# Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{12}) of potential binary (anions/cations) combinations, experimental optimization of ILs properties would be expensive and time consuming. Nevertheless, the selection of an ionic liquid having the optimal combination of the required properties is achievable by applying computational techniques such as the quantitative structure-activity/property relationship (QSAR/QSPR) approach [2]. QSAR/QSPR provides an opportunity to predict the property of interest for a number of empirically untested ILs based on the previously defined relationship between the variation in their chemical structures (encoded by a series of numerical values, so-called ‘descriptors’, e.g., the number of double bonds in the molecule) and the property (e.g., density, viscosity, octanol-water partition coefficient). The same applies for predicting biological activity, including toxicity (e.g., toxicity to Vibrio fisheri, Daphnia magna and Danio rerio), which is important from the human and environmental safety point of view [3,4,5,6]. By exploring the predictions coming from QSAR/QSPR models one is able to perform virtual screening of a vast number of ionic liquids to find ones with the preferred physicochemical properties and low toxicity to human and to the environment.

^{2}validation coefficient and low values of the root mean square error calculated for the external validation set), hence similarly good quality. In contrary, the model utilizing descriptors calculated from DFT-based geometries showed lower quality. The above results allowed the authors to recommend the use of the semi-empirical PM7 method as a routine for separate geometry optimization of anion and cation and then for the calculation of descriptors for anions and cations separately [9]. Subsequently, the two blocks of descriptors (calculated for different anions and cations) can be put together to form a single table of descriptors that characterizes the set of ILs (rows in the table correspond to particular ILs, whereas columns contain descriptors).

## 2. Materials and Methods

#### 2.1. Experimental Data and Molecular Descriptors

#### 2.2. Model Development

^{2}), root mean square error of calibration (RMSE

_{C}), mean of absolute errors (MAE). All calculated metrics can be found in Supplementary Materials (Tables S2–S7).

#### 2.3. Validation Process

^{2}

_{CV}) and root mean square error of cross-validation (RMSE

_{CV}). We also estimate the predictive ability by calculating two external validation measures: external validation coefficient (Q

^{2}

_{EXT}) and root mean square error of prediction (RMSE

_{EXT}). It should be noticed that external measures are calculated only for chemicals from the validation set. Additional parameters have been also calculated in order to confirm quality of the developed QSPR models, namely: concordance correlation coefficient (CCC) and modified r

^{2}for whole dataset (r

^{2}

_{(overall)}) [17]. We also estimated the presence of influential points in the training set by performing F-test proposed by Toth et al., where F value is equal to: (1 – Q

^{2}

_{CV})/(1 – R

^{2}) [18]. Moreover, we calculated other metrics and compared them with criteria proposed by Tropsha and thereby confirmed the good quality of the developed QSPR models [19]. Those criteria and values of all additional metrics can be found in Supplementary Materials (Tables S2–S7).

#### 2.4. Applicability Domain

_{i}> h* are treated as the results of extrapolation, so they will be less reliable [23].

## 3. Results and Discussion

#### 3.1. Relationship between the Form of Structure Representation and the Model Quality

^{2}, Q

^{2}

_{CV}, Q

^{2}

_{EXT}close to 1 and low values of the errors: RMSE

_{C}, RMSE

_{CV}, RMSE

_{EXT}) (Figure 1). Surprisingly, models with 3D descriptors are not the ones with the best quality metrics. The model based on 2D descriptors calculated for ionic pair (M4) is the one that is most accurate in terms of internal as well as external data set. The visual correlations between the experimental and the predicted log EC50 values for all developed models confirmed the differences in the statistical parameters mentioned above (Figure 2 and Figure 3).

^{A}) reflects the electronegativity of atoms in the molecule and its topology (anions in this case) [24], whereas the second (QZZm

^{C}) characterizes the distribution of electric charges, taking into account the mass of the cation.

#### 3.2. Influence of Structure Representation on the Variable Selection

#### 3.3. Influence of the Presence of the Second ion on Reliability of the Applicability Domain Assessment

^{A}descriptor (equals to 1.38) in the case of both: 1-butyl-3-methylpyridinium thiocyanate and 1-octyl-3-methylimidazolium thiocyanate. The same situation is in the case of cationic descriptors (Table 3). Therefore, the borders of the applicability domain are exact and easy to define. However, when the descriptors are calculated based on the structure of the whole ionic liquid the values of 3D descriptors are influenced by geometries of both ions. Thus, the same descriptors have a range of different values (Table 4). In consequence, the verification of AD is more challenging. Moreover, in such a case it should be investigated whether the selection of geometry optimization method and conditions would significantly affect the calculated 3D descriptors.

