{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:03:38Z","timestamp":1780441418666,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"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 Mol Model"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00894-026-06762-z","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:56:08Z","timestamp":1778644568000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-accuracy QSPR models for azeotropic property prediction of binary aromatic hydrocarbon mixtures: a genetic function approximation approach"],"prefix":"10.1007","volume":"32","author":[{"given":"Liping","family":"Lv","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaogang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huisheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deng","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"6762_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.compchemeng.2017.02.007","volume":"100","author":"L Zhao","year":"2017","unstructured":"Zhao L, Lyu XY, Wang WC, Shan J, Qiu T (2017) Comparison of heterogeneous azeotropic distillation and extractive distillation methods for ternary azeotrope ethanol\/toluene\/water separation. Comput Chem Eng 100:27\u201337. https:\/\/doi.org\/10.1016\/j.compchemeng.2017.02.007","journal-title":"Comput Chem Eng"},{"key":"6762_CR2","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.seppur.2016.10.020","volume":"174","author":"WL Luyben","year":"2017","unstructured":"Luyben WL (2017) Control of a triple-column pressure-swing distillation process. Sep Purif Technol 174:232\u201344. https:\/\/doi.org\/10.1016\/j.seppur.2016.10.020","journal-title":"Sep Purif Technol"},{"key":"6762_CR3","doi-asserted-by":"publisher","first-page":"2150","DOI":"10.1021\/acs.iecr.6b04118","volume":"56","author":"ZY Zhu","year":"2017","unstructured":"Zhu ZY, Xu DF, Jia H, Zhao YT, Wang YL (2017) Heat integration and control of a triple-column pressure-swing distillation process. Ind Eng Chem Res 56:2150\u20132167. https:\/\/doi.org\/10.1021\/acs.iecr.6b04118","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR4","unstructured":"Zhang H, Zhang Q et al (2014) Economic optimum design of the pressure-swing distillation for toluene-ethanol separation. Energy Chem Ind (China) 35:31\u20135.\u00a0https:\/\/d.wanfangdata.com.cn\/periodical\/hxgyygcjs201406009"},{"issue":"5","key":"6762_CR5","doi-asserted-by":"publisher","first-page":"71","DOI":"10.13353\/j.issn.1004.9533.2013.05.002","volume":"30","author":"Z Luan","year":"2013","unstructured":"Luan Z, Jinwei Z, Hongji Z (2013) Separa\u2043 tion of methano1\u2043 toluene by extractive distillation. Chemcial Industry and Engineering 30(5):71\u20135. https:\/\/doi.org\/10.13353\/j.issn.1004.9533.2013.05.002","journal-title":"Chemcial Industry and Engineering"},{"key":"6762_CR6","doi-asserted-by":"publisher","unstructured":"Zhu Z, Li X, et al (2016) The optimum process of pressure swing-top MVRHP distillation for separating toluene and N-butyl alcohol. Contemp Chem Ind 45:162\u20135. https:\/\/doi.org\/10.13840\/j.cnki.cn21-1457\/tq.2016.01.051","DOI":"10.13840\/j.cnki.cn21-1457\/tq.2016.01.051"},{"key":"6762_CR7","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.molliq.2013.09.037","volume":"188","author":"V Zare-Shahabadi","year":"2013","unstructured":"Zare-Shahabadi V, Lotfizadeh M, Gandomani ARA, Papari MM (2013) Determination of boiling points of azeotropic mixtures using quantitative structure\u2013property relationship (QSPR) strategy. J Mol Liq 188:222\u2013229. https:\/\/doi.org\/10.1016\/j.molliq.2013.09.037","journal-title":"J Mol Liq"},{"key":"6762_CR8","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.cjche.2017.10.020","volume":"26","author":"Z Xing","year":"2018","unstructured":"Xing Z, Gao Y, Ding H, Wang X, Li