Neural Network Prediction of Secondary Structure in Crystals: Hydrogen-Bond Systems in Pyrazole Derivatives

Abstract

With the purpose of predicting by neural networks some structural properties of crystals, in particular, the types of secondary structure built by hydrogen bonds, 46 molecules, containing the pyrazole ring, have been codified in vectors of equal dimension. Looking for an unbiased codification, we selected the components of these vectors from the one-dimensional Fourier transform of the corresponding three-dimensional molecular charge distribution. Matrices of similarity and similarity maps of Kohonen's trained networks have allowed classification of the molecules, as a previous step before prediction of their hydrogen-bond system. Thus, we have worked under the hypothesis that this molecular codification contains information relevant to the structural level in crystals. The classes obtained show correlation with the previously known secondary structure of the corresponding crystals. Then, we have achieved, by means of training a neural network with some molecular vectors supervised by their coded secondary structure, a significant prediction of the type of secondary structure for the rest of the molecules. This molecular codification seems also to account for other noncovalent molecular interactions involved in the packing.