Awesome-SpectraAI-Resources

KO | EN

gitlite — search

#react #typescript #llm #go #golang #machine-learning #deep-learning #docker #windows #cli #security #ios #openai #open-source

Awesome-SpectraAI-Resources

(★ 25)

✨✨ Latest Advances on AI for Spectra Data Analysis (SpectraAI)

Awesome-SpectraAI-Resources Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Awesome-SpectraAI-Resources
<div align="center">
  <img width="690" height="83" alt="Awesome SpectraAI Resources" src="https://github.com/user-attachments/assets/5ecb4f23-cb12-46c8-bd8b-5f13481a5850" />
</div>

✨✨ A curated collection of resources on artificial intelligence for spectral data analysis, covering computational methods for mass spectrometry (MS), NMR, IR, and XRD data.

---

- [1. Mass Spectrometry (Small Molecules)](#1-mass-spectrometry-small-molecules)
- [2. Mass Spectrometry (Peptides)](#2-mass-spectrometry-peptides)
- [3. NMR Spectroscopy (Small Molecules)](#3-nmr-spectroscopy-small-molecules)
- [4. IR Spectroscopy (Small Molecules)](#4-ir-spectroscopy-small-molecules)
- [5. Multimodal Spectroscopy (Small Molecules)](#5-multimodal-spectroscopy-small-molecules)
- [6. X-ray Diffraction (XRD) (Crystals)](#6-x-ray-diffraction-xrd-crystals)

## 1. Mass Spectrometry (Small Molecules)

### 1.1 Forward Task (Molecule → Spectrum)  
*Computational approaches for predicting mass spectra from molecular structures*  
 
 | Paper Title & Link | Method Type | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [Inferring CID by Estimating Breakage Events and Reconstructing their Graphs](https://pubs.acs.org/doi/abs/10.1021/acs.analchem.3c04654) | GNN, Transformer | Analytical Chemistry, NeurIPS2023 | [![Star](https://img.shields.io/github/stars/coleygroup/ms-pred.svg?style=social&label=Star)](https://github.com/coleygroup/ms-pred)  |ICEBERG, [SCARF](https://papers.neurips.cc/paper_files/paper/2023/file/97d596ca21d0751ba2c633bad696cf7f-Paper-Conference.pdf)|
| [FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events](https://www.nature.com/articles/s41467-025-57422-4) | GNN | Nature Communication | [![Star](https://img.shields.io/github/stars/coleygroup/ms-pred.svg?style=social&label=Star)](https://github.com/BAMeScience/fiora)  | FIORA |
| [Efficiently predicting high resolution mass spectra with graph neural networks](https://proceedings.mlr.press/v202/murphy23a.html) | GNN | ICML2023 | [![Star](https://img.shields.io/github/stars/murphy17/graff-ms.svg?style=social&label=Star)](https://github.com/murphy17/graff-ms)  |GRAFF-MS |
| [CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification](https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.1c01465?ref=article_openPDF) | Machine Learning Model | analytical chemistry |  | CFM-ID 4.0, [project website](http://cfmid4.wishartlab.com/) |
| [Computational prediction of electron ionization mass spectra to assist in GC-MS compound identification](https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.6b01622?ref=article_openPDF) | Artificial Neural Network, Probabilistic Generative Model | analytical chemistry | | CFM-EI,old version of CFM-ID, [project website](http://cfmid4.wishartlab.com/) |
| [Tandem mass spectrum prediction for small molecules using graph transformers](https://doi.org/10.1038/s42256-024-00816-8) | Graph Transformer, Deep Learning Model | Nature Machine Intelligence | [![Star](https://img.shields.io/github/stars/Roestlab/massformer.svg?style=social&label=Star)](https://github.com/Roestlab/massformer) | MassFormer |
| [Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks](https://doi.org/10.1021/acscentsci.9b00085) | Neural Network, Graph-Convolutional Network | ACS Central Science | [![Star](https://img.shields.io/github/stars/brain-research/deep-molecular-massspec.svg?style=social&label=Star)](https://github.com/brain-research/deep-molecular-massspec) | NEIMS |
| [Mass Spectra Prediction with Structural Motif-based Graph Neural Networks](https://doi.org/10.1038/s41598-024-51760-x) | GNN, MLP, Graph Transformer | Scientific Reports |  | MoMS-Net |
| [Prediction of electron ionization mass spectra based on graph convolutional networks](https://doi.org/10.1016/j.ijms.2022.116817) | GCN, MLP | International Journal of Mass Spectrometry |  | Baojie Zhang mass spectra prediction |
|[QCxMS and QCEIMS related publications](https://xtb-docs.readthedocs.io/en/latest/qcxms_doc/qcxms_cites.html)|Born-Oppenheimer Molecular Dynamics|| [![Star](https://img.shields.io/github/stars/qcxms/QCxMS.svg?style=social&label=Star)](https://github.com/qcxms/QCxMS) |QCxMS, QCxMS2, QCEIMS, First Principles Calculation, [project website](https://xtb-docs.readthedocs.io/en/latest/qcxms_doc/qcxms_cites.html)|
|[3DMolMS: prediction of tandem mass spectra from 3D molecular conformations]()| 3D Molecular Network |Bioinformatics|[![Star](https://img.shields.io/github/stars/JosieHong/3DMolMS.svg?style=social&label=Star)](https://github.com/JosieHong/3DMolMS)|3DMolMs,[project website](https://koina.wilhelmlab.org/docs#post-/3dmolms_qtof/infer)|
|[Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization–Mass Spectrometry](https://pubs.acs.org/doi/10.1021/acs.analchem.2c02093?ref=pdf)| GNN |Analytical Chemistry|[![Star](https://img.shields.io/github/stars/thejonaslab/rassp-public.svg?style=social&label=Star)](https://github.com/thejonaslab/rassp-public)|rassp|
| [Towards First Principles Calculation of Electron Impact Mass Spectra of Molecules](https://onlinelibrary.wiley.com/doi/epdf/10.1002/anie.201300158)|Mass Spectroscopy computational therory| Angewandte International Edition Chemie|| theory introduction|

