sato S (2025) Challenges of aufheben to promote druglikeness: chemical modification strategies to improve aqueous solubility and permeability. J Med Chem 68:20859\u201320902. https:\/\/doi.org\/10.1021\/acs.jmedchem.5c00287","journal-title":"J Med Chem"},{"key":"854_CR2","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S0045-6535(96)00377-3","volume":"34","author":"P Ruelle","year":"1997","unstructured":"Ruelle P, Kesselring UW (1997) Aqueous solubility prediction of environmentally important chemicals from the mobile order thermodynamics. Chemosphere 34:275\u2013298. https:\/\/doi.org\/10.1016\/S0045-6535(96)00377-3","journal-title":"Chemosphere"},{"key":"854_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmgm.2021.107901","volume":"106","author":"N Meftahi","year":"2021","unstructured":"Meftahi N, Walker ML, Smith BJ (2021) Predicting aqueous solubility by QSPR modeling. J Mol Graph Model 106:107901. https:\/\/doi.org\/10.1016\/j.jmgm.2021.107901","journal-title":"J Mol Graph Model"},{"key":"854_CR4","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1021\/ja01062a035","volume":"86","author":"C Hansch","year":"1964","unstructured":"Hansch C, Fujita T (1964) p-\u03c3-\u03c0 Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616\u20131626. https:\/\/doi.org\/10.1021\/ja01062a035","journal-title":"J Am Chem Soc"},{"key":"854_CR5","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1007\/s11030-024-11056-8","volume":"29","author":"A Banerjee","year":"2025","unstructured":"Banerjee A, Roy K, Gramatica P (2025) A bibliometric analysis of the Cheminformatics\/QSAR literature (2000\u20132023) for predictive modeling in data science using the SCOPUS database. Mol Divers 29:3703\u20133715. https:\/\/doi.org\/10.1007\/s11030-024-11056-8","journal-title":"Mol Divers"},{"key":"854_CR6","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1039\/D4EM00173G","volume":"26","author":"A Banerjee","year":"2024","unstructured":"Banerjee A, Roy K (2024) ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data. Environ Sci Process Impacts 26:991\u20131007. https:\/\/doi.org\/10.1039\/D4EM00173G","journal-title":"Environ Sci Process Impacts"},{"key":"854_CR7","doi-asserted-by":"publisher","first-page":"8435","DOI":"10.1039\/C7NR02211E","volume":"9","author":"A Gajewicz","year":"2017","unstructured":"Gajewicz A (2017) What if the number of nanotoxicity data is too small for developing predictive nano-QSAR models? An alternative read-across based approach for filling data gaps. Nanoscale 9:8435\u20138448. https:\/\/doi.org\/10.1039\/C7NR02211E","journal-title":"Nanoscale"},{"key":"854_CR8","doi-asserted-by":"publisher","unstructured":"Manganelli S, Benfenati E (2016) Use of read-across tools. In: Benfenati E (ed) In silico methods for predicting drug toxicity. Methods in molecular biology, vol 1425. Humana Press, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4939-3609-0_13","DOI":"10.1007\/978-1-4939-3609-0_13"},{"key":"854_CR9","doi-asserted-by":"crossref","unstructured":"Roy K, Banerjee A (2025) Activity cliffs: where QSAR predictions fail. Springer Cham. https:\/\/link.springer.com\/book\/9783032100801","DOI":"10.1007\/978-3-032-10081-8"},{"key":"854_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-52057-0","author":"K Roy","year":"2024","unstructured":"Roy K, Banerjee A (2024) q-RASAR: A path to predictive cheminformatics. Springer Cham. https:\/\/doi.org\/10.1007\/978-3-031-52057-0","journal-title":"Springer Cham"},{"key":"854_CR11","doi-asserted-by":"publisher","first-page":"2847","DOI":"10.1007\/s11030-022-10478-6","volume":"26","author":"A Banerjee","year":"2022","unstructured":"Banerjee A, Roy K (2022) First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability. Mol Divers 26:2847\u20132862. https:\/\/doi.org\/10.1007\/s11030-022-10478-6","journal-title":"Mol Divers"},{"key":"854_CR12","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1021\/acs.chemrestox.3c00155","volume":"36","author":"A Banerjee","year":"2023","unstructured":"Banerjee A, Roy K (2023) Prediction-inspired intelligent training for the development of classification read-across structure\u2013activity relationship (c-RASAR) models for organic skin sensitizers: assessment of classification error rate from novel similarity coefficients. Chem Res Toxicol 36:1518\u20131531. https:\/\/doi.org\/10.1021\/acs.chemrestox.3c00155","journal-title":"Chem Res Toxicol"},{"key":"854_CR13","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1039\/D5EM00068H","volume":"27","author":"A