#### 3.4. Other Practical Aspects of ILs Modeling with QSAR/QSPR

_{50}) for Vibrio fischeri based on for 33 ionic liquids. Because all 33 ILs contained a halide anion (chloride or bromide), only the molecular structure of the cations was optimized at the DFT level of theory and used for calculating constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors (with CODESSA software,). Similarly, Nekoeinia et al. [30] developed a QSPR model for predicting the normalized polarity parameter (E

^{T}

_{N}). The model was developed based on a set of 52 ILs having the same anions: (CF

_{3}SO

_{2})N

^{2−}. The 2D and 3D descriptors such as topological and GETAWAY were calculated only for cations, after geometry optimization at the molecular mechanics level of theory (MM+ force field implemented in HyperChem software, v.7). In the model of cytotoxicity to the leukemia rat cell line (IPC-81) developed by Torrecilla et al. [31] the dataset included ionic liquids having various types of cation and anions. Therefore, the authors optimized molecular geometries of the cations and anions independently at the level of DFT (B3LYP/6-31++G**). Based on that, they derived the Sσ-profile molecular descriptors of counterions. A different approach was used by Wang et al. [32]. Although they modeled ionic liquids with the same anion (bromide), they were optimizing geometry of the whole structure (at the DFT level, B3LYP/6–311G (d, p)) in the case of each IL. Then, they used the quantum-mechanical properties (e.g., HOMO/LUMO energy, the total energy) of Br-ILs as descriptors in a QSAR model predicting toxicity towards V. fischeri and D. magna.

## 4. Conclusions

- 2D descriptors are suitable to build reliable QSAR models;
- The strategy in which the 2D descriptors were calculated for the whole ionic liquid allowed to build the model with the highest quality;
- More precise description of the ionic liquid’s structure (through 3D descriptors calculated for ions or geometry optimization of ionic pairs followed by descriptors calculation) does not guarantee the better accuracy and predictive ability of the developed model;
- Models based on 2D descriptors are easier to apply and reproduce, even by non-experts in computational chemistry, which could lead to an increase of the application of in silico methods in various R&D areas.