L, Zhou H (2018) Isobaric vapor\u2013liquid equilibrium for ternary system of ethanol, ethyl propionate and para-xylene at 101.3 kPa. Chin J Chem Eng 26:560\u2013565. https:\/\/doi.org\/10.1016\/j.cjche.2017.10.020","journal-title":"Chin J Chem Eng"},{"key":"6762_CR9","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1002\/jctb.4452","volume":"90","author":"Y Wang","year":"2015","unstructured":"Wang Y, Cui P, Ma Y, Zhang Z (2015) Extractive distillation and pressure-swing distillation for THF\/ethanol separation. J Chem Technol Biotechnol 90:1463\u20131472. https:\/\/doi.org\/10.1002\/jctb.4452","journal-title":"J Chem Technol Biotechnol"},{"key":"6762_CR10","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.5b03666","author":"Y Wang","year":"2015","unstructured":"Wang Y, Liang S, Bu G, Liu W, Zhang Z, Zhu Z (2015) Effect of solvent flow rates on controllability of extractive distillation for separating binary azeotropic mixture. Ind Eng Chem Res. https:\/\/doi.org\/10.1021\/acs.iecr.5b03666","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR11","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1021\/ie901157g","volume":"49","author":"K-Y Hsu","year":"2010","unstructured":"Hsu K-Y, Hsiao Y-C, Chien I-L (2010) Design and control of dimethyl carbonate\/methanol separation via extractive distillation in the dimethyl carbonate reactive-distillation process. Ind Eng Chem Res 49:735\u2013749. https:\/\/doi.org\/10.1021\/ie901157g","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR12","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.jct.2017.11.009","volume":"118","author":"J Wu","year":"2018","unstructured":"Wu J, Xu D, Shi P, Gao J, Zhang L, Ma Y et al (2018) Separation of azeotrope (allyl alcohol\u202f+\u202fwater): isobaric vapour-liquid phase equilibrium measurements and extractive distillation. J Chem Thermodyn 118:139\u201346. https:\/\/doi.org\/10.1016\/j.jct.2017.11.009","journal-title":"J Chem Thermodyn"},{"key":"6762_CR13","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.5b00572","author":"W Li","year":"2015","unstructured":"Li W, Zhong L, He Y, Meng J, Yao F, Guo Y et al (2015) Multiple steady-states analysis and unstable operating point stabilization in homogeneous azeotropic distillation with intermediate entrainer. Ind Eng Chem Res. https:\/\/doi.org\/10.1021\/acs.iecr.5b00572","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR14","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.compchemeng.2016.03.027","volume":"89","author":"MB Franke","year":"2016","unstructured":"Franke MB (2016) MINLP optimization of a heterogeneous azeotropic distillation process: separation of ethanol and water with cyclohexane as an entrainer. Comput Chem Eng 89:204\u2013221. https:\/\/doi.org\/10.1016\/j.compchemeng.2016.03.027","journal-title":"Comput Chem Eng"},{"key":"6762_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cherd.2015.03.022","author":"Q-K Le","year":"2015","unstructured":"Le Q-K, Halvorsen IJ, Pajalic O, Skogestad S (2015) Dividing wall columns for heterogeneous azeotropic distillation. Chem Eng Res Des. https:\/\/doi.org\/10.1016\/j.cherd.2015.03.022","journal-title":"Chem Eng Res Des"},{"key":"6762_CR16","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.cep.2017.10.012","volume":"122","author":"X Gao","year":"2017","unstructured":"Gao X, Zhu B, Ma J, Yang D (2017) A combination of pressure-swing and extractive distillation for separating complex binary azeotropic system. Chem Eng Process Process Intensif 122:269\u2013276. https:\/\/doi.org\/10.1016\/j.cep.2017.10.012","journal-title":"Chem Eng Process Process Intensif"},{"key":"6762_CR17","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.compchemeng.2015.02.016","volume":"76","author":"Z Zhu","year":"2015","unstructured":"Zhu Z, Wang L, Ma Y, Wang