Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Collection
2.2. Calculation of Descriptors
2.3. Dataset Division and Validation Methods
2.4. Feature Selection and Model Development
2.5. Model Evaluation
2.6. Consensus Prediction with Multiple Models
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
References
- Sheldon, R.A. Fundamentals of green chemistry: Efficiency in reaction design. Chem. Soc. Rev. 2012, 41, 1437–1451. [Google Scholar] [CrossRef] [Green Version]
- Clark, J.H.; Tavener, S.J. Alternative solvents: Shades of green. Org. Process. Res. Dev. 2007, 11, 149–155. [Google Scholar] [CrossRef]
- Rogers, R.D.; Seddon, K.R. Chemistry. Ionic liquids-solvents of the future? Science 2003, 302, 792–793. [Google Scholar] [CrossRef] [PubMed]
- Das, R.N.; Roy, K. Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future. Mol. Divers. 2013, 17, 151–196. [Google Scholar] [CrossRef] [PubMed]
- Abbott, A.P.; Capper, G.; Davies, D.L.; Rasheed, R.K.; Tambyrajah, V. Novel solvent properties of choline chloride/urea mixtures. Chem. Comm. 2003, 70–71. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Garcia, G.; Aparicio, S.; Ullah, R.; Atilhan, M. Deep Eutectic Solvents: Physicochemical properties and gas separation applications. Energy Fuels 2015, 29, 2616–2644. [Google Scholar] [CrossRef]
- Halder, A.K.; Cordeiro, M.N.D.S. Probing the environmental toxicity of deep eutectic solvents and their components: An in silico modeling approach. ACS Sustain. Chem. Eng. 2019, 7, 10649–10660. [Google Scholar] [CrossRef]
- Ahmadi, R.; Hemmateenejad, B.; Safavi, A.; Shojaeifard, Z.; Mohabbati, M.; Firuzi, O. Assessment of cytotoxicity of choline chloride-based natural deep eutectic solvents against human HEK-293 cells: A QSAR analysis. Chemosphere 2018, 209, 831–838. [Google Scholar] [CrossRef]
- Roy, K.; Das, R.N.; Popelier, P.L.A. Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity. Environ. Sci. Pollut. Res. Int. 2015, 22, 6634–6641. [Google Scholar] [CrossRef]
- Smith, E.L.; Abbott, A.P.; Ryder, K.S. Deep eutectic solvents (DESs) and their applications. Chem. Rev. 2014, 114, 11060–11082. [Google Scholar]
- Shishov, A.; Bulatov, A.; Locatelli, M.; Carradori, S.; Andruch, V. Application of deep eutectic solvents in analytical chemistry. A review. Microchem. J. 2017, 135, 33–38. [Google Scholar] [CrossRef]
- Carriazo, D.; Serrano, M.C.; Gutierrez, M.C.; Ferrer, M.L.; del Monte, F. Deep-eutectic solvents playing multiple roles in the synthesis of polymers and related materials. Chem. Soc. Rev. 2012, 41, 4996–5014. [Google Scholar] [CrossRef]
- Jablonsky, M.; Majova, V.; Ondrigova, K.; Sima, K. Preparation and characterization of physicochemical properties and application of novel ternary deep eutectic solvents. Cellulose 2019, 26, 3031–3045. [Google Scholar] [CrossRef]
