{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:28:31Z","timestamp":1772166511722,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1815866"],"award-info":[{"award-number":["1815866"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006116","name":"Advanced Manufacturing Office","doi-asserted-by":"publisher","award":["DE\u2010EE0009507"],"award-info":[{"award-number":["DE\u2010EE0009507"]}],"id":[{"id":"10.13039\/100006116","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    MF-LOGP, a new method for determining a single component octanol\u2013water partition coefficients (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$LogP$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>LogP<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) is presented which uses molecular formula as the only input. Octanol\u2013water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$LogP$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>LogP<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$RMSE$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>RMSE<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.77\u2009\u00b1\u20090.007,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$MAE$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>MAE<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.52\u2009\u00b1\u20090.003, and\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$${R}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mrow>\n                              <mml:mi>R<\/mml:mi>\n                            <\/mml:mrow>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.83\u2009\u00b1\u20090.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$RMSE$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>RMSE<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.42\u20131.54,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$MAE$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>MAE<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.09\u20131.07, and\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$${R}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mrow>\n                              <mml:mi>R<\/mml:mi>\n                            <\/mml:mrow>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    = 0.32\u20130.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-022-00660-1","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T11:03:29Z","timestamp":1674126209000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Dimensionally reduced machine learning model for predicting single component octanol\u2013water partition coefficients"],"prefix":"10.1186","volume":"15","author":[{"given":"David H.","family":"Kenney","sequence":"first","affiliation":[]},{"given":"Randy C.","family":"Paffenroth","sequence":"additional","affiliation":[]},{"given":"Michael T.","family":"Timko","sequence":"additional","affiliation":[]},{"given":"Andrew R.","family":"Teixeira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"issue":"3","key":"660_CR1","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1063\/1.555833","volume":"18","author":"J Sangster","year":"1989","unstructured":"Sangster J (1989) Octanol-water partition coefficients of simple organic compounds. J Phys Chem Ref Data 18(3):1111\u201312227","journal-title":"J Phys Chem Ref Data"},{"issue":"4","key":"660_CR2","doi-asserted-by":"publisher","first-page":"405","DOI":"10.2174\/157340906778992346","volume":"2","author":"TM Cronin","year":"2006","unstructured":"Cronin TM (2006) the role of hydrophobicity in toxicity prediction. Curr Computer-Aided Drug Design. 2(4):405\u2013413","journal-title":"Curr Computer-Aided Drug Design."},{"issue":"4","key":"660_CR3","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1021\/es102769t","volume":"45","author":"H-M Shin","year":"2011","unstructured":"Shin H-M, Vieira VM, Ryan PB, Detwiler R, Sanders B, Steenland K, Bartell SM (2011) Environmental fate and transport modeling for perfluorooctanoic acid emitted from the washington works facility in West Virginia. Environ Sci Technol 45(4):1435\u20131442","journal-title":"Environ Sci Technol"},{"key":"660_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/b11864","volume-title":"Pesticides: evaluation of environmental pollution","author":"HS Rathore","year":"2012","unstructured":"Rathore HS, Nollet LML (2012) Pesticides: evaluation of environmental pollution. CRC Press, Boca Raton"},{"key":"660_CR5","volume-title":"Fate and transport of POPs in the aquatic environment: with focus on contaminated sediments. doctoral thesis, comprehensive summary","author":"S Josefsson","year":"2011","unstructured":"Josefsson S (2011) Fate and transport of POPs in the aquatic environment: