Dataset Information

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids.

ABSTRACT: The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model's quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors' selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

SUBMITTER: Rybinska-Fryca A

PROVIDER: S-EPMC7321456 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids.

Rybińska-Fryca Anna A Sosnowska Anita A Puzyn Tomasz T

Materials (Basel, Switzerland) 20200530 11

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pair ...[more]

PMID: 32486309

Similar Datasets

Project description:Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid-liquid extraction process using ionic liquids (I.Ls) is an option for this separation. The selection of the most appropriate I.L. for this application is a challenging task due to the variety of anion and cation structures. In the current study, the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been evaluated using the Quantitative Structure-Property Relationship (QSPR) method. A dataset comprising of 112 ternary systems (namely, I.L., benzene, and aliphatic hydrocarbon) was compiled after an extensive review of literature. The primary dataset consists of 17 anions, 20 cations, and 12 aliphatic hydrocarbons. Therefore, the impact of the structure of anion, cation, or aliphatic hydrocarbon on the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been investigated. The linear QSPR models were constructed using Multiple Linear Regression (MLR). The statistical evaluation of the final linear model showed that the constructed model (R2 = 0.900) has an acceptable capability to predict the mole fraction of benzene in the I.L.-rich phase. Additionally, non-linear QSPR models were developed using Genetic Programming (GP) and Artificial Neural Network (ANN) machine learning methods. The statistical evaluation of the GP model (R2 = 0.927) and ANN model (R2 = 0.939) showed that non-linear models had slightly higher prediction accuracy compared to the linear model. The final QSPR model was developed using the BELe3 cation descriptor which is a 2D Burden eigenvalues descriptor and HTm anion descriptor which is a 3D GETAWAY descriptor. After model construction, the selected molecular descriptors of anion and cation structures has been interpreted. The results showed that the size and the electronegativity of the atoms in the anion and cation structure are probably important parameters that affect the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases. Additionally, the anion shape can be considered as an effective parameter in the benzene extraction process.

Dataset Information

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids.

Publications

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets