Published October 28, 2019 | Version v1
Dataset Open

Electron density learning of non-covalent systems

  • 1. Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
  • 2. Laboratory of Computational Science and Modeling, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Contact person

Description

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.

Files

File preview

files_description.md

All files

Files (119.3 MiB)

Name Size
md5:a6da6e0ddaeed2586316717659854a44
693 Bytes Preview Download
md5:6a4ff66087fe9f99af8b647c470b40a7
504.4 KiB Download
md5:b5792d607bd75f307232fdcbfcc40eee
15.6 MiB Download
md5:d96cfc997cebe1ca4c7b92be416b1359
552.6 KiB Download
md5:253e172af6723799da7c10ca934e927d
102.7 MiB Preview Download
md5:3008ce8f7bfb052867ab791a26fe060c
880 Bytes Preview Download

References

Journal reference
A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, C. Corminboeuf, Chem. Sci., Advance Article (2019), doi: 10.1039/C9SC02696G

Journal reference
A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, C. Corminboeuf, Chem. Sci., Advance Article (2019)