- Haghbakhsh, R.; Bardool, R.; Bakhtyari, A.; Duarte, A.R.C.; Raeissi, S. Simple and global correlation for the densities of deep eutectic solvents. J. Mol. Liq. 2019, 296, 111830. [Google Scholar] [CrossRef]
- Crespo, E.A.; Costa, J.M.L.; Palma, A.M.; Soares, B.; Martin, M.C.; Segovia, J.J.; Carvalho, P.J.; Coutinho, J.A.P. Thermodynamic characterization of deep eutectic solvents at high pressures. Fluid Phase Equilibr. 2019, 500, 112249. [Google Scholar] [CrossRef]
- Muratov, E.N.; Bajorath, J.; Sheridan, R.P.; Tetko, I.V.; Filimonov, D.; Poroikov, V.; Oprea, T.I.; Baskin, I.I.; Varnek, A.; Roitberg, A.; et al. QSAR without borders. Chem. Soc. Rev. 2020, 49, 3525–3564. [Google Scholar] [CrossRef] [PubMed]
- Wood, D.J.; Carlsson, L.; Eklund, M.; Norinder, U.; Stalring, J. QSAR with experimental and predictive distributions: An information theoretic approach for assessing model quality. J. Comput.-Aid. Mol. Des. 2013, 27, 203–219. [Google Scholar] [CrossRef] [Green Version]
- Halder, A.K.; Moura, A.S.; Cordeiro, M.N.D.S. QSAR modelling: A therapeutic patent review 2010-present. Expert Opin. Ther. Pat. 2018, 28, 467–476. [Google Scholar] [CrossRef] [PubMed]
- El-Harbawi, M.; Samir, B.B.; Babaa, M.R.; Mutalib, M.I.A. A new QSPR model for predicting the densities of ionic liquids. Arab. J. Sci. Eng. 2014, 39, 6767–6775. [Google Scholar] [CrossRef]
- Lemaoui, T.; Hammoudi, N.E.; Alnashef, I.M.; Balsamo, M.; Erto, A.; Ernst, B.; Benguerba, Y. Quantitative structure properties relationship for deep eutectic solvents using S sigma-profile as molecular descriptors. J. Mol. Liq. 2020, 309, 113165. [Google Scholar] [CrossRef]
- Lemaoui, T.; Darwish, A.S.; Attoui, A.; Abu Hatab, F.; Hammoudi, N.E.; Benguerba, Y.; Vega, L.F.; Alnashef, I.M. Predicting the density and viscosity of hydrophobic eutectic solvents: Towards the development of sustainable solvents. Green Chem. 2020, 22, 8511–8530. [Google Scholar] [CrossRef]
- Toropov, A.A.; Toropova, A.P. QSPR/QSAR: State-of-art, weirdness, the future. Molecules 2020, 25, 1292. [Google Scholar] [CrossRef] [Green Version]
- Muratov, E.N.; Varlamova, E.V.; Artemenko, A.G.; Polishchuk, P.G.; Kuz’min, V.E. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol. Inform. 2012, 31, 202–221. [Google Scholar] [CrossRef]
- Halder, A.K.; Cordeiro, M.N.D.S. Development of predictive linear and non-linear QSTR models for aliivibrio fischeri toxicity of deep eutectic solvents. Internat. J. Quant. Struc. Prop. Relat. 2019, 4, 50–69. [Google Scholar]
- Oprisiu, I.; Novotarskyi, S.; Tetko, I.V. Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM). J. Cheminformatics 2013, 5, 4. [Google Scholar] [CrossRef] [Green Version]
- Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. Dragon software: An easy approach to molecular descriptor calculations. Match-Commun. Math Co. 2006, 56, 237–248. [Google Scholar]
- Hechinger, M.; Leonhard, K.; Marquardt, W. What Is Wrong with Quantitative Structure-Property Relations Models Based on Three-Dimensional Descriptors? J. Chem. Inf. Model. 2012, 52, 1984–1993. [Google Scholar] [CrossRef] [PubMed]