Banerjee","year":"2025","unstructured":"Banerjee A, Roy K (2025) The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints. Environ Sci Process Impacts 27:1229\u20131243. https:\/\/doi.org\/10.1039\/D5EM00068H","journal-title":"Environ Sci Process Impacts"},{"key":"854_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2025.139302","volume":"496","author":"A Banerjee","year":"2025","unstructured":"Banerjee A, Roy K (2025) A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression. J Hazard Mater 496:139302. https:\/\/doi.org\/10.1016\/j.jhazmat.2025.139302","journal-title":"J Hazard Mater"},{"key":"854_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2023.102527","volume":"79","author":"F Grisoni","year":"2023","unstructured":"Grisoni F (2023) Chemical language models for de novo drug design: challenges and opportunities. Curr Opin Struc Biol 79:102527. https:\/\/doi.org\/10.1016\/j.sbi.2023.102527","journal-title":"Curr Opin Struc Biol"},{"key":"854_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.70057","volume":"15","author":"A Banerjee","year":"2025","unstructured":"Banerjee A (2025) From feature\u2010based chemical similarity to chemical language models\u2014a paradigm shift in computer\u2010aided molecular design and property predictions. WIREs Comput Mol Sci 15:e70057. https:\/\/doi.org\/10.1002\/wcms.70057","journal-title":"WIREs Comput Mol Sci"},{"key":"854_CR17","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/S0166-1280(02)00616-4","volume":"622","author":"MTD Cronin","year":"2003","unstructured":"Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struc THEOCHEM 622:39\u201351. https:\/\/doi.org\/10.1016\/S0166-1280(02)00616-4","journal-title":"J Mol Struc THEOCHEM"},{"key":"854_CR18","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1080\/1062936x.2016.1178171","volume":"27","author":"E Benfenati","year":"2016","unstructured":"Benfenati E (2016) Results of a round-robin exercise on read-across. SAR QSAR Environ Res 27:371\u2013384. https:\/\/doi.org\/10.1080\/1062936x.2016.1178171","journal-title":"SAR QSAR Environ Res"},{"key":"854_CR19","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1021\/acs.chemrestox.4c00192","volume":"37","author":"IV Tetko","year":"2024","unstructured":"Tetko IV (2024) Tox24 challenge. Chem Res Toxicol 37:825\u2013826. https:\/\/doi.org\/10.1021\/acs.chemrestox.4c00192","journal-title":"Chem Res Toxicol"},{"key":"854_CR20","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.3762\/bjnano.15.121","volume":"15","author":"D-D Varsou","year":"2024","unstructured":"Varsou D-D (2024) The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential. Beilstein J Nanotechnol 15:1536\u20131553. https:\/\/doi.org\/10.3762\/bjnano.15.121","journal-title":"Beilstein J Nanotechnol"},{"issue":"1","key":"854_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-023-00752-6","volume":"15","author":"A Tayyebi","year":"2023","unstructured":"Tayyebi A (2023) Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models. J Cheminformatics 15(1):99. https:\/\/doi.org\/10.1186\/s13321-023-00752-6","journal-title":"J Cheminformatics"},{"key":"854_CR22","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273\u20131280. https:\/\/doi.org\/10.1021\/ci010132r","journal-title":"J Chem Inf Comput Sci"},{"key":"854_CR23","doi-asserted-by":"publisher","first-page":"2386","DOI":"10.1002\/jcc.21820","volume":"32","author":"PP Roy","year":"2011","unstructured":"Roy PP, Kovarich S, Gramatica P (2011) QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. J Comput Chem 32:2386\u20132396. https:\/\/doi.org\/10.1002\/jcc.21820","journal-title":"J Comput Chem"},{"key":"854_CR24","doi-asserted-by":"publisher","unstructured":"Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, London. https:\/\/doi.org\/10.1016\/C2014-0-00286-9","DOI":"10.1016\/C2014-0-00286-9"},{"key":"854_CR25","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","volume":"36","author":"H-S Park","year":"2009","unstructured":"Park H-S, Jun C-H (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336\u20133341. https:\/\/doi.org\/10.1016\/j.eswa.2008.01.039","journal-title":"Expert Syst Appl"},{"key":"854_CR26","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1021\/ci00020a020","volume":"34","author":"D Rogers","year":"1994","unstructured":"Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34:854\u2013866. https:\/\/doi.org\/10.1021\/ci00020a020","journal-title":"J Chem Inf Comput