### 1.2 Inverse Task (Spectrum → Molecule)  
*AI methods for molecular identification and elucidation from mass spectra*  
 | Paper Title & Link | Method Type | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [An end-to-end deep learning framework for translating mass spectra to de-novo molecules](https://www.nature.com/articles/s42004-023-00932-3) |GRU, CNN |communications chemistry| [![Star](https://img.shields.io/github/stars/KavrakiLab/Spec2Mol.svg?style=social&label=Star)](https://github.com/KavrakiLab/Spec2Mol)  |Spec2mol |
| [Searching molecular structure databases with tandem mass spectra using CSI:FingerID](https://doi.org/10.1073/pnas.1509788112) | Machine Learning | Proceedings of the National Academy of Sciences | [project website](https://ww16.csi-fingerid.org/?sub1=20250830-1925-2033-949e-1db2a86ecef8) | CSI:FingerID |
| [Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra](https://doi.org/10.1093/bioinformatics/btac260) | DNN, Kernel-based Model, SVM | Bioinformatics | [project website](https://bio.informatik.uni-jena.de/software/sirius) | Deep kernel learning method |
| [CSU-MS $^2$ : A Contrastive Learning Framework for Cross-Modal Compound Identification from MS/MS Spectra to Molecular Structures](https://pubs.acs.org/doi/10.1021/acs.analchem.5c01594) | Contrastive Learning Framework, Transformer, GNN | Analytical Chemistry | [![Star](https://img.shields.io/github/stars/tingxiecsu/CSU-MS2.svg?style=social&label=Star)](https://github.com/tingxiecsu/CSU-MS2) | CSU-MS² |
| [MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra](https://www.mdpi.com/2218-273X/11/12/1793) | Transformer, Variational Autoencoder | Biomolecules  | [![Star](https://img.shields.io/github/stars/neilswainston/FragGenie.svg?style=social&label=Star)](https://github.com/neilswainston/FragGenie) | MassGenie, FragGenie, VAE-Sim |
| [JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data](https://doi.org/10.1093/bioinformatics/btaf354) | Joint Embedding Space Technique, GNN, MLP | Bioinformatics | [![Star](https://img.shields.io/github/stars/HassounLab/JESTR1.svg?style=social&label=Star)](https://github.com/HassounLab/JESTR1/) | JESTR |
| [Metabolite Identification through Machine Learning — Tackling CASMI Challenge Using FingerID](https://doi.org/10.3390/metabo3020484) | Machine Learning, Kernel-based approach, SVM | Metabolites |  | FingerID |
| [Annotating metabolite mass spectra with domain-inspired chemical formula transformers](https://www.nature.com/articles/s42256-023-00708-3) | Transformer, Neural Network, Contrastive Learning Model | Nature Machine Intelligence | [![Star](https://img.shields.io/github/stars/samgoldman97/mist.svg?style=social&label=Star)](https://github.com/samgoldman97/mist) | MIST |
| [Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS](https://doi.org/10.1038/s41587-025-02663-3) | Transformer, Foundation Model | Nature Biotechnology | [![Star](https://img.shields.io/github/stars/pluskal-lab/DreaMS.svg?style=social&label=Star)](https://github.com/pluskal-lab/DreaMS) | DreaMS, Foundation model |
| [Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra](https://doi.org/10.1038/s41587-020-0740-8) | DNN, SVMs | Nature Biotechnology | [![Star](https://img.shields.io/github/stars/boecker-lab/sirius-libs.svg?style=social&label=Star)](https://github.com/boecker-lab/sirius-libs) | CANOPUS |
| [Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa](https://doi.org/10.5702/massspectrometry.s0033) | Software, Substructure-based algorithm | Mass Spectrometry | [project website](www.emetabolomics.org/magma) | MAGMa |
| [DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra](http://arxiv.org/pdf/2502.09571v2) | Transformer, Discrete Graph Diffusion Model | arXiv | [![Star](https://img.shields.io/github/stars/coleygroup/DiffMS.svg?style=social&label=Star)](https://github.com/coleygroup/DiffMS) | DiffMS |
| [MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation](https://doi.org/10.1007/s11306-020-01726-7) | Artificial Neural Network | Metabolomics |  | MetFID |
| [MADGEN - MASS-SPEC ATTENDS TO DE NOVO MOLECULAR GENERATION](https://arxiv.org/abs/2501.01950) | Attention-based generative model, LSTM, CNN | arxiv| [![Star](https://img.shields.io/github/stars/HassounLab/MADGEN.svg?style=social&label=Star)](https://github.com/HassounLab/MADGEN) | MADGEN |
| [An end-to-end deep learning framework for translating mass spectra to de-novo molecules](https://doi.org/10.1038/s42004-023-00932-3) | Deep Learning architecture, Autoencoder | Communications Chemistry | [![Star](https://img.shields.io/github/stars/KavrakiLab/Spec2Mol.svg?style=social&label=Star)](https://github.com/KavrakiLab/Spec2Mol) | Spec2Mol |
| [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](http://arxiv.org/pdf/2507.06853v1) | Diffusion Model, Transformer | arXiv |  | DiffSpectra |
| [MS2Mol: A transformer model for illuminating dark chemical space from mass spectra](https://doi.org/10.26434/chemrxiv-2023-vsmpx-v3) | Transformer, Generative Model |  |  | MS2Mol |
| [Improved metabolite identification with MIDAS and MAGMa through MS/MS spectral dataset-driven parameter optimization](https://doi.org/10.1007/s11306-016-1036-3) | Machine Learning, Rule-based | Metabolomics | [![Star](https://img.shields.io/github/stars/savantas/MAGMa-plus.svg?style=social&label=Star)](https://github.com/savantas/MAGMa-plus) | MIDAS, MAGMa, CSI: FingerID |
| [Using Graph Neural Networks for Mass Spectrometry Prediction](http://arxiv.org/pdf/2010.04661v1) | GNN | arXiv |  | GNN-based models |
| [MSNovelist: de novo structure generation from mass spectra](https://doi.org/10.1038/s41592-022-01486-3) | Encoder-decoder neural network, RNN | Nature Methods | [![Star](https://img.shields.io/github/stars/meowcat/MSNovelist.svg?style=social&label=Star)](https://github.com/meowcat/MSNovelist) | MSNovelist |
| [Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks](https://pubs.acs.org/doi/10.1021/acs.analchem.0c01450) | Deep Neural Network | analytical chemistry | [![Star](https://img.shields.io/github/stars/hcji/DeepEI.svg?style=social&label=Star)](https://github.com/hcji/DeepEI) | DeepEI, FP Model, molecular fingerprint prediction |
| [Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification](https://doi.org/10.1021/acs.analchem.8b05405.s004) | Deep Neural Network |  | [![Star](https://img.shields.io/github/stars/hcji/DeepMASS.svg?style=social&label=Star)](https://github.com/hcji/DeepMASS) | DeepMASS, mass spectrum-based library match for Metabolite  |
| [Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library](https://www.nature.com/articles/s41467-023-39279-7)|Word2vec, HNSW|nature communications|[![Star](https://img.shields.io/github/stars/Qiong-Yang/FastEI.svg?style=social&label=Star)](https://github.com/Qiong-Yang/FastEI)|FastEI, spectrum simulation expansion, spectrum search for compoundidentification|
| [In silico fragmentation for computer assisted identification of metabolite mass spectra](https://doi.org/10.1186/1471-2105-11-148) | Combinatorial Fragmenter | BMC Bioinformatics |  | MetFrag, mass spectrum-based metabolite identification, [Project Link](http://msbi.ipb-halle.de/MetFrag/) |
| [MS2Query: reliable and scalable MS2 mass spectra-based analogue search](https://doi.org/10.1038/s41467-023-37446-4) |embedding-based chemical similarity predictors  | Nature Communications | [![Star](https://img.shields.io/github/stars/iomega/ms2query.svg?style=social&label=Star)](https://github.com/iomega/ms2query) | MS2Query, mass spectra analogue search, Project Link: [https://doi.org/10.5281/zenodo.6124553](https://doi.org/10.5281/zenodo.6124553) |

