Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps
In this work, we apply generative topographic maps as a universal approach for data visualization and structure?property modeling of melting points (mp), which is one of the most important physical properties for the design and application of ionic liquids (ILs) as green solvents. Data visualization is part of a more general concept of chemography, which is a relatively new field dealing with visualization of chemical data, representation of chemical space, and navigation in this space. This field has received much attention by chemists as it may help to analyze and to intuitively comprehend relevant molecular features and relationships. In this study, to our knowledge for the first time, we proposed the universal approach that can be used both for the visualization of the chemical space of ILs according to their melting point values and for the development of the classification models able to predict the melting points of novel ILs. The structurally diverse data set of 717 ILs containing bromides of nitrogen-containing organic cations and including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT) was involved in model development. This study was carried out in several descriptor spaces analyzing the impact of descriptor choice. The clear criteria for data visualization and classification quality were used to assess the performance of the developed models.