W, Wang Y (2015) Separating an azeotropic mixture of toluene and ethanol via heat integration pressure swing distillation. Comput Chem Eng 76:137\u2013149. https:\/\/doi.org\/10.1016\/j.compchemeng.2015.02.016","journal-title":"Comput Chem Eng"},{"key":"6762_CR18","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.cep.2016.10.011","volume":"110","author":"Y Cao","year":"2016","unstructured":"Cao Y, Li M, Wang Y, Zhao T, Li X, Zhu Z (2016) Effect of feed temperature on economics and controllability of pressure-swing distillation for separating binary azeotrope. Chem Eng Process Process Intensif 110:160\u201371. https:\/\/doi.org\/10.1016\/j.cep.2016.10.011","journal-title":"Chem Eng Process Process Intensif"},{"key":"6762_CR19","doi-asserted-by":"publisher","first-page":"14162","DOI":"10.1021\/ie2018614","volume":"50","author":"VP Solov\u2019ev","year":"2011","unstructured":"Solov\u2019ev VP, Oprisiu I, Marcou G, Varnek A (2011) Quantitative structure\u2013property relationship (QSPR) modeling of normal boiling point temperature and composition of binary azeotropes. Ind Eng Chem Res 50:14162\u201314167. https:\/\/doi.org\/10.1021\/ie2018614","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR20","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.chemolab.2016.10.009","volume":"159","author":"K Roy","year":"2016","unstructured":"Roy K, Ambure P (2016) The \u201cdouble cross-validation\u201d software tool for MLR QSAR model development. Chemom Intell Lab Syst 159:108\u2013126. https:\/\/doi.org\/10.1016\/j.chemolab.2016.10.009","journal-title":"Chemom Intell Lab Syst"},{"key":"6762_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1021\/acs.iecr.6b04347","volume":"56","author":"B Wang","year":"2016","unstructured":"Wang B, Zhou L, Xu K, Wang Q (2016) Prediction of minimum ignition energy from molecular structure using quantitative structure\u2013property relationship (QSPR) models. Ind Eng Chem Res 56:47\u201351. https:\/\/doi.org\/10.1021\/acs.iecr.6b04347","journal-title":"Ind Eng Chem Res"},{"key":"6762_CR22","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1590\/0104-1428.00916","volume":"27","author":"X Yu","year":"2017","unstructured":"Yu X, Huang X (2017) A quantitative relationship between Tgs and chain segment structures of polystyrenes. Polimeros 27:68\u201374. https:\/\/doi.org\/10.1590\/0104-1428.00916","journal-title":"Polimeros"},{"key":"6762_CR23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ecoenv.2016.01.020","volume":"128","author":"M Goodarzi","year":"2016","unstructured":"Goodarzi M, Coelho LS, Honarparvar B, Ortiz EV, Duchowicz PR (2016) Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides. Ecotoxicol Environ Saf 128:52\u201360. https:\/\/doi.org\/10.1016\/j.ecoenv.2016.01.020","journal-title":"Ecotoxicol Environ Saf"},{"key":"6762_CR24","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/0166-1280(94)80117-7","volume":"307","author":"JS Murray","year":"1994","unstructured":"Murray JS, Lane P, Paulsen K et al (1994) Statistically-based interaction indices derived from molecular surface electrostatic potentials: a general interaction properties function (GIPF). J Mol Struct THEOCHEM 307:55\u201364. https:\/\/doi.org\/10.1016\/0166-1280(94)80117-7","journal-title":"J Mol Struct THEOCHEM"},{"key":"6762_CR25","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1002\/wcms.19","volume":"1","author":"JS Murray","year":"2011","unstructured":"Murray JS, Politzer P (2011) The electrostatic potential: an overview. WIREs Comput Mol Sci 1:153\u2013163. https:\/\/doi.org\/10.1002\/wcms.19","journal-title":"WIREs Comput Mol Sci"},{"key":"6762_CR26","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1080\/1062936X.2019.1699602","volume":"31","author":"PR Duchowicz","year":"2019","unstructured":"Duchowicz PR (2019) QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides. SAR QSAR Environ Res 31:135\u2013148. https:\/\/doi.org\/10.1080\/1062936X.2019.1699602","journal-title":"SAR QSAR Environ Res"},{"key":"6762_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.molliq.2019.112013","author":"B Sepehri","year":"2020","unstructured":"Sepehri B (2020) A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view. J Mol Liq. https:\/\/doi.org\/10.1016\/j.molliq.2019.112013","journal-title":"J Mol Liq"},{"key":"6762_CR28","doi-asserted-by":"publisher","unstructured":"Huoyu R, Zhiqiang Z, Zhanggao L, Zhenzhen X (2022) QSPR models for the critical temperature and pressure of cycloalkanes. Chem Phys Lett 1(808):140088. https:\/\/doi.org\/10.1016\/j.cplett.2022.140088","DOI":"10.1016\/j.cplett.2022.140088"},{"key":"6762_CR29","doi-asserted-by":"publisher","DOI":"10.1080\/26395940.2025.2587497","author":"S Duan","year":"2025","unstructured":"Duan S, Xu B, Zhao M, Zhang X, Xu Y (2025) Construction of quantitative structure\u2013property relationship models for perfluoroalkyl and polyfluoroalkyl substances: organic carbon\u2013water partition coefficient and bioconcentration factor based on multitask learning. Environ Pollut Bioavail. https:\/\/doi.org\/10.1080\/26395940.2025.2587497","journal-title":"Environ Pollut Bioavail"},{"key":"6762_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2025.109430","author":"MF Hanif","year":"2026","unstructured":"Hanif MF (2026) Integration of graph-based descriptors with machine learning algorithm for QSPR modeling of fluoroquinolones. Comput Chem Eng. https:\/\/doi.org\/10.1016\/j.compchemeng.2025.109430","journal-title":"Comput Chem Eng"},{"key":"6762_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/math9010060","author":"PCS Costa","year":"2020","unstructured":"Costa PCS, Evangelista JS, Leal I, Miranda PCML (2020) Chemical graph theory for property modeling in QSAR and QSPR\u2014charming QSAR & QSPR. Mathematics. https:\/\/doi.org\/10.3390\/math9010060","journal-title":"Mathematics"},{"key":"6762_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.fluid.2013.06.008","volume":"353","author":"G Liang","year":"2013","unstructured":"Liang G, Xu J, Liu L (2013) QSPR analysis for melting point of fatty acids using genetic algorithm based multiple linear regression (GA-MLR). Fluid Phase Equilib 353:15\u201321. https:\/\/doi.org\/10.1016\/j.fluid.2013.06.008","journal-title":"Fluid Phase Equilib"},{"key":"6762_CR33","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.psep.2016.07.004","volume":"103","author":"TNG Borhani","year":"2016","unstructured":"Borhani TNG, Afzali A, Bagheri M (2016) QSPR estimation of the auto-ignition temperature for pure hydrocarbons. Process Saf Environ Prot 103:115\u2013125. https:\/\/doi.org\/10.1016\/j.psep.2016.07.004","journal-title":"Process Saf Environ Prot"},{"key":"6762_CR34","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10847-013-0337-7","volume":"79","author":"S Ahmadi","year":"2014","unstructured":"Ahmadi S, Babaee E, khazaei MR (2014) Application of self organizing maps and GA-MLR for the estimation of stability constant of 18-crown-6 ether derivatives with sodium cation. J Incl Phenom Macrocycl Chem 79:141\u2013149. https:\/\/doi.org\/10.1007\/s10847-013-0337-7","journal-title":"J Incl Phenom Macrocycl Chem"},{"key":"6762_CR35","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.aca.2007.10.004","volume":"604","author":"J Ghasemi","year":"2007","unstructured":"Ghasemi J, Saaidpour S (2007) Quantitative structure\u2013property relationship study of n-octanol\u2013water partition coefficients of some of diverse drugs using multiple linear regression. Anal Chim Acta 604:99\u2013106. https:\/\/doi.org\/10.1016\/j.aca.2007.10.004","journal-title":"Anal Chim