Sci"},{"key":"854_CR27","volume-title":"Statistical Methods","author":"GW Snedecor","year":"1989","unstructured":"Snedecor GW, Cochran WG (1989) Statistical Methods, 8th edn. Wiley-Blackwell","edition":"8"},{"key":"854_CR28","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1021\/acs.chemrestox.5c00273","volume":"38","author":"SA Eytcheson","year":"2025","unstructured":"Eytcheson SA, Tetko IV (2025) Which modern AI methods provide accurate predictions of toxicological end points? Analysis of tox24 challenge results. Chem Res Toxicol 38:1443\u20131451. https:\/\/doi.org\/10.1021\/acs.chemrestox.5c00273","journal-title":"Chem Res Toxicol"},{"key":"854_CR29","doi-asserted-by":"publisher","unstructured":"Mauri A (2020) alvaDesc: a tool to calculate and analyze molecular descriptors and fingerprints. In: Roy K (ed) Ecotoxicological QSARs. Methods in pharmacology and toxicology. Humana, New York, NY. https:\/\/doi.org\/10.1007\/978-1-0716-0150-1_32","DOI":"10.1007\/978-1-0716-0150-1_32"},{"key":"854_CR30","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/wics.113","volume":"2","author":"A Alin","year":"2010","unstructured":"Alin A (2010) Minitab. WIREs. Comput Stat 2:723\u2013727. https:\/\/doi.org\/10.1002\/wics.113","journal-title":"Comput Stat"},{"key":"854_CR31","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1039\/D1EN00725D","volume":"9","author":"M Chatterjee","year":"2022","unstructured":"Chatterjee M, Banerjee A, De P, Gajewicz-Skretna A, Roy K (2022) A novel quantitative read-across tool designed purposefully to fill the existing gaps in nanosafety data. Environ Sci Nano 9:189\u2013203. https:\/\/doi.org\/10.1039\/D1EN00725D","journal-title":"Environ Sci Nano"},{"key":"854_CR32","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10822-011-9440-2","volume":"25","author":"I Sushko","year":"2011","unstructured":"Sushko I, Novotarskyi S, K\u00f6rner R, Pandey AK, Rupp M, Teetz W, Brandmaier S, Abdelaziz A, Prokopenko VV, Tanchuk VY, Todeschini R et al (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput-Aided Mol Des 25:533\u2013554. https:\/\/doi.org\/10.1007\/s10822-011-9440-2","journal-title":"J Comput-Aided Mol Des"},{"key":"854_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chemolab.2015.07.007","volume":"147","author":"P Ambure","year":"2015","unstructured":"Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K (2015) \u201cNanoBRIDGES\u201d software: open access tools to perform QSAR and nano-QSAR modeling. Chemom Intell Lab Syst 147:1\u201313. https:\/\/doi.org\/10.1016\/j.chemolab.2015.07.007","journal-title":"Chemom Intell Lab Syst"},{"key":"854_CR34","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1021\/ci300338w","volume":"52","author":"TM Martin","year":"2012","unstructured":"Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A (2012) Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Model 52:2570\u20132578. https:\/\/doi.org\/10.1021\/ci300338w","journal-title":"J Chem Inf Model"},{"key":"854_CR35","doi-asserted-by":"publisher","first-page":"450","DOI":"10.2174\/138620711795767893","volume":"14","author":"K Roy","year":"2011","unstructured":"Roy K, Mitra I (2011) On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Through Screen 14:450\u2013474. https:\/\/doi.org\/10.2174\/138620711795767893","journal-title":"Comb Chem High Through Screen"},{"key":"854_CR36","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1002\/jcc.23361","volume":"34","author":"P Gramatica","year":"2013","unstructured":"Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34:2121\u20132132. https:\/\/doi.org\/10.1002\/jcc.23361","journal-title":"J Comput Chem"},{"key":"854_CR37","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1002\/minf.201200075","volume":"31","author":"P Gramatica","year":"2012","unstructured":"Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E (2012) QSAR modeling is not \u201cpush a button and find a correlation\u201d: a case study of toxicity of (benzo-)triazoles on algae. Mol Inform 31:817\u2013835. https:\/\/doi.org\/10.1002\/minf.201200075","journal-title":"Mol Inform"},{"key":"854_CR38","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1002\/jcc.21707","volume":"32","author":"CW Yap","year":"2011","unstructured":"Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466\u20131474. https:\/\/doi.org\/10.1002\/jcc.21707","journal-title":"J Comput Chem"},{"key":"854_CR39","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"854_CR40","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ et al (2020) Array programming with NumPy. Nature 585:357\u2013362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"854_CR41","doi-asserted-by":"crossref","unstructured":"McKinney W (2010) Data structures for statistical computing in Python. Proceedings of the 9th Python in science conference, (SciPy 2010). pp 51\u201356.