The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real world data
Open accessPreprint — published as 10.1186/s13321-025-00960-2 · Paper page
Abstract
In this study, we propose a neural network based approach to analyze IR spectra and detect the presence of functional groups. Our neural network architecture is based on the concept of learning split representations. We demonstrate that our method achieves favorable validation performance using the NIST dataset. Furthermore, by incorporating additional data from the open-access research data repository Chemotion, we show that our model improves the classification performance for nitriles and amides. We could reach an improved performance of our model referring to previous models with F1 scores for identifying 17 functional groups above 0.7.
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Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Infrared Spectroscopy: Fundamentals and Applications
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Interpretation of Infrared Spectra, A Practical Approach
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MoleculeNet: a benchmark for molecular machine learning
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Metadata
- Authors
- Punjabi, D., Huang, Y.-C., Holzhauer, L., Tremouilhac, P., Friederich, P., Jung, N., & Bräse, S.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- ChemRxiv
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-08-15
- ISSN
- 2573-2293 (ISSN-L)
- OpenAlex
- W4401649544
- Cited by
- 1
- References
- 23
- Citations by year
- 2026: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- ChemRxiv (acceptedVersion, cc-by)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- infrared spectroscopy, functional group identification, artificial neural networks, nitriles, amides, organic compounds, spectral classification
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- The paper
- Punjabi, D., Huang, Y.-C., Holzhauer, L., Tremouilhac, P., Friederich, P., Jung, N., & Bräse, S. (2024). Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real world data. ChemRxiv. https://doi.org/10.26434/chemrxiv-2024-7twhh
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