### 1.3 General Tools
 | Paper Title & Link | Feasible scene | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [matchms- processing and similarity evaluation of mass spectrometry data](https://joss.theoj.org/papers/10.21105/joss.02411) |raw mass spectra to pre- and post-processe  | The Journal of Open Source Software |[![Star](https://img.shields.io/github/stars/matchms/matchms.svg?style=social&label=Star)](https://github.com/matchms/matchms)  |python package|
| [MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00558-4) | compare tandem mass spectra | Journal of Cheminformatics| [![Star](https://img.shields.io/github/stars/matchms/ms2deepscore.svg?style=social&label=Star)](https://github.com/matchms/ms2deepscore) | |
| [Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships](https://doi.org/10.1371/journal.pcbi.1008724) | NLP-inspired Model, Word2Vec | PLOS Computational Biology  | [![Star](https://img.shields.io/github/stars/iomega/spec2vec.svg?style=social&label=Star)](https://github.com/iomega/spec2vec) | Spec2Vec |
| [Chemically informed analyses of metabolomics mass spectrometry data with Qemistree](https://www.nature.com/articles/s41589-020-00677-3) | Machine Learning, Tree-based approach | Nature chemical biology | [![Star](https://img.shields.io/github/stars/biocore/q2-qemistree.svg?style=social&label=Star)](https://github.com/biocore/q2-qemistree) | Qemistree |
| [MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics](https://doi.org/10.1038/s41467-024-48009-6) | Software Tool | Nature Communications | [![Star](https://img.shields.io/github/stars/xia-lab/MetaboAnalystR.svg?style=social&label=Star)](https://github.com/xia-lab/MetaboAnalystR) | MetaboAnalystR 4.0, LC-MS data processing, R package, Project Link: [MetaboAnalyst](https://www.metaboanalyst.ca/docs/Databases.xhtml) |
| [Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning](https://doi.org/10.1021/jacs.2c03631) | Decision Trees, Neural Network, LSTM | Journal of the American Chemical Society | [![Star](https://img.shields.io/github/stars/Ananikov-Lab/medusa.svg?style=social&label=Star)](https://github.com/Ananikov-Lab/medusa) | MEDUSA, mass spectrum analysis overall framework, Project Link: [project_link](https://ananikov-lab.github.io/medusa/) |
| [The METLIN small molecule dataset for machine learning-based retention time prediction](https://doi.org/10.1038/s41467-019-13680-7) | Deep Learning | Nature Communications |  | [project website](https://figshare.com/articles/dataset/The_METLIN_small_molecule_dataset_for_machine_learning-based_retention_time_prediction/8038913), retention time prediction, R package |
| [An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles](https://doi.org/10.1038/s41467-024-51433-3) | Deep Learning | Nature Communications | [![Star](https://img.shields.io/github/stars/yjdeng9/DeepMSProfiler.svg?style=social&label=Star)](https://github.com/yjdeng9/DeepMSProfiler) | DeepMSProfiler, disease-specific metabolic profiling |
| [Trackable and scalable LC-MS metabolomics data processing using asari](https://doi.org/10.1038/s41467-023-39889-1) | open-source software tool | Nature Communications | [![Star](https://img.shields.io/github/stars/shuzhao-li/asari.svg?style=social&label=Star)](https://github.com/shuzhao-li/asari) | asari, LC-MS data processing, Project Link: [https://pypi.org/project/asari-metabolomics/](https://pypi.org/project/asari-metabolomics/) |
|[Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa](https://pmc.ncbi.nlm.nih.gov/articles/PMC4321337/pdf/massspectrometry-3-Special_Issue_2-S0033.pdf)|Ranking algorithm| Mass Spectrom (Tokyo)||Peak list annotation, MAGMa|
|[MIST-CF: Chemical Formula Inference from Tandem Mass Spectra](https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c01082)|  Spectrum Transformers|Journal of Chemical Information and Modeling|[![Star](https://img.shields.io/github/stars/samgoldman97/mist-cf.svg?style=social&label=Star)](https://github.com/samgoldman97/mist-cf)|Mass Spectra Formula Inference|
|[Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra](https://www.nature.com/articles/s41587-020-0740-8)|Deep Neural Network|Nature Biotechnology|[![Star](https://img.shields.io/github/stars/boecker-lab/sirius-libs.svg?style=social&label=Star)](https://github.com/boecker-lab/sirius-libs)| compound class annotation, CANOPUS|