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"854_CR42","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s00204-022-03252-y","volume":"96","author":"P De","year":"2022","unstructured":"De P (2022) Prediction reliability of QSAR models: an overview of various validation tools. Arch Toxicol 96:1279\u20131295. https:\/\/doi.org\/10.1007\/s00204-022-03252-y","journal-title":"Arch Toxicol"},{"key":"854_CR43","doi-asserted-by":"crossref","unstructured":"Russell I, Markov Z (2017) An introduction to the Weka data mining system. In: Proceedings of the 2017 ACM SIGCSE Technical symposium on computer science education. p 742","DOI":"10.1145\/3017680.3017821"},{"key":"854_CR44","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.chemolab.2015.04.013","volume":"145","author":"K Roy","year":"2015","unstructured":"Roy K, Kar S, Ambure PO (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22\u201329. https:\/\/doi.org\/10.1016\/j.chemolab.2015.04.013","journal-title":"Chemom Intell Lab Syst"},{"key":"854_CR45","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","volume":"63","author":"R Rodriguez-Perez","year":"2020","unstructured":"Rodriguez-Perez R (2020) Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values. J Med Chem 63:8761\u20138777. https:\/\/doi.org\/10.1021\/acs.jmedchem.9b01101","journal-title":"J Med Chem"},{"key":"854_CR46","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci034243x","volume":"44","author":"JS Delaney","year":"2004","unstructured":"Delaney JS (2004) ESOL: estimating aqueous solubility directly from molecular structure. J Chem Inf Comput Sci 44:1000\u20131005. https:\/\/doi.org\/10.1021\/ci034243x","journal-title":"J Chem Inf Comput Sci"},{"key":"854_CR47","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1021\/ci034134i","volume":"43","author":"TJ Hou","year":"2003","unstructured":"Hou TJ, Xu XJ (2003) ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J Chem Inf Comput Sci 43:2137\u20132152. https:\/\/doi.org\/10.1021\/ci034134i","journal-title":"J Chem Inf Comput Sci"},{"key":"854_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s10953-025-01508-6","author":"SY Ugurlu","year":"2025","unstructured":"Ugurlu SY (2025) Inter-Pol: an interpretable machine learning framework for solvent polarity prediction. J Solut Chem. https:\/\/doi.org\/10.1007\/s10953-025-01508-6","journal-title":"J Solut Chem"},{"key":"854_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/ph17020263","volume":"17","author":"M Mondal","year":"2024","unstructured":"Mondal M, Mandal SK (2024) Shaping the future of obesity treatment: in silico multi-modeling of IP6K1 inhibitors for obesity and metabolic dysfunction. Pharmaceuticals 17:263. https:\/\/doi.org\/10.3390\/ph17020263","journal-title":"Pharmaceuticals"},{"key":"854_CR50","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1039\/D3GC03109H","volume":"26","author":"Y Li","year":"2024","unstructured":"Li Y (2024) Ecotoxicological risk assessment of pesticides against different aquatic and terrestrial species: using mechanistic QSTR and iQSTTR modelling approaches to fill the toxicity data gap. Green Chem 26:839\u2013856. https:\/\/doi.org\/10.1039\/D3GC03109H","journal-title":"Green Chem"},{"key":"854_CR51","doi-asserted-by":"publisher","first-page":"21735","DOI":"10.1038\/s41598-022-25935-3","volume":"12","author":"L Pallante","year":"2022","unstructured":"Pallante L, Korfiati A, Androutsou L, Stojceski F, Bompotas A, Giannikos I, Raftopoulos C, Malavolta M, Grasso G, Mavroudi S, Kalogeras A, Martos V, Amoroso D, Piga D, Theofilatos K, Deriu MA (2022) Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci Rep 12:21735. https:\/\/doi.org\/10.1038\/s41598-022-25935-3","journal-title":"Sci Rep"},{"key":"854_CR52","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1021\/acs.chemrestox.2c00374","volume":"36","author":"A Banerjee","year":"2023","unstructured":"Banerjee A, Roy K (2023) on some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity end points. Chem Res Toxicol 36:446\u2013464. https:\/\/doi.org\/10.1021\/acs.chemrestox.2c00374","journal-title":"Chem Res