- Muratov, E.N.; Varlamova, E.V.; Artemenko, A.G.; Polishchuk, P.G.; Nikolaeva-Glomb, L.; Galabov, A.S.; Kuz’min, V.E. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Struct. Chem. 2013, 24, 1665–1679. [Google Scholar] [CrossRef]
- Raschka, S. MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. J. Open Source Software 2018, 3, 638. [Google Scholar] [CrossRef]
- Gramatica, P. On the development and validation of QSAR models. Methods Mol. Biol. 2013, 930, 499–526. [Google Scholar] [PubMed]
- Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model. 2002, 20, 269–276. [Google Scholar] [CrossRef]
- Roy, K.; Chakraborty, P.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N. Some case studies on application of “r(m)2” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data. J. Comput. Chem. 2013, 34, 1071–1082. [Google Scholar] [CrossRef]
- Roy, P.P.; Paul, S.; Mitra, I.; Roy, K. On two novel parameters for validation of predictive QSAR models. Molecules 2009, 14, 1660–1701. [Google Scholar]
- Ojha, P.K.; Roy, K. Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr. Intell. Lab. Sys. 2011, 109, 146–161. [Google Scholar] [CrossRef]
- Serra, A.; Onlu, S.; Festa, P.; Fortino, V.; Greco, D. MaNGA: A novel multi-niche multi-objective genetic algorithm for QSAR modelling. Bioinformatics 2020, 36, 145–153. [Google Scholar] [CrossRef]
- Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci. 2007, 26, 694–701. [Google Scholar] [CrossRef]
- Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007, 9, 90–95. [Google Scholar] [CrossRef]
- Roy, K.; Ambure, P.; Kar, S.; Ojha, P.K. Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J. Chemometr. 2018, 32, e2992. [Google Scholar] [CrossRef]
- Khan, K.; Khan, P.M.; Lavado, G.; Valsecchi, C.; Pasqualini, J.; Baderna, D.; Marzo, M.; Lombardo, A.; Roy, K.; Benfenati, E. QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors. Chemosphere 2019, 229, 8–17. [Google Scholar] [CrossRef]
- Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics, 2nd ed.; Wiley-VCH: Weinheim, Germany, 2009. [Google Scholar]
- Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, Germany, 2000. [Google Scholar]
- Ong, S.A.K.; Lin, H.H.; Chen, Y.Z.; Li, Z.R.; Cao, Z.W. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinform. 2007, 8, 300. [Google Scholar] [CrossRef] [Green Version]
- Bosque, R.; Sales, J. Polarizabilities of solvents from the chemical composition. J. Chem. Inf. Comput. Sci. 2002, 42, 1154–1163. [Google Scholar] [CrossRef] [PubMed]
- Reutlinger, M.; Koch, C.P.; Reker, D.; Todoroff, N.; Schneider, P.; Rodrigues, T.; Schneider, G. Chemically Advanced Template Search (CATS) for scaffold-hopping and prospective target prediction for “orphan” molecules. Mol. Inform. 2013, 32, 133–138. [Google Scholar] [CrossRef] [Green Version]
