Molecular Crystal Prediction Approach by Molecular Similarity: Data Mining on Molecular Aggregation Predictors and Crystal Descriptors

Assuming that isolated molecules contain information of their self-aggregation into the condensed state, similar molecules with respect to that information would form similar crystals. This fact is used in this work to predict molecular crystal packing. To compare isolated molecules, they are codified by equi-dimensional vectors including, as molecular aggregation predictors, the electric potential distribution on the surface of the molecular inertial ellipsoid with axes ( L, M, S ) and some other molecular form parameters. Their corresponding molecular crystals are codified by equi-dimensional vectors including, as packing descriptors, the cell axes ( a, b, c ) in decreasing order a > b > c, the cell/ellipsoid axes quotients ( a / L, b / M, c / S ), and some crystal symmetry information. Both the molecular vectors and crystal vectors of 31 molecules containing an azol group are separately classified by similarity, and although both classifications are diffuse, they are shown to be correlated, indicating that an appropriate selection for aggregation predictors and packing descriptors had been chosen. Finally, given an isolated molecule, even a new theoretical one, the above correlation allows the prediction of the packing descriptors of the probable crystal that this molecule would form, in particular, the crystal cell dimensions with an average relative error of 18%.