Electron density learning of non-covalent systems. Issue 41 (17th September 2019)

• Record Type: Journal Article
• Authors: Fabrizio, Alberto; Grisafi, Andrea; Meyer, Benjamin; Ceriotti, Michele; Corminboeuf, Clemence
• Is Part Of: Chemical science. Volume 10:Issue 41(2019)
• Page Start: 9424
• Page End: 9432
• Publication Date: 2019-09-17
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c9sc02696g
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Machine learning model of the electron densities for analyzing non-covalent interaction patterns in peptides. Abstract : 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 ρ ( 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. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying ρ ( r ) in a diverse ensemble of sidechain–sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.