- Labute, P. A widely applicable set of descriptors. J. Mol. Graph. Model. 2000, 18, 464–477. [Google Scholar] [CrossRef]
- Huang, Y.; Zhao, Y.; Zeng, S.; Zhang, X.; Zhang, S. Density prediction of mixtures of ionic liquids and molecular solvents using two new generalized models. Ind. Eng. Chem. Res. 2014, 53, 15270–15277. [Google Scholar] [CrossRef]
- Shahbaz, K.; Baroutian, S.; Mjalli, F.S.; Hashim, M.A.; AlNashef, I.M. Densities of ammonium and phosphonium based deep eutectic solvents: Prediction using artificial intelligence and group contribution techniques. Thermochim. Acta 2012, 527, 59–66. [Google Scholar] [CrossRef]
- Mjalli, F.S.; Shahbaz, K.; AlNashef, I.M. Modified Rackett equation for modelling the molar volume of deep eutectic solvents. Thermochim. Acta 2015, 614, 185–190. [Google Scholar] [CrossRef]
- Mjalli, F.S. Mass connectivity index-based density prediction of deep eutectic solvents. Fluid Phase Equilib. 2016, 409, 312–317. [Google Scholar] [CrossRef]
- Shahbaz, K.; Mjalli, F.S.; Hashim, M.A.; AlNashef, I.M. Prediction of deep eutectic solvents densities at different temperatures. Thermochim Acta 2011, 515, 67–72. [Google Scholar] [CrossRef]
- Kovacs, A.; Neyts, E.C.; Wijnants, M.; Cornet, I.; Billen, P. Modeling the physicochemical properties of natural deep eutectic solvents—A review. ChemSusChem 2020, 13, 3789–3804. [Google Scholar] [CrossRef] [PubMed]
- Alkhatib, I.I.I.; Bahamon, D.; Llovell, F.; Abu-Zahra, M.R.M.; Vega, L.F. Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents. J. Mol. Liq. 2020, 298, 112183. [Google Scholar] [CrossRef]
Model | Model Development Parameters | Training Set Results | Test Set Results | Max Inc # | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Scoring | CV | Seed | Intv * | Ntr | Q2LOO | Q2LCO | MAELOO | Ntest | R2Pred | MAEtest | ||
MO029 | NMAE | 0 | 4 | 2 | 666 | 0.967 | 0.930 | 0.010 | 488 | 0.627 | 0.043 | 0.606 |
MO023 | NMAE | 0 | 2 | 2 | 619 | 0.967 | 0.930 | 0.010 | 535 | 0.626 | 0.040 | 0.586 |
MO011 | R2 | 0 | 4 | 2 | 666 | 0.973 | 0.949 | 0.0101 | 488 | 0.424 | 0.054 | 0.475 |
MO041 | NMPD | 0 | 2 | 2 | 619 | 0.972 | 0.955 | 0.010 | 535 | 0.527 | 0.046 | 0.573 |
MO035 | NMAE | 0 | 6 | 2 | 711 | 0.964 | 0.941 | 0.011 | 443 | 0.540 | 0.046 | 0.600 |
MO005 | R2 | 0 | 2 | 2 | 619 | 0.966 | 0.946 | 0.012 | 535 | 0.480 | 0.047 | 0.720 |
MO071 | R2 | 5 | 6 | 2 | 711 | 0.956 | 0.933 | 0.013 | 443 | 0.642 | 0.045 | 0.811 |
MO059 † | R2 | 5 | 2 | 2 | 619 | 0.954 | 0.919 | 0.013 | 535 | 0.748 | 0.033 | 0.776 |
MO012 | R2 | 0 | 4 | 3 | 818 | 0.956 | 0.917 | 0.013 | 336 | 0.750 | 0.036 | 0.814 |
MO053 | NMPD | 0 | 6 | 2 | 711 | 0.952 | 0.936 | 0.014 | 443 | 0.444 | 0.052 | 0.719 |
MO017 | R2 | 0 | 6 | 2 | 711 | 0.953 | 0.933 | 0.014 | 443 | 0.475 | 0.050 | 0.719 |
MO085 | R2 | 10 | 4 | 4 | 894 | 0.943 | 0.924 | 0.014 | 260 | 0.543 | 0.050 | 0.841 |
