  Chiral molecule assignation is crucial for asymmetric catalysis, functional
materials, and the drug industry. The conventional approach requires
theoretical calculations of electronic circular dichroism (ECD) spectra, which
is time-consuming and costly. To speed up this process, we have incorporated
deep learning techniques for the ECD prediction. We first set up a large-scale
dataset of Chiral Molecular ECD spectra (CMCDS) with calculated ECD spectra. We
further develop the ECDFormer model, a Transformer-based model to learn the
chiral molecular representations and predict corresponding ECD spectra with
improved efficiency and accuracy. Unlike other models for spectrum prediction,
our ECDFormer creatively focused on peak properties rather than the whole
spectrum sequence for prediction, inspired by the scenario of chiral molecule
assignation. Specifically, ECDFormer predicts the peak properties, including
number, position, and symbol, then renders the ECD spectra from these peak
properties, which significantly outperforms other models in ECD prediction, Our
ECDFormer reduces the time of acquiring ECD spectra from 1-100 hours per
molecule to 1.5s.