Acta"},{"key":"6762_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.chemolab.2016.05.023","volume":"156","author":"L Jiao","year":"2016","unstructured":"Jiao L, Zhang X, Qin Y, Wang X, Li H (2016) QSPR study on the flash point of organic binary mixtures by using electrotopological state index. Chemom Intell Lab Syst 156:211\u2013216. https:\/\/doi.org\/10.1016\/j.chemolab.2016.05.023","journal-title":"Chemom Intell Lab Syst"},{"key":"6762_CR37","first-page":"12","volume":"32","author":"A Bouakkadia","year":"2016","unstructured":"Bouakkadia A, Haddag H, Bouarra N, Messadi D (2016) QSPR study of the water solubility of a diverse set of agrochemicals: hybrid (GA\/MLR) approach. Amel Bouakkadia, Hamza Haddag, Nabil Bouarra & Djelloul Messadi 32:12\u201321","journal-title":"Amel Bouakkadia, Hamza Haddag, Nabil Bouarra & Djelloul Messadi"},{"key":"6762_CR38","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.theochem.2006.09.026","volume":"805","author":"J Ghasemi","year":"2007","unstructured":"Ghasemi J, Saaidpour S, Brown SD (2007) QSPR study for estimation of acidity constants of some aromatic acids derivatives using multiple linear regression (MLR) analysis. J Mol Struct THEOCHEM 805:27\u201332https:\/\/doi.org\/10.1016\/j.theochem.2006.09.026","journal-title":"J Mol Struct THEOCHEM"},{"key":"6762_CR39","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.chemolab.2017.06.009","volume":"167","author":"L Zhou","year":"2017","unstructured":"Zhou L, Wang B, Jiang J, Pan Y, Wang Q (2017) Predicting the gas-liquid critical temperature of binary mixtures based on the quantitative structure property relationship. Chemom Intell Lab Syst 167:190\u2013195. https:\/\/doi.org\/10.1016\/j.chemolab.2017.06.009","journal-title":"Chemom Intell Lab Syst"},{"key":"6762_CR40","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.tca.2017.06.021","volume":"655","author":"L Zhou","year":"2017","unstructured":"Zhou L, Wang B, Jiang J, Pan Y, Wang Q (2017) Quantitative structure-property relationship (QSPR) study for predicting gas-liquid critical temperatures of organic compounds. Thermochim Acta 655:112\u2013116. https:\/\/doi.org\/10.1016\/j.tca.2017.06.021","journal-title":"Thermochim Acta"},{"key":"6762_CR41","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1007\/s10973-017-6941-9","volume":"132","author":"B Wang","year":"2018","unstructured":"Wang B, Park H, Xu K, Wang Q (2018) Prediction of lower flammability limits of blended gases based on quantitative structure\u2013property relationship. J Therm Anal Calorim 132:1125\u20131130. https:\/\/doi.org\/10.1007\/s10973-017-6941-9","journal-title":"J Therm Anal Calorim"},{"key":"6762_CR42","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.1021\/jp104287p","volume":"115","author":"AR Katritzky","year":"2011","unstructured":"Katritzky AR, Stoyanova-Slavova IB, T\u00e4mm K, Tamm T, Karelson M (2011) Application of the QSPR approach to the boiling points of azeotropes. J Phys Chem A 115:3475\u20133479. https:\/\/doi.org\/10.1021\/jp104287p","journal-title":"J Phys Chem A"},{"key":"6762_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2025.122992","author":"Y Zheng","year":"2026","unstructured":"Zheng Y, Ji C, Shi Z, Li R, Shi J, Peng C et al (2026) Machine learning-enhanced QSPR model for predicting the viscosity of ionic liquids. Chem Eng Sci. https:\/\/doi.org\/10.1016\/j.ces.2025.122992","journal-title":"Chem Eng Sci"},{"key":"6762_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.jlp.2025.105876","author":"Y Zhang","year":"2026","unstructured":"Zhang Y, Zhang X, Niu S, Pan Y (2026) Prediction of the exothermic onset temperature of aromatic nitro compounds by the SiRMS descriptors from molecular structures. J Loss Prev Process Ind. https:\/\/doi.org\/10.1016\/j.jlp.2025.105876","journal-title":"J Loss Prev Process