This mini review presents a brief description of the research efforts for new topological indices of organic molecular structures undertaken in the authors' laboratory at Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. They were used for the processing of chemical information, as highly selective topological indices for uniqueness determination, as highly selective atomic chiral indices for chiral center recognition, in the exhaustive generation of isomers, in a stereo code for the exhaustive generation of stereoisomers, in the prediction of C-13 nuclear magnetic resonance spectra, and in studies on rare earth extractions. The topological indices A mi , 3D descriptors, and chiral descriptors are described, as well as their applications in quantitative structure activity/property relationship studies. KEYWORDShighly selective chiral indices, highly selective topological indices, processing of chemical information, QSAR/QSPR, structure generator 1 | OVERVIEW Among the myriad of available methods for the representation of molecular structures, graph theoretical (topological) indices are one of the most widely used techniques for processing chemical information, quantitative structure activity/property relationship (QSAR/QSPR) studies, and for searching combinatorial libraries to find structures that are most similar to a target structure. 1 However, not all indices can be used for the same tasks. For example, the Wiener index has been satisfactorily correlated with paraffin boiling points, but its uniqueness (selectivity) is not sufficient.For the administration of chemical information, databases are the important components, such as mass spectra, infrared spectra, nuclear magnetic resonance (NMR), rare earth (RE), and toxicological compound databases constructed from the early 1980s in China. Nuclear magnetic resonance and RE databases were built by the Changchun Institute of Applied Chemistry, Chinese Academy of Sciences (CIAC, CAS). Correspondingly, a large number of schemes for encoding molecules and extracting descriptors of structures were suggested, which were used for database searching

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In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher.

Ionic liquids as green solvents have been paid extensive attention in recent years. However, mostly it is cost and time-consuming to measure their properties. Thus, theorical methods, especially ultrafast chemoinformatics methods were introduced into these studies. Instead of abstract and complex models in some QSPR studies, in this study, the 2D structural features related to the toxicity of ionic liquids were discussed at first, and then the corresponding intuitive and meaningful descriptors were suggested to construct quantitative chemoinformatics models, finally a multiple linear regression (MLR) based on the empirical-like models were applied to the estimation of toxicities of 304 ionic liquids. For the test sets, the relationship coefficients reached up to R = 0.90. An external test set of 11 ionic liquids collected from other literatures was submitted to the achieved MLR equations, and the satisfactory result (R = 0.94) was obtained.

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