with focus on contaminated sediments. doctoral thesis, comprehensive summary. Kemiska institutionen, Ume\u00e5"},{"issue":"6","key":"660_CR6","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1021\/ci700257y","volume":"47","author":"T Cheng","year":"2007","unstructured":"Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L (2007) Computation of octanol\u2212water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model 47(6):2140\u20132148","journal-title":"J Chem Inf Model"},{"issue":"1","key":"660_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","volume":"23","author":"CA Lipinski","year":"1997","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23(1):3\u201325","journal-title":"Adv Drug Deliv Rev"},{"key":"660_CR8","volume-title":"Thermodynamics and its applications","author":"JW Tester","year":"1997","unstructured":"Tester JW, Modell M (1997) Thermodynamics and its applications. Prentice Hall PTR, Hoboken"},{"issue":"6","key":"660_CR9","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1021\/cr60274a001","volume":"71","author":"A Leo","year":"1971","unstructured":"Leo A, Hansch C, Elkins D (1971) Partition Coefficients and Their Uses. Chem Rev 71(6):525\u2013616","journal-title":"Chem Rev"},{"key":"660_CR10","unstructured":"OECD: Test No. 107: Partition Coefficient (n-octanol\/water): Shake Flask Method; 1995."},{"key":"660_CR11","unstructured":"OECD: Test No. 123: Partition Coefficient (1-Octanol\/Water): Slow-Stirring Method; 2006."},{"issue":"4","key":"660_CR12","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.1021\/acs.jced.9b01129","volume":"65","author":"CD Sch\u00f6nsee","year":"2020","unstructured":"Sch\u00f6nsee CD, Bucheli TD (2020) Experimental determination of octanol-water partition coefficients of selected natural toxins. J Chem Eng Data 65(4):1946\u20131953","journal-title":"J Chem Eng Data"},{"issue":"9","key":"660_CR13","doi-asserted-by":"publisher","first-page":"6244","DOI":"10.1021\/acsomega.7b01102","volume":"2","author":"H Cumming","year":"2017","unstructured":"Cumming H, R\u00fccker C (2017) Octanol-Water partition coefficient measurement by a simple 1H NMR Method. ACS Omega 2(9):6244\u20136249","journal-title":"ACS Omega"},{"issue":"21","key":"660_CR14","doi-asserted-by":"publisher","first-page":"11130","DOI":"10.1021\/acs.analchem.5b03311","volume":"87","author":"M Abolhasani","year":"2015","unstructured":"Abolhasani M, Coley CW, Jensen KF (2015) Multiphase oscillatory flow strategy for in situ measurement and screening of partition coefficients. Anal Chem 87(21):11130\u201311136","journal-title":"Anal Chem"},{"issue":"1","key":"660_CR15","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.theochem.2005.08.020","volume":"755","author":"W Zhou","year":"2005","unstructured":"Zhou W, Zhai Z, Wang Z, Wang L (2005) Estimation of n-octanol\/water partition coefficients (Kow) of all PCB congeners by density functional theory. J Mol Struct (Thoechem) 755(1):137\u2013145","journal-title":"J Mol Struct (Thoechem)"},{"issue":"3","key":"660_CR16","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1147\/rd.33.0210","volume":"3","author":"AL Samuel","year":"1959","unstructured":"Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210\u2013229","journal-title":"IBM J Res Dev"},{"key":"660_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116659","volume":"197","author":"MM Kumbure","year":"2022","unstructured":"Kumbure MM, Lohrmann C, Luukka P, Porras J (2022) Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst Appl 197:116659","journal-title":"Expert Syst Appl"},{"issue":"1","key":"660_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","volume":"23","author":"I Kononenko","year":"2001","unstructured":"Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23(1):89\u2013109","journal-title":"Artif Intell Med"},{"issue":"40","key":"660_CR19","first-page":"100395","volume":"2021","author":"TK Balaji","year":"2021","unstructured":"Balaji TK, Annavarapu CSR, Bablani A (2021) Machine learning algorithms for social media analysis: A survey. Computer Science Review. 2021(40):100395","journal-title":"Computer Science Review."