Ind"},{"key":"6762_CR45","doi-asserted-by":"publisher","first-page":"9369","DOI":"10.1021\/j100139a019","volume":"97","author":"JS Murray","year":"1993","unstructured":"Murray JS, Lane P, Brinck T, Paulsen K, Grice ME, Politzer P (1993) Relationships of critical constants and boiling points to computed molecular surface properties. J Phys Chem 97:9369\u20139373. https:\/\/doi.org\/10.1021\/j100139a019","journal-title":"J Phys Chem"},{"key":"6762_CR46","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.jlp.2017.10.010","volume":"50","author":"B Wang","year":"2017","unstructured":"Wang B, Zhou L, Xu K, Wang Q (2017) Fast prediction of minimum ignition energy from molecular structure using simple QSPR model. J Loss Prev Process Ind 50:290\u2013294. https:\/\/doi.org\/10.1016\/j.jlp.2017.10.010","journal-title":"J Loss Prev Process Ind"},{"key":"6762_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e28260","author":"MS Abubakar","year":"2024","unstructured":"Abubakar MS, Aremu KO, Aphane M, Amusa LB (2024) A QSPR analysis of physical properties of antituberculosis drugs using neighbourhood degree-based topological indices and support vector regression. Heliyon. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e28260","journal-title":"Heliyon"},{"key":"6762_CR48","doi-asserted-by":"publisher","DOI":"10.3390\/catal11080920","author":"M Meraz","year":"2021","unstructured":"Meraz M, Malik A, Yang W, Sun W-H (2021) Catalytic performance of cycloalkyl-fused aryliminopyridyl nickel complexes toward ethylene polymerization by QSPR modeling. Catalysts. https:\/\/doi.org\/10.3390\/catal11080920","journal-title":"Catalysts"},{"key":"6762_CR49","doi-asserted-by":"publisher","unstructured":"Yuan S, Jiao Z, Quddus N, Kwon JS II, Mashuga CV (2019) Developing quantitative structure\u2013property relationship models to predict the upper flammability limit using machine learning. Ind Eng Chem Res 58:3531\u20137. https:\/\/doi.org\/10.1021\/acs.iecr.8b05938","DOI":"10.1021\/acs.iecr.8b05938"},{"key":"6762_CR50","unstructured":"Cheng N (2015) Solvents Handbook (Fifth Edition) (In Chinese). Chemistry Industry Press. https:\/\/www.cip.com.cn\/"},{"key":"6762_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.molliq.2021.115970","author":"Y Sun","year":"2021","unstructured":"Sun Y, Chen M, Zhao Y, Zhu Z, Xing H, Zhang P et al (2021) Machine learning assisted QSPR model for prediction of ionic liquid\u2019s refractive index and viscosity: the effect of representations of ionic liquid and ensemble model development. J Mol Liq. https:\/\/doi.org\/10.1016\/j.molliq.2021.115970","journal-title":"J Mol Liq"},{"key":"6762_CR52","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3969\/j.issn.1005-9954.2021.11.010","volume":"49","author":"Q Zhou","year":"2021","unstructured":"Zhou Q, Zhu L, Songzhao L, Lv L, Zhang C, Hao Q (2021) QSPR model for azeotropic properties of binary azeotropes containing halogenated hydrocarbons. Chem Eng (China) 49:47\u201365. https:\/\/doi.org\/10.3969\/j.issn.1005-9954.2021.11.010","journal-title":"Chem Eng (China)"},{"key":"6762_CR53","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1002\/poc.3540","volume":"29","author":"D Beiknejada","year":"2016","unstructured":"Beiknejada D, Chaichia MJ, Fatemi MH (2016) Prediction of photolysis half-lives of dihydroindolizinesby genetic algorithm-multiple linear regression (GA-MLR). J Phys Org Chem 29:312\u2013320. https:\/\/doi.org\/10.1002\/poc.3540","journal-title":"J Phys Org Chem"},{"key":"6762_CR54","doi-asserted-by":"publisher","unstructured":"Zeng XY, Zhu L, Lv L, Li B Prediction of azeotropic temperature and composition of binary azeotrope containing water based on quantitative structure-property relationship. Chem Ind Eng (China) 