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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of chemical information and modeling 2023-03, Vol.63 (5), p.1462-1471
Hauptverfasser: McNaughton, Andrew D., Joshi, Rajendra P., Knutson, Carter R., Fnu, Anubhav, Luebke, Kevin J., Malerich, Jeremiah P., Madrid, Peter B., Kumar, Neeraj
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.2c01662