### 1.4 Datasets, Benchmark and Review
 | Name | Type | Venue | Website | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [MassSpecGym: A benchmark for the discovery and identification of molecules](https://proceedings.neurips.cc/paper_files/paper/2024/hash/c6c31413d5c53b7d1c343c1498734b0f-Abstract-Datasets_and_Benchmarks_Track.html) | Benchmark, Transformer, GNN | Advances in Neural Information Processing Systems | [![Star](https://img.shields.io/github/stars/pluskal-lab/MassSpecGym.svg?style=social&label=Star)](https://github.com/pluskal-lab/MassSpecGym) | MassSpecGym |
|[Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking](https://www.nature.com/articles/nbt.3597) | Mass Spectrum Database | Nature Biotechnology | | [Database link](https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp?redirect=auth) |
|[A versatile toolkit for drug metabolism studies with GNPS2: from drug development to clinical monitoring](https://www.nature.com/articles/s41596-025-01237-6)| Mass Spectrum Database | Nature Protocals | | [Database link](https://www.gnps2.org/homepage) |
| [MassBank: a public repository for sharing mass spectral data for life sciences](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/jms.1777) | Mass Spectrum Database | Journal of mass spectrometry |  | [Database link-EU](https://massbank.eu/MassBank/) [Database link-NA](https://mona.fiehnlab.ucdavis.edu/) | 
| [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](http://arxiv.org/pdf/2502.09897v1) | Neural architectures, ML-empowered solution | arXiv |  | review |
| [BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds](https://doi.org/10.1016/j.phytochem.2020.112427) | Spectral Library | Phytochemistry | [![Star](https://img.shields.io/github/stars/chalbori/bmdms-np.svg?style=social&label=Star)](https://github.com/chalbori/bmdms-np) | BMDMS-NP, ESI-MS/MS spectral library |
| [Annual Review of Analytical Chemistry Machine Learning in Small-Molecule Mass Spectrometry](https://doi.org/10.1146/annurev-anchem-071224-082157) | Sequence-based Model, Graph-based Model, Deep Learning Model, Siamese Neural Network, Natural Language Processing Model, Transformer, MLP, Random Forest, Bayesian Regularized Neural Network, XGBoost, Light Gradient Boosting Machine, CNN, SVR | Annual Review of Analytical Chemistry |  | Review Paper, SELFIES, Graph Neural Networks, MPNNs, SchNet, DimeNet++, ComENet, DeepMASS, MS2DeepScore, Spec2Vec, CLERMS, NEIMS, MassFormer, 3DMolMS, CFM-ID 4.0, SCARF, ICEBERG, Retip, METLIN-DLM, GNN-RT, DeepGCN-RT, RT-transformer, DNNpwa-TL, MetCCS, AllCCS, CCSBase, CCSP 2.0, DeepCCS, SigmaCCS, AllCCS2, SIRIUS, BUDDY,  mass spectrometry analysis |
| [Critical Assessment of Small Molecule Identification](http://casmi-contest.org/2016/proceedings.shtml.html) | Competation, Benchmark |Phytochemistry Letters ||CASMI 2012, 2013, 2014, 2016, 2017, 2022, [project website](http://casmi-contest.org/2016/challenges-cat2+3.shtml.html)|
| [Insights into predicting small molecule retention times in liquid chromatography using deep learning](https://doi.org/10.1186/s13321-024-00905-1) | SVM, Deep Learning, Transformer, GNN, RF, MLP, DNN, CNN, RNN | Journal of Cheminformatics | [![Star](https://img.shields.io/github/stars/LiuLime/PredRT_review_2024.svg?style=social&label=Star)](https://github.com/LiuLime/PredRT_review_2024.git) | CSI-FingerID, SIRIUS 4, MSNovelist, MassGenie, Smiles-Bert, Smiles transformer, Chemformer, PredRet, GCN, RGCN, MPNN, GIN, DNNpwa-TL, CMM-RT, 1D CNN-TL, AWD-LSTM, TransformerXL, MDC-ANN, retention time_GNN, QGeGNN, RT-transformer, mt-QSRR, MultiConditionRT, HighResNPS, GNN-RT-TL, Retip, SMRT, GNN-TL,  review paper |
| [Quantum chemical electron impact mass spectrum prediction for de novo structure elucidation: Assessment against experimental reference data and comparison to competitive fragmentation modeling](https://doi.org/10.1002/qua.25460) | Quantum Chemical Model, Expert system, Spectrum Calculator | International Journal of Quantum Chemistry |  | QCEIMS, CFM-EI, comparison between first principle simulation and expert system |
| [FragHub: A Mass Spectral Library Data Integration Workflow](https://doi.org/10.1021/acs.analchem.4c02219.s001) | Workflow | Analytical Chemistry  | [![Star](https://img.shields.io/github/stars/eMetaboHUB/FragHub.svg?style=social&label=Star)](https://github.com/eMetaboHUB/FragHub) | FragHub, mass spectral lib integration |
| [Evaluation of the performance of a tandem mass spectral library with mass spectral data extracted from literature](https://doi.org/10.1002/dta.341) | Database | Drug Testing and Analysis |  | MSforID, mass spectrum analysis |
| [Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods](https://pubs.acs.org/doi/10.1021/jasms.3c00059) | Quantum Chemistry, Machine Learning, Algorithm |  |  | QCEIMS, CFM-EI, NEIMS comparison |
| [Computational mass spectrometry for small-molecule fragmentation](https://doi.org/10.1016/j.trac.2013.09.008) | General models of fragmentation, Simulation Software, Fragmentation Prediction Software, Machine Learning, Heuristic Method, Combination of MetFrag and spectral library search, Classifier, Kernel-based method, Combinatorial Optimization Model | TrAC Trends in Analytical Chemistry |  | DENDRAL, Mass Frontier 4, Mass Frontier 6, ACD Fragmenter, ISIS, MetFrag, MetFusion, Varmuza feature-based classification approach, Heinonen et al. kernel-based approach, Fragmentation Trees, mass spectrometry fragmentation, review paper |
| [Searching molecular structure databases using tandem MS data: are we there yet?](https://doi.org/10.1016/j.cbpa.2016.12.010) | Automated Method, Machine Learning | Current Opinion in Chemical Biology |  | CFM-ID, MetFrag, MAGMa, FingerID, CSI:FingerID, MetFrag2.2, MAGMa+, MSFINDER, MIDAS, IOKR version of CSI:FingerID, metabolite identification, review paper |
| [Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry](https://doi.org/10.1002/mas.21602) | Unsupervised machine learning | Mass Spectrometry Reviews |  | PCA, IMS data analysis,  Project Link: [BioMap](https://www.ms-imaging.org/biomap/), review paper |
|[Machine Learning in Small-Molecule Mass Spectrometry](https://www.annualreviews.org/content/journals/10.1146/annurev-anchem-071224-082157)| GNN, Transformer, Machine learning|Annual Review of Analytical Chemistry |  || |review paper|
|[NIST20 ](https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:distributors)|data library| | |[project website](https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:distributors), purchase required|
|[NPLIB1](https://github.com/samgoldman97/mist)|dataset,benchmark| | |[data source](https://github.com/samgoldman97/mist), deriving from [CANOPUS](https://www.nature.com/articles/s41587-020-0740-8) by filtering to [M+H] spectra |
|[GeMS](https://huggingface.co/datasets/roman-bushuiev/GeMS)| dataset| | |dataset collected by [Dreams](https://www.nature.com/articles/s41587-025-02663-3), LC-MS, 714 million MS/MS spectra|
| [MSnLib: efficient generation of open multi-stage fragmentation mass spectral libraries](https://www.nature.com/articles/s41592-025-02813-0) | Spectral Library |  Nature Methods |  |  MSnLib |

### 1.5 Spectrum Databases (API Usage)
| Name & Link | Request Type | Description | Return information | API_link |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [GNPS](https://external.gnps2.org/gnpslibraryjson) | html | This gets you all spectra but without peaks. | Spectrum Metadata | [json](https://external.gnps2.org/gnpslibraryjson) |
| [MoNA](https://mona.fiehnlab.ucdavis.edu/documentation/query) | curl | Search for Mass Spectrum | | |

