Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties

Accurate
understanding of ultraviolet–visible (UV–vis)
spectra is critical for the high-throughput synthesis of compounds
for drug discovery. Experimentally determining UV–vis spectra
can become expensive when dealing with a large quantity of novel compounds.
This provides us an opportunity to drive computational advances in
molecular property predictions using quantum mechanics and machine
learning methods. In this work, we use both quantum mechanically (QM)
predicted and experimentally measured UV–vis spectra as input
to devise four different machine learning architectures, UVvis-SchNet,
UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance
of each method. We find that the UVvis-MPNN model outperforms the
other models when using optimized 3D coordinates and QM predicted
spectra as input features. This model has the highest performance
for predicting UV–vis spectra with a training RMSE of 0.06
and validation RMSE of 0.08. Most importantly, our model can be used
for the challenging task of predicting differences in the UV–vis
spectral signatures of regioisomers.