Exploration of automatic learning to establish relationships between the molecular structure of chiral ionic liquids and the specific optical rotation

The experimental assignment of absolute configurations of chiral compounds is generally expensive and time consuming. Theoretical prediction of optical rotations can greatly assist in this endeavor. Herein a chemoinformatics exploration is reported to automatically distribute ionic liquids (IL) in a counterpropagation neural network incorporating a Kohonen layer for processing descriptors of cations, and five output layers to store optical rotations of ILs with five different anions. Cations were represented by chiral descriptors based on parities of the chiral centers assigned according to several steric and physicochemical atomic properties of the ligands. A data set with 106 chiral ionic liquids and their enantiomers was used covering a range of specific optical rotation between −147.3° and +147.3°. The obtained maps reveal relationships between the molecular structure of cations, the anions, and the optical rotation. The correct assignment of the sign of the specific optical rotations was achieved for 20 of the 22 pairs of enantiomers in an independent test set. CPG NNs estimated the value of the specific rotation with a RMS error of 22°, but more accurate predictions were obtained with a random forest that yielded a RMS error of 11°.