Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning

Abstract

Vibrational Circular Dichroism (VCD) spectra often differ strongly from one conformer to another, even within the same absolute configuration of a molecule. Simulated molecular VCD spectra typically require expensive quantum chemical calculations for all conformers to generate a Boltzmann averaged total spectrum. This paper reports whether machine learning (ML) can partly replace these quantum chemical calculations by capturing the intricate connection between a conformer geometry and its VCD spectrum. Three hypotheses concerning the added value of ML are tested. First, it is shown that for a single stereoisomer, ML can predict the VCD spectrum of a conformer from solely the conformer geometry. Second, it is found that the ML approach results in important time savings. Third, the ML model produced is unfortunately hardly transferable from one stereoisomer to another.

Modelling solute–solvent interactions in VCD spectra analysis with the micro-solvation approach

“…Recent advances in the application of genetic algorithms (GAs) and machine learning (ML) for VCD spectra analysis may also help addressing solvation problems to a certain degree. 74–77 GAs tackled computational uncertainties in computed conformer weights by finding better conformer weights through fitting of the computed spectra to the experimental data. The examples, however, did not consider strong solute–solvent interactions.…”