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## References

- 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] - Cronin, M.T.D. Quantitative Structure–Activity Relationships (QSARs)—Applications and methodology. In Recent Advances in QSAR Studies. Methods and Applications; Puzyn, T., Leszczynski, J., Cronin, M.T.D., Eds.; Springer: Dordrecht, The Netherlands; New York, NY, USA, 2010; pp. 3–11. ISBN 97814020978291402097824. [Google Scholar]
- Peric, B.; Sierra, J.; Martí, E.; Cruañas, R.; Garau, M.A. Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids. Ecotoxicol. Environ. Saf.
**2015**, 115, 257–262. [Google Scholar] [CrossRef] [PubMed] - Roy, K.; Das, R.N.; Popelier, P.L.A. Quantitative structure-activity relationship for toxicity of ionic liquids to Daphnia magna: Aromaticity vs. lipophilicity. Chemosphere
**2014**, 112, 120–127. [Google Scholar] [CrossRef] [PubMed] - Chen, B.-K.K.; Liang, M.-J.J.; Wu, T.-Y.Y.; Wang, H.P. A high correlate and simplified QSPR for viscosity of imidazolium-based ionic liquids. Fluid Phase Equilib.
**2013**, 350, 37–42. [Google Scholar] [CrossRef] - Rybinska, A.; Sosnowska, A.; Grzonkowska, M.; Barycki, M.; Puzyn, T. Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient. J. Hazard. Mater.
**2016**, 303, 137–144. [Google Scholar] [CrossRef] [PubMed] - Roy, K.; Kar, S.; Das, R.N. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment; Elsevier Academic Press: Amsterdam, The Netherlands; Boston, MA, USA, 2015; ISBN 01280150559780128015056. [Google Scholar]
- Barycki, M.; Sosnowska, A.; Puzyn, T. Which structural features stand behind micelization of ionic liquids? Quantitative Structure-Property Relationship studies. J. Colloid Interface Sci.
**2017**, 487, 475–483. [Google Scholar] [CrossRef] - Rybinska, A.; Sosnowska, A.; Barycki, M.; Puzyn, T. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids. J. Comput. Aided. Mol. Des.
**2016**, 30, 165–176. [Google Scholar] [CrossRef] - 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] - Ghanem, O.B.; Shah, S.N.; Lévêque, J.-M.; Mutalib, M.I.A.; El-Harbawi, M.; Khan, A.S.; Alnarabiji, M.S.; Al-Absi, H.R.H.; Ullah, Z. Study of the antimicrobial activity of cyclic cation-based ionic liquids via experimental and group contribution QSAR model. Chemosphere
**2018**, 195, 21–28. [Google Scholar] [CrossRef] - Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.A.; et al. Gaussian 09, Revision D.01; Gaussian, Inc.: Wallingford, CT, USA, 2013. [Google Scholar]
- Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. DRAGON software: an easy approach to molecular descriptor. Commun. Math. Comput. Chem.
**2006**, 56, 237–248. [Google Scholar] - Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; Mannhold, R., Kubinyi, H., Folkers, G., Eds.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2009; ISBN 978-3-527-31852-0. [Google Scholar]
- Hebbali, A. olsrr: Tools for Building OLS Regression Models. Available online: https://cran.r-project.org/web/packages/olsrr/index.html. (accessed on 5 April 2020).
- OECD. OECD Principles for the Validation, for Regulatory Purposes, of Quantitative Structure-Activity Relationship Models; OECD: Paris, France, 2004. [Google Scholar]
- 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] [PubMed] - Toth, G.; Bodai, Z.; Heberger, K. 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.
**2013**, 27, 837–844. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tropsha, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Inform.
**2010**, 29, 476–488. [Google Scholar] [CrossRef] [PubMed] - Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR applicabilty domain estimation by projection of the training set descriptor space: A review. Altern. Lab. Anim.
**2005**, 33, 445–459. [Google Scholar] [CrossRef] [PubMed] - Roy, K.; Kar, S.; Ambure, P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst.
**2015**, 145, 22–29. [Google Scholar] [CrossRef] - Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb. Sci.
**2007**, 26, 694–701. [Google Scholar] [CrossRef] - Gramatica, P.; Cassani, S.; Roy, P.P.; Kovarich, S.; Yap, C.W.; Papa, E. QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae. Mol. Inform.
**2012**, 31, 817–835. [Google Scholar] [CrossRef] - Pogliani, L. Modeling with Molecular Pseudoconnectivity Descriptors. A Useful Extension of the Intrinsic I-State Concept. J. Phys. Chem. A
**2000**, 104, 9029–9045. [Google Scholar] [CrossRef] - Ghanem, O.B.; Mutalib, M.; El-Harbawi, M.; Gonfa, G.; Kait, C.F.; Alitheen, N.B.M.; Lévêque, J.-M. Effect of imidazolium-based ionic liquids on bacterial growth inhibition investigated via experimental and QSAR modelling studies. J. Hazard. Mater.
**2015**, 297, 198–206. [Google Scholar] [CrossRef] - Cho, C.-W.; Park, J.-S.; Stolte, S.; Yun, Y.-S. Modelling for antimicrobial activities of ionic liquids towards Escherichia coli, Staphylococcus aureus and Candida albicans using linear free energy relationship descriptors. J. Hazard. Mater.
**2016**, 311, 168–175. [Google Scholar] [CrossRef] - Cornmell, R.J.; Winder, C.L.; Tiddy, G.J.T.; Goodacre, R.; Stephens, G. Accumulation of ionic liquids in Escherichia coli cells. Green Chem.
**2008**, 10, 836. [Google Scholar] [CrossRef] - Forrest, S. Genetic algorithms: principles of natural selection applied to computation. Science
**1993**, 261, 872–878. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bruzzone, S.; Chiappe, C.; Focardi, S.E.; Pretti, C.; Renzi, M. Theoretical descriptor for the correlation of aquatic toxicity of ionic liquids by quantitative structure-toxicity relationships. Chem. Eng. J.
**2011**, 175, 17–23. [Google Scholar] [CrossRef] - Nekoeinia, M.; Yousefinejad, S.; Abdollahi-Dezaki, A. Prediction of E-N(T) Polarity Scale of Ionic Liquids Using a QSPR Approach. Ind. Eng. Chem. Res.
**2015**, 54, 12682–12689. [Google Scholar] [CrossRef] - Torrecilla, J.S.; Palomar, J.; Lemus, J.; Rodríguez, F. A quantum-chemical-based guide to analyze/quantify the cytotoxicity of ionic liquids. Green Chem.
**2010**, 12, 123–134. [Google Scholar] [CrossRef] - Wang, C.; Wei, Z.; Wang, L.; Sun, P.; Wang, Z. Assessment of bromide-based ionic liquid toxicity toward aquatic organisms and QSAR analysis. Ecotoxicol. Environ. Saf.
**2015**, 115, 112–118. [Google Scholar] [CrossRef] [PubMed] - Sepehri, B. A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view. J. Mol. Liq.
**2020**, 297, 112013. [Google Scholar] [CrossRef] - Abramenko, N.; Kustov, L.; Metelytsia, L.; Kovalishyn, V.; Tetko, I.; Peijnenburg, W. A review of recent advances towards the development of QSAR models for toxicity assessment of ionic liquids. J. Hazard. Mater.
**2020**, 384, 121429. [Google Scholar] [CrossRef]