MO047 | NMPD | 0 | 4 | 2 | 666 | 0.951 | 0.929 | 0.014 | 488 | 0.503 | 0.046 | 0.715 |
MO031 | NMAE | 0 | 4 | 4 | 894 | 0.926 | 0.902 | 0.015 | 260 | 0.658 | 0.043 | 0.918 |
MO022 | NMAE | 0 | 1 | 5 | 915 | 0.940 | 0.919 | 0.016 | 239 | 0.689 | 0.033 | 0.643 |
Model | Model Development Parameters | Training Set Results | Test Set Results | Max Inc # | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Scoring | CV | Seed | Intv * | Ntr | Q2LOO | Q2LCO | MAELOO | Ntest | R2Pred | MAEtest | ||
CO023 | NMPD | 0 | 1 | 1 | 609 | 0.947 | 0.901 | 0.010 | 545 | 0.637 | 0.063 | 0.647 |
CO001 | R2 | 0 | 1 | 1 | 609 | 0.948 | 0.875 | 0.011 | 545 | 0.624 | 0.054 | 0.541 |
CO012 | NMAE | 0 | 1 | 1 | 609 | 0.925 | 0.838 | 0.011 | 545 | 0.225 | 0.088 | 0.545 |
CO013 | NMAE | 0 | 1 | 2 | 825 | 0.956 | 0.934 | 0.012 | 329 | 0.750 | 0.055 | 0.535 |
CO014 | NMAE | 0 | 1 | 3 | 784 | 0.926 | 0.726 | 0.012 | 370 | −3.107 | 0.168 | 0.931 |
CO015 † | NMAE | 0 | 1 | 4 | 827 | 0.934 | 0.915 | 0.012 | 327 | 0.867 | 0.036 | 0.503 |
CO004 | R2 | 0 | 1 | 4 | 827 | 0.938 | 0.927 | 0.013 | 327 | 0.731 | 0.060 | 0.503 |
CO026 | NMPD | 0 | 1 | 4 | 827 | 0.938 | 0.927 | 0.013 | 327 | 0.731 | 0.060 | 0.503 |
CO016 | NMAE | 0 | 1 | 5 | 831 | 0.930 | 0.900 | 0.013 | 323 | 0.618 | 0.068 | 0.868 |
CO002 | R2 | 0 | 1 | 2 | 825 | 0.950 | 0.895 | 0.013 | 329 | 0.707 | 0.059 | 0.848 |
CO017 | NMAE | 0 | 1 | 6 | 837 | 0.927 | 0.891 | 0.014 | 317 | 0.880 | 0.041 | 0.538 |
CO005 | R2 | 0 | 1 | 5 | 831 | 0.931 | 0.910 | 0.014 | 323 | 0.625 | 0.068 | 0.833 |
CO027 | NMPD | 0 | 1 | 5 | 831 | 0.918 | 0.887 | 0.014 | 323 | 0.327 | 0.084 | 0.670 |
CO029 | NMPD | 0 | 2 | 1 | 600 | 0.954 | 0.925 | 0.014 | 554 | 0.645 | 0.045 | 0.852 |
CO018 | NMAE | 0 | 2 | 1 | 600 | 0.938 | 0.877 | 0.015 | 554 | 0.617 | 0.050 | 0.384 |
Model | Equation | Training Set Results | Test Set Results |
---|---|---|---|
Ntraining = 619; R2 = 0.956; | |||
MO59 | ρ = +1.065(±0.012) + 0.072(±0.002) MAXDNpmix + 0.007(±0.000) P_VSA_s_5pmix | R2Adj = 0.955; | Ntest = 535; |
+0.018(±0.002) nHDonpmix + 0.024(±0.002) CATS2D_03_DApmix | F(10,608) = 1305.70; | R2Pred = 0.748; | |
+0.042(±0.003) CATS2D_01_NLpmix − 0.011(±0.006) CATS2D_08_LLpmix | Q2LOO = 0.953; MAELOO = 0.013; | MAEtest = 0.033, | |
+0.010(±0.000) MLOGP2pmix + 0.042(±0.002) VE3sign_Xnmix | Q2LCO = 0.919; MAELCO = 0.018; | rm2(test) = 0.646; | |
+0.091(±0.009) MATS4pmix − 0.001(±0.000) T(K) | rm2(LOO) = 0.933; ∆rm2(LOO) = 0.040; | ∆rm2 (test) = 0.199; | |
%AARDtraining = 1.151; | %AARDtest = 2.914 | ||
cR2P (1000 runs)= 0.948 | |||
Ntraining = 827; R2 = 0.937; | |||
CO15 | ρ = +1.101(±0.014) + 0.033(±0.002) AMWpmix − 0.066(±0.005) Psi_i_1dpmix | R2Adj = 0.936; | Ntest = 327; |
−0.012(±0.000) ATSC8mpmix + 0.851(±0.016) ATSC1epmix | F(10,816) = 1213; | R2Pred = 0.867; | |
−0.255(±0.016) VE2_Dz(Z)pmix + 0.054(±0.005) nCconjpmix | Q2LOO = 0.934; MAELOO = 0.012; | MAEtest = 0.036; | |