37:30\u20139. https:\/\/doi.org\/10.13353\/j.issn.1004.9533.20191702","DOI":"10.13353\/j.issn.1004.9533.20191702"},{"key":"6762_CR55","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3969\/j.issn.1005-9954.2018.02.006","volume":"46","author":"J Jiang","year":"2018","unstructured":"Jiang J, Pan Y, Song X, jiang J (2018) Prediction study for flash points of ternary miscible liquid mixtures (In Chinese). Chem Eng (China) 46:23\u201328. https:\/\/doi.org\/10.3969\/j.issn.1005-9954.2018.02.006","journal-title":"Chem Eng (China)"},{"key":"6762_CR56","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","journal-title":"J Chem Inf Comput Sci"},{"key":"6762_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.molliq.2023.122711","author":"X Liu","year":"2023","unstructured":"Liu X, Yu M, Jia Q, Yan F, Zhou Y-N, Wang Q (2023) Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids. J Mol Liq. https:\/\/doi.org\/10.1016\/j.molliq.2023.122711","journal-title":"J Mol Liq"},{"key":"6762_CR58","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1021\/jm030584q","volume":"47","author":"M Shen","year":"2004","unstructured":"Shen M, B\u00e9guin C, Golbraikh A, Stables JP, Kohn H, Tropsha A (2004) Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds. J Med Chem 47:2356\u20132364. https:\/\/doi.org\/10.1021\/jm030584q","journal-title":"J Med Chem"},{"key":"6762_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2025.108847","author":"M Aqib","year":"2026","unstructured":"Aqib M, Malik MA, Ali Y, Jutt S, Imran M (2026) Predicting optimal drugs for fin rot by reverse topological QSPR analysis. Comput Biol Chem. https:\/\/doi.org\/10.1016\/j.compbiolchem.2025.108847","journal-title":"Comput Biol Chem"},{"key":"6762_CR60","doi-asserted-by":"publisher","first-page":"4791","DOI":"10.3390\/molecules17054791","volume":"17","author":"F Sahigara","year":"2012","unstructured":"Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791\u2013810. https:\/\/doi.org\/10.3390\/molecules17054791","journal-title":"Molecules"},{"key":"6762_CR61","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 models validation: internal and external. QSAR Comb Sci 26:694\u2013701. https:\/\/doi.org\/10.1002\/qsar.200610151","journal-title":"QSAR Comb Sci"},{"key":"6762_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2024.120825","author":"A Ebrahimpoor Gorji","year":"2025","unstructured":"Ebrahimpoor Gorji A, Laakso J-P, Alopaeus V, Uusi-Kyyny P (2025) Towards the prediction of CPA pure parameters for imidazolium, ammonium, and pyridinium based-ionic liquids using QSPR study: a comparative study. Chem Eng Sci. https:\/\/doi.org\/10.1016\/j.ces.2024.120825","journal-title":"Chem Eng Sci"},{"key":"6762_CR63","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/S1570-7946(07)80078-2","volume":"24","author":"M Shacham","year":"2007","unstructured":"Shacham M, Brauner N, Cholakov GS, Stateva RP (2007) Identifying applicability domains for quantitative structure property relationships. Comput Aided Chem Eng 24:327\u2013332. https:\/\/doi.org\/10.1016\/S1570-7946(07)80078-2","journal-title":"Comput Aided Chem Eng"}],"container-title":["Journal of Molecular Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00894-026-06762-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00894-026-06762-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00894-026-06762-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:03:32Z","timestamp":1780437812000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00894-026-06762-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["6762"],"URL":"https:\/\/doi.org\/10.1007\/s00894-026-06762-z","relation":{},"ISSN":["1610-2940","0948-5023"],"issn-type":[{"value":"1610-2940","type":"print"},{"value":"0948-5023","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"25 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"186"}}