## 2. Mass Spectrometry (Peptides)

### 2.1 Forward Task (Peptides → Spectrum)  
*Computational methods for predicting peptides mass spectra*  
 | Paper Title & Link | Method Type | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network](https://pubs.acs.org/doi/10.1021/acs.analchem.9b04867) |CNN|Analytical Chemistry 2020| [![Star](https://img.shields.io/github/stars/lkytal/PredFull.svg?style=social&label=Star)](https://github.com/lkytal/PredFull)  |PredFull |
| [Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning](https://www.nature.com/articles/s41592-019-0426-7) |Transformer|Nature Methods 2019| [![Star](https://img.shields.io/github/stars/kusterlab/prosit.svg?style=social&label=Star)](https://github.com/kusterlab/prosit/?tab=readme-ov-file)  |Prosit |
| [High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis](https://www.nature.com/articles/s41592-019-0427-6) |LSTM|Nature Methods 2019| [![Star](https://img.shields.io/github/stars/verilylifesciences/deepmass/tree/master/prism.svg?style=social&label=Star)](https://github.com/verilylifesciences/deepmass/tree/master/prism)  |DeepMass:Prism |

### 2.2 Inverse Task (Spectrum → Peptides)  
*AI approaches for peptides identification and quantification*  
 | Paper Title & Link | Method Type | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments](https://www.nature.com/articles/s42256-025-01019-5) |Transformer, diffusion model |nature machine intelligence| [![Star](https://img.shields.io/github/stars/instadeepai/InstaNovo.svg?style=social&label=Star)](https://github.com/instadeepai/InstaNovo)  |InstaNovo |
| [De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model](https://www.biorxiv.org/content/10.1101/2022.02.07.479481v2) |Transformer|ICML 2022| [![Star](https://img.shields.io/github/stars/Noble-Lab/casanovo.svg?style=social&label=Star)](https://github.com/Noble-Lab/casanovo)  |CasaNovo |
| [Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo](https://www.biorxiv.org/content/10.1101/2024.10.19.619186v1) |Transformer, Database|ICLR 2025| — |SearchNovo |
| [De novo peptide sequencing by deep learning](https://www.pnas.org/doi/10.1073/pnas.1705691114) |CNN, LSTM|PNAs 2017| [![Star](https://img.shields.io/github/stars/nh2tran/DeepNovo.svg?style=social&label=Star)](https://github.com/nh2tran/DeepNovo)  |deepNovo |
| [Computationally Instrument-Resolution-independent De Novo Peptide Sequencing for High-Resolution Devices](https://www.nature.com/articles/s42256-021-00304-3) |CNN, LSTM|Nature Machine Intelligence 2021| — |PointNovo |
| [AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information](https://arxiv.org/abs/2403.07013) |Transformer|NeurIPS 2024| [![Star](https://img.shields.io/github/stars/Westlake-OmicsAI/adanovo_v1.svg?style=social&label=Star)](https://github.com/Westlake-OmicsAI/adanovo_v1)  |AdaNovo |
| [Accurate de novo peptide sequencing using fully convolutional neural networks](https://link.springer.com/article/10.1038/s41467-023-43010-x) | CNN | Nature Communications 2023| [![Star](https://img.shields.io/github/stars/lkytal/PepNet.svg?style=social&label=Star)](https://github.com/lkytal/PepNet)  |PepNet |
| [A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data](https://www.nature.com/articles/s41592-025-02718-y) | Transformer | Nature Method 2025| [![Star](https://img.shields.io/github/stars/Noble-Lab/cascadia.svg?style=social&label=Star)](https://github.com/Noble-Lab/cascadia)  |cascadia |
| [Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry](https://doi.org/10.1038/s42256-024-00960-1) | Transformer | Nature machine intelligence 2025| [![Star](https://img.shields.io/github/stars/yonghanyu/DeepSearch.svg?style=social&label=Star)](https://github.com/yonghanyu/DeepSearch)  |Deepsearch, database search|
| [π-PrimeNovo: an accurate and efficient nonautoregressive deep learning model for de novo peptide sequencing](https://www.nature.com/articles/s41467-024-55021-3) | Transformer | Nature Communications 2025| [![Star](https://img.shields.io/github/stars/PHOENIXcenter/pi-PrimeNovo.svg?style=social&label=Star)](https://github.com/PHOENIXcenter/pi-PrimeNovo)  |π-PrimeNovo|

### 2.3 Datasets, Benchmark and Review
 | Paper Title & Link | Method Type | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics](https://www.nature.com/articles/s41467-022-34904-3) | Benchmark | Nature communications |  [![Star](https://img.shields.io/github/stars/Westlake-OmicsAI/adanovo_v1.svg?style=social&label=Star)](https://github.com/MannLabs/alphapeptdeep) |  AlphaPeptDeep |
| [NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics](https://arxiv.org/abs/2406.11906) | Benchmark |NeurIPS 2024| [![Star](https://img.shields.io/github/stars/Westlake-OmicsAI/NovoBench.svg?style=social&label=Star)](https://github.com/Westlake-OmicsAI/NovoBench)  |NovoBench |

### 2.4 General Tools
 | Paper Title & Link | Feasible scene | Venue | Code | Notes |
|--------------------|------------------|--------------------|--------------------|------------------------------------------|
| [MSBooster: improving peptide identification rates using deep learning-based features](https://www.nature.com/articles/s41467-023-40129-9#Fig1) | Rating  between LC-MS/MS spectra and Piptide | Nature communications |[![Star](https://img.shields.io/github/stars/matchms/matchms.svg?style=social&label=Star)](https://raw.githubusercontent.com/AIMS-Lab-HKUSTGZ/Awesome-SpectraAI-Resources/main/[https:/github.com/matchms/matchms](https:/github.com/Nesvilab/MSBooster))  |python package|
---

