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Urbina F, Batra K, Luebke KJ, White JD, Matsiev D, Olson LL, Malerich JP, Hupcey MAZ, Madrid PB, Ekins S. UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra. Anal Chem 2021; 93:16076-16085. [PMID: 34812602 DOI: 10.1021/acs.analchem.1c03741] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we present UV-adVISor as a new computational tool for predicting the UV-Vis spectra from a molecule's structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem. We utilized Long-Short Term Memory and attention-based neural networks with Extended Connectivity Fingerprint Diameter 6 or molecule SMILES to generate predictive models for the UV spectra. We have produced two spectrum datasets (dataset I, N = 949, and dataset II, N = 2222) using different compound collections and spectrum acquisition methods to train, validate, and test our models. We evaluated the prediction accuracy of the complete spectra by the correspondence of wavelengths of absorbance maxima and with a series of statistical measures (the best test set median model parameters are in parentheses for model II), including RMSE (0.064), R2 (0.71), and dynamic time warping (DTW, 0.194) of the entire spectrum curve. Scrambling molecule structures with the experimental spectra during training resulted in a degraded R2, confirming the utility of the approaches for prediction. UV-adVISor is able to provide fast and accurate predictions for libraries of compounds.
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Affiliation(s)
- Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Kushal Batra
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.,Computer Science, NC State University, Raleigh, North Carolina 27606, United States
| | - Kevin J Luebke
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Jason D White
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Daniel Matsiev
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Lori L Olson
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Jeremiah P Malerich
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Maggie A Z Hupcey
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
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Langhals H, Schlücker T, Ernst H. Novel Spectrophotometric Protocol for the Long-Term Characterization of the Hue of Artwork. ANAL LETT 2017. [DOI: 10.1080/00032719.2017.1281936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Heinz Langhals
- Department of Chemistry, LMU University of Munich, Munich, Germany
| | | | - Helena Ernst
- Department of Conservation and Restoration, TUM Technical University of Munich, Munich, Germany
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