Machine learning the electron density

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density rho(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. A collaboration between the Laboratory of Computational Science and Modelling and the Laboratory for Computational Molecular Design developed a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. This model has been trained on a database of small molecular fragment, and used to predict the electronic density of polypeptides, and to compute electrostatic interactions between fragments, that contribute to the stability of proteins and the interactions between enzymes and drug molecules. The study has been published on the journal Chemical Science.