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

McNaughton, Andrew D.; Joshi, Rajendra Prashad; Knutson, Carter R.; Fnu, Anubhav; Luebke, Kevin J.; Malerich, Jeremiah P.; Madrid, Peter B.; Kumar, Neeraj

doi:10.1021/acs.jcim.2c01662

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

Journal Article · Mon Feb 27 00:00:00 UTC 2023 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.2c01662· OSTI ID:1964168

^[1]; Joshi, Rajendra Prashad ^[1]; ^[1]; Fnu, Anubhav ^[1]; Luebke, Kevin J. ^[2]; Malerich, Jeremiah P. ^[2]; ^[2]; ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
SRI International, Menlo Park, CA (United States)

Accurate understanding of Ultraviolet–visible (UV–Vis) spectra is critical for highthroughput design of compounds for drug discovery. Experimentally determining UV–Vis spectra can become expensive when dealing with a large quantity of novel molecules. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning. In this work, we use both Quantum Mechanically (QM) predicted and measured UV–Vis spectra as input to modify four different machine learning architectures: UVvis-SchNet, UVvis- DTNN, UVvis-Transformer, and UVvis-MPNN. Here 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 UVVisible spectra with a training RMSE of 0.06 and validation RMSE of 0.08.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: Defense Advanced Research Projects Agency (DARPA); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1964168

Report Number(s):: PNNL-SA-171633

Journal Information:: Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 5 Vol. 63; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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