## 3. NMR Spectroscopy (Small Molecules)

### 3.1 Forward Task (Molecule → Spectrum)

| Paper Title & Link                                                                                                                                         | Method Type                    | Venue/Year      | Data Source             | Metric       | Code / Data                                                                                                                                     | Notes                                               |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------ | --------------- | ----------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- |
| [Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations](https://arxiv.org/pdf/2510.05623) | - | preprint | ^1H/^13C NMR spectra | - | — |  |
| [Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts]([https://www.nature.com/articles/s41467-025-57721-w](https://www.nature.com/articles/s43588-025-00783-z)) | Transformer | Nature Computional Science 2025 | 3Dstructure to NMR |  | [Zenodo](https://github.com/Colin-Jay/NMRNet) | 
| [Deep Learning Network for NMR Spectra Reconstruction in Time-Frequency Domain and Quality Assessment](https://www.nature.com/articles/s41467-025-57721-w) | Transformer + CNN hybrid | Nature Communications 2025 | 1D/2D NMR time-series | RMSE (dB) | SpectraNet · Zenodo | Time-frequency domain reconstruction; spectral quality scoring
| [GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts]([https://arxiv.org/pdf/2408.15681](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00927-9)) | Graph + Transformer  |      |   |    | [GT-NMR]([https://github.com/Colin-Jay/NMRNet](https://github.com/Anan-Wu-XMU/GT-NMR/)) ·                                                    | |
| [PROSPRE: Solvent-aware ^1H NMR chemical shift prediction using deep learning](https://www.mdpi.com/2218-1989/14/5/290) | Deep Learning + Solvent-aware   | Metabolites 2024 | Experimental NMR data   | MAE < 0.10 ppm | —                                                                                                                                               | Trained on large-scale solvent-specific datasets    |
[Prediction of chemical shift in NMR: A review](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/mrc.5234)                                                                                                              | Review                         | —               | —                       | —            | —                                                                                                                                               | Survey of methods & datasets                        |
| [iShiftML: Highly Accurate Prediction of NMR Chemical Shifts](https://doi.org/10.1002/pca.3292)                                                                                                               | Hybrid ML + QM                 | —               | QM descriptors          | < 0.2 ppm    | —                                                                                                                                               | Fast inference; QM features required                |
| [TransPeakNet: Solvent-Aware 2D NMR Prediction via Multi-Task Pre-Training and Unsupervised Learning](https://github.com/siriusxiao62/2dNMR)      | GNN + Multitask                | Chem paper 2023 | Graph-based input       | 2D spectra   | [Code](https://github.com/siriusxiao62/2dNMR) · [Data](https://drive.google.com/drive/folders/1wQxk7mnIwi5aAGaF34_hk7xo6IeEh-IE?usp=drive_link) | 1D→2D solvent-aware prediction                      |
| [NMR shift prediction from small data quantities](https://link.springer.com/article/10.1186/s13321-023-00785-x)                                                                                                            | ML                             | —               | NMRShiftDB2             | MAE (ppm)    | —                                                                                                                                               | Small-data learning                                 |
| [NMR-spectrum prediction for dynamic molecules](https://pubs.aip.org/aip/jcp/article/158/19/194108/2891394)                                                                                                              | ML-Dynamics                    | —               | Simulated ensembles     | Time-avg ppm | —                                                                                                                                               | Conformational averaging                            |
| [Machine learning in NMR spectroscopy](https://www.sciencedirect.com/science/article/pii/S0079656525000196)                                                                                                                       | Review (DL)                    | —               | NMRShiftDB2             | —            | —                                                                                                                                               | Multitask trends & outlook                          |
| [A framework for automated structure elucidation from routine NMR spectra](https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc04105c)                                                                                   | ML-based structure elucidation | Chem. Sci. 2021 | 1D ^1H/^13C NMR spectra | Top-10 accuracy | —                                                                                                                                               | ML framework for automated structure elucidation    |
| [Deep learning enabled ultra-high quality NMR chemical shift prediction from spin echo spectra](https://pubs.rsc.org/en/content/articlehtml/2024/sc/d4sc04742g) | Deep Learning + Signal Processing | Science 2024    | Spin echo NMR spectra   | MAE (ppm)    | —                                                                                                                                               | High-resolution chemical shift prediction          |

---

### 3.2 Inverse Task (Spectrum → Molecule)

| Paper Title & Link                                                                                                                                                                                                                                                                                                           | Method Type                     | Venue/Year                | Input                  | Metric             | Code / Data | Notes                                                                   |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------- | ------------------------- | ---------------------- | ------------------ | ----------- | ----------------------------------------------------------------------- |
| [Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra](https://openreview.net/pdf/c657f3724243fdae6e7cfbbfd793eba4aae2eeb6.pdf) | - | openreview | ^1H/^13C NMR spectra | - | — | DiT Based 3D molecular generation |
| [NMRMind: A Transformer-Based Model Enabling the Elucidation from Multidimensional NMR to Structures](https://pubs.acs.org/doi/10.1021/acs.analchem.5c03783) | Transformer | Analytical Chemistry 2025 | ^1H/^13C NMR spectra | Top-k accuracy | — |  |
| [DIFFNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation](https://arxiv.org/pdf/2507.08854) |  | Arxiv 2025 | ^1H/^13C NMR spectra | Top-k accuracy | — | Diffusion Based Generation |
| [A Transformer Based Generative Chemical Language AI Model for Structural Elucidation of Organic Compounds](https://arxiv.org/abs/2410.14719) | Transformer-based Generative AI | Arxiv 2024                | IR, UV, ^1H NMR spectra | Top-15 accuracy      | —           | End-to-end structure elucidation via chemical language modeling         |
| [NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization](https://arxiv.org/abs/2509.00640) | Hybrid Spectral Matching + Physics-guided Optimization | Arxiv 2025                | ^1H/^13C NMR spectra    | Top-1 accuracy      | —           | Combines spectral matching with physics-guided optimization             |
| [Accurate and Efficient Structure Elucidation from Routine One-Dimensional NMR Spectra Using Multitask Machine Learning](https://arxiv.org/pdf/2408.08284)                                                                                                                                                | CNN + Transformer (multitask)   | Arxiv 2024                | 1D spectra             | Top-1 / Top-k      | —           | High accuracy with 1D only                                              |
| [Learning the Language of NMR Structure Elucidation from NMR Spectra Using Transformer Models](https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64d5e4ccdfabaf06ff1763ef/original/learning-the-language-of-nmr-structure-elucidation-from-nmr-spectra-using-transformer-models.pdf) | Transformer (sequence modeling) | ChemRxiv 2023             | 1D spectra             | —                  | —           | Treats NMR as a language; structure reasoning                           |
| [DeepSAT: Learning Molecular Structures from NMR Data](https://link.springer.com/article/10.1186/s13321-023-00738-4)                                                                                                                                                                                                                                                                       | Multimodal DL                   | —                         | NMR spectra            | Structure accuracy | —           | Uses NPAtlas, NPASS, GNPS etc.; multimodal molecular structure learning |                             |
| [Bayesian approach to structural elucidation with crystalline-state SSNMR](https://arxiv.org/abs/1909.00870)                                                                                                                                                                                              | Bayesian / probabilistic        | arXiv 2019                | Solid-state NMR        | Top-5              | —           | Requires crystal info                                                   |
| [Deep RL + GCN for NMR inverse problem](https://pubs.acs.org/doi/10.1021/acs.jpclett.2c00624)                                                                                                                                                                                                    | RL (MCTS) + GCN                 | J. Phys. Chem. Lett. 2022 | Shift table            | Top-3              | —           | Effective for small molecules                                           |