},{"key":"660_CR20","volume-title":"Exploring QSAR: fundamentals and applications in chemistry and biology","author":"C Hansch","year":"1995","unstructured":"Hansch C, Leo A, Hoekman DH (1995) Exploring QSAR: fundamentals and applications in chemistry and biology. American Chemical Society, New York"},{"issue":"3","key":"660_CR21","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1021\/ci60019a013","volume":"19","author":"JT Chou","year":"1979","unstructured":"Chou JT, Jurs PC (1979) Computer-assisted computation of partition coefficients from molecular structures using fragment constants. J Chem Inf Comput Sci 19(3):172\u2013178","journal-title":"J Chem Inf Comput Sci"},{"issue":"3","key":"660_CR22","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1002\/jps.21494","volume":"98","author":"R Mannhold","year":"2009","unstructured":"Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) Calculation of molecular lipophilicity: state-of-the-art and comparison of LogP Methods on more than 96,000 Compounds. J Pharm Sci 98(3):861\u2013893","journal-title":"J Pharm Sci"},{"issue":"12","key":"660_CR23","doi-asserted-by":"publisher","first-page":"3284","DOI":"10.1021\/ci500467k","volume":"54","author":"A Daina","year":"2014","unstructured":"Daina A, Michielin O, Zoete V (2014) iLOGP: a simple, robust, and efficient description of n-octanol\/water partition coefficient for drug design using the GB\/SA Approach. J Chem Inf Model 54(12):3284\u20133301","journal-title":"J Chem Inf Model"},{"issue":"5","key":"660_CR24","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1021\/ci010368v","volume":"41","author":"IV Tetko","year":"2001","unstructured":"Tetko IV, Tanchuk VY, Villa AEP (2001) Prediction of n-Octanol\/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. J Chem Inf Comput Sci 41(5):1407\u20131421","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"660_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1023\/A:1008763405023","volume":"19","author":"R Wang","year":"2000","unstructured":"Wang R, Gao Y, Lai L (2000) Calculating partition coefficient by atom-additive method. Perspect Drug Discovery Des 19(1):47\u201366","journal-title":"Perspect Drug Discovery Des"},{"issue":"5","key":"660_CR26","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1021\/ci990307l","volume":"39","author":"SA Wildman","year":"1999","unstructured":"Wildman SA, Crippen GM (1999) Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci 39(5):868\u2013873","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"660_CR27","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1023\/A:1008715521862","volume":"19","author":"WM Meylan","year":"2000","unstructured":"Meylan WM, Howard PH (2000) Estimating log P with atom\/fragments and water solubility with log P. Perspect Drug Discovery Des 19(1):67\u201384","journal-title":"Perspect Drug Discovery Des"},{"key":"660_CR28","doi-asserted-by":"publisher","first-page":"42717","DOI":"10.1038\/srep42717","volume":"7","author":"A Daina","year":"2017","unstructured":"Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717\u201342717","journal-title":"Sci Rep"},{"key":"660_CR29","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-319-56850-8_2","volume-title":"Advances in QSAR Modeling: Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences","author":"JC Dearden","year":"2017","unstructured":"Dearden JC (2017) The Use of Topological Indices in QSAR and QSPR Modeling. In: Roy K (ed) Advances in QSAR Modeling: Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences. Springer International Publishing, Cham, pp 57\u201388"},{"issue":"4","key":"660_CR30","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1023\/A:1011107422318","volume":"15","author":"R Mannhold","year":"2001","unstructured":"Mannhold R, van de Waterbeemd H (2001) Substructure and whole molecule approaches for calculating log P. J Comput Aided Mol Des 15(4):337\u2013354","journal-title":"J Comput Aided Mol Des"},{"issue":"4","key":"660_CR31","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1021\/cr00020a001","volume":"93","author":"AJ Leo","year":"1993","unstructured":"Leo AJ (1993) Calculating log Poct from structures. Chem Rev 93(4):1281\u20131306","journal-title":"Chem Rev"},{"issue":"13","key":"660_CR32","doi-asserted-by":"publisher","first-page":"5283","DOI":"10.1021\/ac2006735","volume":"83","author":"JH Lee","year":"2011","unstructured":"Lee JH, Choi HS, Nasr KA, Ha M, Kim Y, Frangioni JV (2011) High-throughput small molecule identification using MALDI-TOF and a Nanolayered Substrate. Anal Chem 83(13):5283\u20135289","journal-title":"Anal Chem"},{"issue":"24","key":"660_CR33","doi-asserted-by":"publisher","first-page":"9941","DOI":"10.1021\/ac901594f","volume":"81","author":"FA Fernandez-Lima","year":"2009","unstructured":"Fernandez-Lima FA, Becker C, McKenna AM, Rodgers RP, Marshall AG, Russell DH (2009) Petroleum crude oil characterization by IMS-MS and FTICR MS. Anal Chem 81(24):9941\u20139947","journal-title":"Anal Chem"},{"issue":"10","key":"660_CR34","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1124\/dmd.120.090498","volume":"48","author":"K Utsey","year":"2020","unstructured":"Utsey K, Gastonguay MS, Russell S, Freling R, Riggs MM, Elmokadem A (2020) Quantification of the impact of partition coefficient prediction