IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules
Abstract The construction of predictive models in molecular science increasingly relies on large, high-quality datasets. Synthetic data generation is becoming a foundational strategy for advancing model accuracy and enabling fast discovery workflows. To support the development of structure elucidati...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05729-8 |
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| _version_ | 1849344022458400768 |
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| author | Federico Zipoli Marvin Alberts Teodoro Laino |
| author_facet | Federico Zipoli Marvin Alberts Teodoro Laino |
| author_sort | Federico Zipoli |
| collection | DOAJ |
| description | Abstract The construction of predictive models in molecular science increasingly relies on large, high-quality datasets. Synthetic data generation is becoming a foundational strategy for advancing model accuracy and enabling fast discovery workflows. To support the development of structure elucidation and spectral property prediction models, we present a comprehensive synthetic dataset of infrared (IR) and nuclear magnetic resonance (NMR) spectra for a diverse ensemble of organic molecules. The data were generated using a hybrid computational approach that integrates molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and machine learning (ML) models. The dataset primarily consists of IR spectra for 177,461 molecules, derived from long-timescale MD simulations with ML-accelerated dipole moment predictions. In addition, it includes a smaller subset of 1H-NMR and 13C-NMR chemical shifts for 1,255 molecules. This unique combination of spectral data offers a valuable resource for benchmarking and validating computational methodologies, developing and enhancing artificial intelligence (AI) models for molecular property prediction, and facilitating the interpretation of experimental spectroscopic results. The dataset is publicly available through Zenodo, encouraging its broad utilization within the scientific community. |
| format | Article |
| id | doaj-art-c66dc7f5d6974acaab8ef68f8891e424 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-c66dc7f5d6974acaab8ef68f8891e4242025-08-20T03:42:47ZengNature PortfolioScientific Data2052-44632025-08-0112111410.1038/s41597-025-05729-8IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic moleculesFederico Zipoli0Marvin Alberts1Teodoro Laino2IBM Research EuropeIBM Research EuropeIBM Research EuropeAbstract The construction of predictive models in molecular science increasingly relies on large, high-quality datasets. Synthetic data generation is becoming a foundational strategy for advancing model accuracy and enabling fast discovery workflows. To support the development of structure elucidation and spectral property prediction models, we present a comprehensive synthetic dataset of infrared (IR) and nuclear magnetic resonance (NMR) spectra for a diverse ensemble of organic molecules. The data were generated using a hybrid computational approach that integrates molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and machine learning (ML) models. The dataset primarily consists of IR spectra for 177,461 molecules, derived from long-timescale MD simulations with ML-accelerated dipole moment predictions. In addition, it includes a smaller subset of 1H-NMR and 13C-NMR chemical shifts for 1,255 molecules. This unique combination of spectral data offers a valuable resource for benchmarking and validating computational methodologies, developing and enhancing artificial intelligence (AI) models for molecular property prediction, and facilitating the interpretation of experimental spectroscopic results. The dataset is publicly available through Zenodo, encouraging its broad utilization within the scientific community.https://doi.org/10.1038/s41597-025-05729-8 |
| spellingShingle | Federico Zipoli Marvin Alberts Teodoro Laino IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules Scientific Data |
| title | IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules |
| title_full | IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules |
| title_fullStr | IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules |
| title_full_unstemmed | IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules |
| title_short | IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules |
| title_sort | ir nmr multimodal computational spectra dataset for 177k patent extracted organic molecules |
| url | https://doi.org/10.1038/s41597-025-05729-8 |
| work_keys_str_mv | AT federicozipoli irnmrmultimodalcomputationalspectradatasetfor177kpatentextractedorganicmolecules AT marvinalberts irnmrmultimodalcomputationalspectradatasetfor177kpatentextractedorganicmolecules AT teodorolaino irnmrmultimodalcomputationalspectradatasetfor177kpatentextractedorganicmolecules |