**Figure 1.**Basic steps that form the process of the quantitative structure-activity/property relationship (QSAR/QSPR) model development.

Model’s ID | Type of Descriptors | Descriptors Calculated for: | Equation for Predicting logEC_{50} [mM] |
---|---|---|---|

M1 | 2D | Separate ions | logEC_{50} = 2.49−0.14 Psi_i_0^{A} − 0.001 SMTIV^{C} |

M2 | 3D | Separate ions | logEC_{50} = 2.52−0.12 L1m^{C} − 0.19 L1i^{A} |

M3 | 2D, 3D | Separate ions | logEC_{50} = 2.304−0.142 Psi_i_0^{A} − 0.006 QZZm^{C} |

M4 | 2D | Ionic pair | logEC_{50} = 4.15−0.001 GMTI − 0.09 MDDD − 0.16 AMW |

M5 | 3D | Ionic pair | logEC_{50} = 6.91−0.24 L/Bw − 1.05 RTv + 0.53 L3u |

M6 | 2D, 3D | Ionic pair | logEC_{50} = 3.49−0.001 GMTI − 3.21 E1e + 0.04 DISPm |

Model | Number of All Variables | Anions’ Descriptors | Cations’ Descriptors |
---|---|---|---|

M1 | 572 | 278 | 294 |

M2 | 813 | 352 | 461 |

M3 | 1385 | 630 | 755 |

M4 | 298 | 0 | 0 |

M5 | 414 | 0 | 0 |

M6 | 712 | 0 | 0 |

**Table 3.**Three-dimensional (3D) descriptors calculated for anions and cations after separate optimization of ions. Descriptors in the table form equation of model M2.

IL | L1i^{A} | L1m^{C} | |
---|---|---|---|

[C4mpy] [SCN] | 1.38 | 6.78 | The same anion |

[C8mim] [SCN] | 1.38 | 18.4 | |

[C4mpyrr] [NTf2] | 5.67 | 4.48 | The same cation |

[C4mpyrr] [Br] | 0.00 | 4.48 |

**Table 4.**Three-dimensional (3D) descriptors calculated for ionic pairs with the same anion present in M5 equation.

IL | L/Bw | RTv | L3u |
---|---|---|---|

[C4mim] [NTf2] | 1.98 | 6.69 | 2.29 |

[C4py] [NTf2] | 1.68 | 6.65 | 1.39 |

[C4mpyrr] [NTf2] | 2.3 | 6.75 | 2.13 |

[C4mpip] [NTf2] | 2.03 | 6.82 | 1.39 |

[C8mim] [NTf2] | 2.85 | 7.20 | 2.13 |

[C8py] [NTf2] | 2.2 | 7.16 | 1.56 |

[C8mpyrr] [NTf2] | 2.05 | 6.93 | 1.28 |

[C8mpip] [NTf2] | 2.23 | 7.34 | 1.90 |

Range | 1.17 | 0.69 | 1.02 |

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**MDPI and ACS Style**

Rybińska-Fryca, A.; Sosnowska, A.; Puzyn, T.
Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids. *Materials* **2020**, *13*, 2500.
https://doi.org/10.3390/ma13112500

**AMA Style**

Rybińska-Fryca A, Sosnowska A, Puzyn T.
Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids. *Materials*. 2020; 13(11):2500.
https://doi.org/10.3390/ma13112500

**Chicago/Turabian Style**

Rybińska-Fryca, Anna, Anita Sosnowska, and Tomasz Puzyn.
2020. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids" *Materials* 13, no. 11: 2500.
https://doi.org/10.3390/ma13112500