methods on physiologically based pharmacokinetic model output using a standardized tissue composition. Drug Metab Dispos 48(10):903","journal-title":"Drug Metab Dispos"},{"issue":"4","key":"660_CR35","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s10928-012-9252-6","volume":"39","author":"MD Thompson","year":"2012","unstructured":"Thompson MD, Beard DA, Wu F (2012) Use of partition coefficients in flow-limited physiologically-based pharmacokinetic modeling. J Pharmacokinet Pharmacodyn 39(4):313\u2013327","journal-title":"J Pharmacokinet Pharmacodyn"},{"issue":"11","key":"660_CR36","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1080\/1062936X.2016.1253611","volume":"27","author":"K Mansouri","year":"2016","unstructured":"Mansouri K, Grulke CM, Richard AM, Judson RS, Williams AJ (2016) An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling. SAR QSAR Environ Res 27(11):911\u2013937","journal-title":"SAR QSAR Environ Res"},{"issue":"D1","key":"660_CR37","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2015","unstructured":"Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA et al (2015) PubChem substance and compound databases. Nucleic Acids Res 44(D1):D1202\u2013D1213","journal-title":"Nucleic Acids Res"},{"key":"660_CR38","unstructured":"Nicklaus M, Sitzmann M: CADD Group Chemoinformatics Tools and User Services. Computer-Aided Drug Design (CADD) Group of the Chemical Biology Laboratory (CBL) 2010."},{"key":"660_CR39","unstructured":"Swain M: PubChemPy Documentation., v1.0.4; 2014."},{"key":"660_CR40","unstructured":"Swain M: CIRpy Documentation., v1.0.2; 2015."},{"key":"660_CR41","unstructured":"Boyer G: chemparse Documentation, v0.1.2; 2022."},{"key":"660_CR42","unstructured":"Landrum G: RDKit Documentation, v3.1; 2022."},{"key":"660_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York"},{"key":"660_CR44","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in Python. J Machine Learning Res 12:2825\u20132830","journal-title":"J Machine Learning Res"},{"key":"660_CR45","volume-title":"Prediction and Discovery: AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25\u201329, 2006, Snowbird","author":"Verducci JS, Shen X, Society AM, Lafferty J","year":"2007","unstructured":"Verducci JS, Shen X, Society AM, Lafferty J (2007) Prediction and Discovery: AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning: Prediction and Discovery, June 25\u201329, 2006, Snowbird. American Mathematical Society, Utah"},{"issue":"4","key":"660_CR46","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1109\/TPWRD.2020.3021702","volume":"36","author":"AI Khalyasmaa","year":"2021","unstructured":"Khalyasmaa AI, Senyuk MD, Eroshenko SA (2021) Analysis of the state of high-voltage current transformers based on gradient boosting on decision trees. IEEE Trans Power Delivery 36(4):2154\u20132163","journal-title":"IEEE Trans Power Delivery"},{"issue":"26","key":"660_CR47","doi-asserted-by":"publisher","first-page":"13262","DOI":"10.1039\/C4CP01280A","volume":"16","author":"N Sagawa","year":"2014","unstructured":"Sagawa N, Shikata T (2014) Are all polar molecules hydrophilic? Hydration numbers of nitro compounds and nitriles in aqueous solution. Phys Chem Chem Phys 16(26):13262\u201313270","journal-title":"Phys Chem Chem Phys"},{"issue":"1","key":"660_CR48","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s42004-021-00528-9","volume":"4","author":"N Ulrich","year":"2021","unstructured":"Ulrich N, Goss K-U, Ebert A (2021) Exploring the octanol\u2013water partition coefficient dataset using deep learning techniques and data augmentation. Commun Chemis 4(1):90","journal-title":"Commun Chemis"},{"issue":"1","key":"660_CR49","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13321-018-0316-5","volume":"10","author":"J Plante","year":"2018","unstructured":"Plante J, Werner S (2018) JPlogP: an improved logP predictor trained using predicted data. J Cheminform 10(1):61","journal-title":"J Cheminform"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00660-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-022-00660-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00660-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T12:13:14Z","timestamp":1674130394000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-022-00660-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["660"],"URL":"https:\/\/doi.org\/10.1186\/s13321-022-00660-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2106077\/v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]},"assertion":[{"value":"26 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2023","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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9"}}