### 3.3 Dataset
| Name & Link                                                                                                                                                                          | Type                        | Venue/Year            | Size / Modality                       | Real / Sim          | Download / Access                                      |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------- | --------------------- | ------------------------------------- | ------------------- | ------------------------------------------------------ |
| [Multimodal Spectroscopic Dataset (includes NMR)](https://proceedings.neurips.cc/paper_files/paper/2024/file/e38e60b33bb2c6993e0865160cdb5cf1-Paper-Datasets_and_Benchmarks_Track.p) | Benchmark (multi-spectra)   | NeurIPS 2024          | 7.9e5+ synthetic spectra              | Sim                 | Zenodo (via paper)                                     |
| [IR–NMR Multimodal Computational Spectra Dataset](https://www.nature.com/articles/s41597-025-05729-8)                                                                                | Dataset (IR + NMR)          | Nature Sci. Data 2025 | 177,461 spectra                       | Sim (MD + DFT + ML) | [Zenodo](https://zenodo.org/records/16417648)          |
| [NMRShiftDB2](https://nmrshiftdb.nmr.uni-koeln.de/)                                                                                                                                  | Database (small molecules)  | —                     | ~50k; ¹H/¹³C                          | Real                | Open                                                   |
| [BMRB](https://bmrb.io/)                                                                                                                                                             | Database (bio-molecules)    | —                     | >13k biomolecules; ¹H/¹³C/¹⁵N…        | Real                | FTP/STAR                                               |
| [SDBS](https://sdbs.db.aist.go.jp/sdbs/cgi-bin/cre_index.cgi)                                                                                                                        | Database (multi-modal)      | —                     | ~14k; ¹H/¹³C/IR/MS/UV                 | Real                | Crawl/script                                           |
| [2DNMRGym (HSQC)](https://arxiv.org/abs/2405.18181)                                                                                                                                  | Simulated 2D dataset        | 2024                  | 22k+ HSQC                             | Sim                 | [HuggingFace/Zenodo](https://huggingface.co/datasets/) |
| [NMRMixDB](https://nmrmixdb.github.io/)                                                                                                                                              | Mixtures                    | —                     | ~3k; ¹H                               | Real                | Open                                                   |
| [HMDB 5.0](https://hmdb.ca)                                                                                                                                                          | Database (metabolites)      | —                     | 4,149; ¹H/¹³C                         | Real                | Open-source                                            |
| [BMRB20 (metabolites subset)](https://bmrb.io)                                                                                                                                       | Database (metabolites)      | —                     | 1,200+; ¹H/¹³C                        | Real                | Open-source                                            |
| [NMRShiftDB2 (updated)](https://nmrshiftdb.nmr.uni-koeln.de)                                                                                                                         | Database (small molecules)  | —                     | 53,954; ¹H/¹³C                        | Real                | Open-source                                            |
| [SDBS23](https://sdbs.db.aist.go.jp/Disclaimer.aspx)                                                                                                                                 | Database (natural products) | —                     | 15,218 (¹H) / 13,457 (¹³C); 900+ NPs  | Real                | Open-source                                            |
| [NP-MRD](https://np-mrd.org)                                                                                                                                                         | Database (natural products) | —                     | 1,290; ¹H/¹³C                         | Real                | Open-source                                            |
| [NAPROC-13](https://c13.materia-medica.net)                                                                                                                                          | Database (natural products) | —                     | 6,000+; ¹³C                           | Real                | Open-source                                            |
| [CH-NMR-NP](https://ch-nmr-np.jeol.co.jp/en/nmrdb)                                                                                                                                   | Database (natural products) | —                     | 30,500; 926 compounds; ¹H/¹³C         | Real                | Open-source                                            |
| [Spektraris-NMR](http://langelabtools.wsu.edu/nmr/)                                                                                                                                  | Specialized subset          | —                     | 466 samples; 250 taxanes; ¹H/¹³C      | Real                | Open-source                                            |
| [C6H6](https://www.c6h6.org)                                                                                                                                                         | Database                    | —                     | 506 entries; ¹H/¹³C                   | Real                | Open-source                                            |
| [Ilm-NMR-P31](https://github.com/clacor/Ilm-NMR-P31)                                                                                                                                 | Element-specific database   | —                     | 14,250 (31P spectra) / 13,730 entries | Real                | Open-source                                            |
| [KnowItAll NMR](https://sciencesolutions.wiley.com)                                                                                                                                  | Commercial database         | —                     | 1.28 M+ spectra; ¹H/¹³C               | Real (Commercial)   | Commercial (Wiley)                                     |
| [Micronmr](https://www.nmrdata.com)                                                                                                                                                  | Commercial database         | —                     | 1.0 M+ spectra; ¹³C                   | Real (Commercial)   | Commercial                                             |
| [NMRBank](https://github.com/eat-sugar/NMRExtractor)                                                                                                                                 | Open repository             | —                     | 225,809 (¹H) / 149,135 (¹³C)          | Real                | Open-source                                            |
                                     |

---

### 3.4 Clustering & Representation Learning for NMR

| Paper Title & Link                                                                                                     | Method                      | Venue/Year | Task                       | Notes                        |
| ---------------------------------------------------------------------------------------------------------------------- | --------------------------- | ---------- | -------------------------- | ---------------------------- |
| Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) ([Anal. Chem. 2014](https://pubs.acs.org/doi/10.1021/ac500161k)) | Statistical clustering      | 2014       | ¹H metabolomics clustering | Classic baseline             |
| Sparse Convex Wavelet Clustering (includes NMR signals)                                                                | Wavelet + convex clustering | arXiv      | Signal clustering          | Joint denoising + clustering |
| Deep representation learning for NMR spectral clustering                                                               | Autoencoder / DL            | —          | Spectral clustering        | DL embedding → K-means/HC    |

---

### 3.5 Foundation Models & Chemistry LMs related to NMR

| Model / Paper                | Family                   | Venue/Year         | Link                                            | Notes                                                           |
| ---------------------------- | ------------------------ | ------------------ | ----------------------------------------------- | --------------------------------------------------------------- |
| ChemGPT                      | Large chemistry LM       | —                  | —                                               | Molecular generative & reasoning; spectra-to-structure transfer |
| DETANet                      | DL architecture          | —                  | —                                               | Chemistry perception; potential for spectra-conditioned tasks   |
| DreaMS (MS foundation model) | Transformer (spectra FM) | Nat. Biotech. 2025 | [GitHub](https://github.com/pluskal-lab/DreaMS) | Cross-modal FM idea transferrable to NMR                        |

