Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps

Abstract

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.

Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches

“…The Li ion transport characteristics of the collected data were analyzed by combining regression analysis, thus elucidating the compositionstructure-ionic conductivity relationships and surveying garnetrelated structures for promising compositions with data visualization techniques, known in computer-aided molecular design as chemography or cartography approaches; this provided a bird's eye view of the materials space of solid electrolytes, which is attractive for virtual screening. Both groups of methods 29,[42][43][44][45][46] are based on the chemical similarity principle, which claims similar property values for similar compounds; the latter methods posit a correspondence between the positioning of the compounds in the chemical space defined by the ensemble of selected parameters (descriptors) that are identified to be responsible for the target properties and the structural similarity.…”

Progress in visual representations of chemical space

“…Newer applications include the analysis of property distribution and landscapes in the datasets [60], target and pharmacophore mapping of the natural product space [63], prediction of melting points for ionic liquids [64], classification of drugs according to the solubility and metabolism [65]. Nevertheless, in certain cases no significant difference can be found between the SOM and GTM [66].…”

Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance‐Based Versus Topology‐Based Approaches

“…Generative Topographic Maps (GTM): [21][22][23][24][25][26][27] GTM is a specific unsupervised density network based on generative modeling. It can be considered as a probabilistic extension of Kohonen Self-Organizing Maps.…”

“…Whereas IM relates to geodesic distances (for example, the length of the path that lies on the surface of a low-dimensional manifold), DM implies diffusion ones that reflect probabilities to jump from one point to another according to random walk Markov chains. GTM [21][22][23][24][25][26][27] and Laplacian Eigenmaps (LE) [28] are related to the topology-based techniques. This group of methods is based on the preservation of topology that is concerned with relative proximities: compounds that are close in the data space remain close in visualization.…”