−0.029(±0.002) CATS2D_02_DLpmix + 0.010(±0.000) MLOGP2pmix | Q2LCO = 0.915; MAELCO = 0.014; | rm2(test) = 0.586; | |
+0.185 (±0.014) GGI5nmix − 0.001(±0.000) T(K) | rm2(LOO) = 0.905; ∆rm2(LOO) = 0.055; | ∆rm2 (test) = 0.205; | |
%AARDtraining = 1.040; | %AARDtest = 3.400 | ||
cR2P (1000 runs) = 0.931 |
Model | Parameters | Training Set | Test Set | External Validation Set | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Scoring | CV | Seed | Intv | Ntr | Q2LOO | Q2LCO | MAELOO | Nts | R2Pred | MAEtest | Nex | R2Pred | MAEtest | |
CO54 | R2 | 10 | 4 | 1 | 854 | 0.881 | 0.838 | 0.025 | 300 | 0.803 | 0.030 | 207 | 0.867 | 0.034 |
MO75 | R2 | 10 | 1 | 4 | 856 | 0.865 | 0.845 | 0.025 | 298 | 0.802 | 0.020 | 207 | 0.879 | 0.038 |
CO17 | NMAE | 0 | 1 | 6 | 837 | 0.927 | 0.891 | 0.014 | 317 | 0.880 | 0.041 | 207 | 0.874 | 0.039 |
CO15 | NMAE | 0 | 1 | 4 | 827 | 0.934 | 0.915 | 0.012 | 327 | 0.867 | 0.036 | 207 | 0.842 | 0.040 |
MO59 | R2 | 5 | 2 | 2 | 619 | 0.954 | 0.919 | 0.013 | 535 | 0.748 | 0.033 | 207 | 0.856 | 0.041 |
MO10 | R2 | 0 | 3 | 4 | 885 | 0.884 | 0.865 | 0.022 | 269 | 0.903 | 0.022 | 207 | 0.786 | 0.051 |
No. | Models | CM | R2Pred | MAEtest | rm2(test) | ∆rm2(test) | %AARDtest | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
C1 | CO54 | MO75 | CO17 | CO15 | MO59 | 2 | 0.903 | 0.030 | 0.883 | 0.046 | 2.544 |
C2 | CO54 | - | CO17 | CO15 | MO59 | 2 | 0.901 | 0.300 | 0.895 | 0.047 | 2.533 |
C3 | CO54 | MO75 | CO17 | CO15 | - | 3 | 0.906 | 0.027 | 0.918 | 0.038 | 2.281 |
C4 | CO54 | MO75 | - | CO15 | MO59 | 2 | 0.898 | 0.031 | 0.840 | 0.057 | 2.592 |
C5 | CO054 | MO75 | CO17 | - | MO59 | 2 | 0.911 | 0.029 | 0.868 | 0.050 | 2.460 |
C6 | - | MO75 | CO17 | CO15 | MO59 | 0 | 0.893 | 0.033 | 0.850 | 0.057 | 2.813 |
C7 | CO54 | - | CO17 | CO15 | - | 3 | 0.906 | 0.027 | 0.916 | 0.036 | 2.301 |
C8 | CO54 | MO75 | - | CO15 | - | 3 | 0.893 | 0.028 | 0.903 | 0.017 | 2.311 |
C9 | CO54 | MO75 | CO17 | - | - | 3 | 0.921 | 0.025 | 0.932 | 0.031 | 2.151 |
C10 | CO54 | - | CO017 | - | - | 3 | 0.921 | 0.026 | 0.929 | 0.029 | 2.171 |
C11 | CO54 | MO75 | - | - | - | 3 | 0.907 | 0.030 | 0.793 | 0.074 | 2.619 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Halder, A.K.; Haghbakhsh, R.; Voroshylova, I.V.; Duarte, A.R.C.; Cordeiro, M.N.D.S. Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures. Molecules 2021, 26, 5779. https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26195779
Halder AK, Haghbakhsh R, Voroshylova IV, Duarte ARC, Cordeiro MNDS. Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures. Molecules. 2021; 26(19):5779. https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26195779
Chicago/Turabian StyleHalder, Amit Kumar, Reza Haghbakhsh, Iuliia V. Voroshylova, Ana Rita C. Duarte, and M. Natalia D. S. Cordeiro. 2021. "Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures" Molecules 26, no. 19: 5779. https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26195779