Can deep learning search for exceptional chiroptical properties? The halogenated [6]helicene case
Author(s)
Type
Journal Article
Abstract
The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial-error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the Rmax for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted Rmax obtaining excellent correlation among the values obtained experimentally and the predicted ones.
Date Issued
2024-12-02
Date Acceptance
2024-09-01
Citation
Angewandte Chemie International Edition, 2024, 63 (49)
ISSN
1433-7851
Publisher
Wiley
Journal / Book Title
Angewandte Chemie International Edition
Volume
63
Issue
49
Copyright Statement
© 2024 The Author(s). Angewandte Chemie International Edition published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39329214
Subjects
DFT calculations
[6]helicene
chiroptical properties
deep learning
rotatory strength
Publication Status
Published
Coverage Spatial
Germany
Article Number
e202409998
Date Publish Online
2024-09-27