Toxicol"},{"key":"854_CR53","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1007\/s10822-013-9680-4","volume":"27","author":"G Toth","year":"2013","unstructured":"Toth G, Bodai Z, Heberger K (2013) Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart. J Comput-Aided Mol Des 27:837\u2013844. https:\/\/doi.org\/10.1007\/s10822-013-9680-4","journal-title":"J Comput-Aided Mol Des"},{"key":"854_CR54","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1002\/1521-3838(200210)21:4<348::AID-QSAR348>3.0.CO;2-D","volume":"21","author":"H Kubinyi","year":"2002","unstructured":"Kubinyi H (2002) From narcosis to hyperspace: the history of QSAR. Quant Struc Act Rel 21:348\u2013356. https:\/\/doi.org\/10.1002\/1521-3838(200210)21:4%3c348::AID-QSAR348%3e3.0.CO;2-D","journal-title":"Quant Struc Act Rel"},{"key":"854_CR55","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1002\/qsar.200610151","volume":"26","author":"P Gramatica","year":"2007","unstructured":"Gramatica P (2007) Principles of QSAR model validation: internal and external. QSAR Comb Sci 26:694\u2013701. https:\/\/doi.org\/10.1002\/qsar.200610151","journal-title":"QSAR Comb Sci"},{"key":"854_CR56","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.trac.2009.09.009","volume":"29","author":"K Heberger","year":"2010","unstructured":"Heberger K (2010) Sum of ranking differences compares methods or models fairly. TrAC Trends Anal Chem 29:101\u2013109. https:\/\/doi.org\/10.1016\/j.trac.2009.09.009","journal-title":"TrAC Trends Anal Chem"},{"key":"854_CR57","doi-asserted-by":"publisher","first-page":"8363","DOI":"10.1007\/s00216-013-7206-5","volume":"405","author":"B Skrbic","year":"2013","unstructured":"Skrbic B, Heberger K, Durisic-Mladenovic N (2013) Comparison of multianalyte proficiency test results by sum of ranking differences, principal component analysis, and hierarchical cluster analysis. Anal Bioanal Chem 405:8363\u20138375. https:\/\/doi.org\/10.1007\/s00216-013-7206-5","journal-title":"Anal Bioanal Chem"},{"key":"854_CR58","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.chemolab.2013.06.007","volume":"127","author":"K Kollar-Hunek","year":"2013","unstructured":"Kollar-Hunek K, Heberger K (2013) Method and model comparison by sum of ranking differences in cases of repeated observations (ties). Chemom Intell Lab Syst 127:139\u2013146. https:\/\/doi.org\/10.1016\/j.chemolab.2013.06.007","journal-title":"Chemom Intell Lab Syst"},{"key":"854_CR59","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2307\/3001913","volume":"5","author":"JW Tukey","year":"1949","unstructured":"Tukey JW (1949) Comparing Individual Means in the Analysis of Variance. Biometrics 5:99\u2013114. https:\/\/doi.org\/10.2307\/3001913","journal-title":"Biometrics"},{"key":"854_CR60","doi-asserted-by":"publisher","unstructured":"Crespo M\u00e1rquez A (2022) The curse of dimensionality. In: Digital maintenance management. Springer series in reliability engineering. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-97660-6_7","DOI":"10.1007\/978-3-030-97660-6_7"},{"key":"854_CR61","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1021\/ci100176x","volume":"50","author":"D Fourches","year":"2010","unstructured":"Fourches D, Muratov E, Tropsha A (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 50:1189\u20131204. https:\/\/doi.org\/10.1021\/ci100176x","journal-title":"J Chem Inf Model"},{"key":"854_CR62","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chas.5c00166","author":"B Wang","year":"2025","unstructured":"Wang B, Li Y, Zheng J, Fan W, Wang Y, Ma F, Chen M, Dong Z (2025) Using stacking ensemble machine learning to estimate the human half-life and apparent volume of distribution: implications for human health risk assessment. ACS Chem Health Saf. https:\/\/doi.org\/10.1021\/acs.chas.5c00166","journal-title":"ACS Chem Health Saf"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00854-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-026-00854-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00854-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:43:04Z","timestamp":1780897384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-026-00854-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,8]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["854"],"URL":"https:\/\/doi.org\/10.1007\/s10822-026-00854-x","relation":{},"ISSN":["1573-4951"],"issn-type":[{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,8]]},"assertion":[{"value":"25 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"143"}}