### 📚 Synthetic NMR Papers & Datasets

| Category                 | Name                                 | What it offers                                                       | Typical Use                                                  | Link                                                                                                     |
| ------------------------ | ------------------------------------ | -------------------------------------------------------------------- | ------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------- |
| Dataset & Pipeline       | **ARTINA – 100‑Protein NMR Dataset** | 1329 2D–4D spectra + chemical shift assignments + protein structures | Train/test protein NMR auto‑assignment & structure inference | [https://www.nature.com/articles/s41597-023-02879-5](https://www.nature.com/articles/s41597-023-02879-5) |
| Database (LLM‑extracted) | **NMRBank**                          | \~225k small‑molecule records (SMILES, ¹H/¹³C shifts)                | Build ML models for shift/spectrum prediction                | [https://pmc.ncbi.nlm.nih.gov/articles/PMC12118362/](https://pmc.ncbi.nlm.nih.gov/articles/PMC12118362/) |
| Synthetic multimodal     | **IR‑NMR Dataset**                   | 177k IR spectra + 1.2k NMR shifts (DFT+ML)                           | Cross‑modal learning / pretraining                           | [https://www.nature.com/articles/s41597-025-05729-8](https://www.nature.com/articles/s41597-025-05729-8) |
| Carbohydrates            | **GlycoNMR**                         | 2,609 glycans with 211k NMR shifts                                   | Domain‑specific ML (carbohydrates)                           | [https://arxiv.org/abs/2311.17134](https://arxiv.org/abs/2311.17134)                                     |
| 2D spectra               | **2DNMRGym**                         | 22k+ HSQC spectra + SMILES (partly human‑annotated)                  | Train/benchmark 2D NMR predictors                            | [https://arxiv.org/abs/2505.18181](https://arxiv.org/abs/2505.18181)                                     |

---

#### 🛠️ Tools for Synthetic NMR Generation

| Tool                       | What it does                                                                       | How to use                                                            | Required Inputs                                                                                                        | Output                                              | Link                                                                                                             |
| -------------------------- | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| **MestReNova**       | Comprehensive NMR data analysis software for processing 1D/2D spectra, including peak picking, integration, and chemical shift assignment | Process NMR spectra, analyze data, and assign chemical shifts using MestReNova interface | NMR spectra: 1D, 2D, 3D; chemical shift (ppm), coupling constants (Hz), integration, peak assignment | Processed spectra, chemical shift assignments, peak integration | [MestReNova Official Website](https://mestrelab.com/software/mestreNova/) |
| **Spinach (Matlab)**       | Physics‑based simulation of 1D/2D spectra (COSY, HSQC, NOESY), relaxation, MAS NMR | Define spin system + select pulse sequence + run simulation in Matlab | Spin system: isotopes, chemical shifts (ppm), J couplings (Hz), CSA/dipolar terms; external field (B₀); pulse sequence | Time‑domain FID & frequency‑domain spectra (1D/2D)  | [https://en.wikipedia.org/wiki/Spinach\_(software)](https://en.wikipedia.org/wiki/Spinach_%28software%29)        |
| **ORCA / NWChem**          | Quantum chemistry calculation of shielding tensors and J couplings                 | Run DFT/ab initio with NMR keyword                                    | 3D geometry (XYZ/MOL/PDB), basis set, method, optional solvent model                                                   | Isotropic shieldings (σ), J‑couplings, CSA tensors  | [https://github.com/nwchemgit/nwchem](https://github.com/nwchemgit/nwchem)                                       |
| **NMRDB**                  | Online prediction of ¹H/¹³C (1D and 2D COSY/HSQC/HMBC)                             | Draw molecule or paste SMILES on web UI                               | Molecular structure (drawn or SMILES), optional solvent/field strength                                                 | Simulated 1D/2D spectra, JCAMP export               | [https://www.nmrdb.org/](https://www.nmrdb.org/)                                                                 |
| **ChemAxon NMR Predictor** | Predicts ¹H/¹³C chemical shifts and spectra (GUI + CLI)                            | Use MarvinSketch or `cxcalc nmr` CLI                                  | Structure input (SMILES, MOL, SDF), optional solvent/field                                                             | Chemical shifts, spectra, JCAMP files               | [https://docs.chemaxon.com/display/docs/NMR%2BPredictor](https://docs.chemaxon.com/display/docs/NMR%2BPredictor) |
| **NMRbox**                 | VM platform bundling many NMR tools (TopSpin, Sparky, CCPN, etc.)                  | Launch VM, import spectra, run pipelines                              | Experimental or synthetic spectra, peak lists                                                                          | Processed spectra, assignments, structural analysis | [https://nmrbox.nmrhub.org/software](https://nmrbox.nmrhub.org/software)                                         |

---

## 4. IR Spectroscopy (Small Molecules)

### 4.1 Forward Task (Molecule → Spectrum)  
*Infrared spectrum prediction from molecular structures*  
| Paper Title & Link | Venue | Method Type | Input |  Data Source | Code | CKPT |
|------------|---------------|-------|-------|--------------------|------|------|
| [Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra (2017)](https://doi.org/10.1039/c7sc02267k) | Chem. Sci. | ML (HDNNP + NN Dipole) | 3D coordinates  | — | — | — |
| [A Machine Learning Protocol for Predicting Protein Infrared Spectra (2020)](https://pubs.acs.org/doi/10.1021/jacs.0c06530) | J. Am. Chem. Soc. | ML (MLP) | 3D coordinates  | — | — | — |
| [Predicting Infrared Spectra with Message Passing Neural Networks (2021)](https://doi.org/10.1021/acs.jcim.1c00055) | J. Chem. Inf. Model. | DL (MPNN + FFNN) |  **Simulated:** SMILES<br>**Experimental:** SMILES+Phase | **Simulated:** PubChem<br>**Experimental:** NIST, PNNL, AIST, Coblentz  | [![Star](https://img.shields.io/github/stars/gfm-collab/chemprop-IR.svg?style=social&label=Star)](https://github.com/gfm-collab/chemprop-IR) | <a href="https://zenodo.org/records/4698943"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Graphormer-IR: Graph Transformers Predict Experimental IR Spectra (2024)](https://doi.org/10.1021/acs.jcim.4c00378) | J. Chem. Inf. Model. | DL (Graph Transformer + MLP, 1D-CNN) | SMILES+Phase | NIST, AIST, Coblentz | [![Star](https://img.shields.io/github/stars/HopkinsLaboratory/Graphormer-IR.svg?style=social&label=Star)](https://github.com/HopkinsLaboratory/Graphormer-IR) | <a href="https://zenodo.org/records/10790190"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure (2024)](https://doi.org/10.1016/j.cplett.2024.141603) | Chem. Phys. Lett. | DL (MPNN) | 3D Molecular Structure | NIST | [![Star](https://img.shields.io/github/stars/allouchear/NNMol-IR.svg?style=social&label=Star)](https://github.com/allouchear/NNMol-IR) | <a href="https://zenodo.org/records/13681778"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach with Structural Neighboring Mechanism (2024)](https://doi.org/10.1021/acs.analchem.4c01972) | Anal. Chem. | ML | SMILES | CIAD | [![Star](https://img.shields.io/github/stars/ChengchunLiu/IR-DIAZO-KETONE.svg?style=social&label=Star)](https://github.com/ChengchunLiu/IR-DIAZO-KETONE) | -- |
| [Infrared Spectra Prediction Using Attention-Based Graph Neural Networks (2024)](https://doi.org/10.1039/d3dd00254c) | Digital Discovery | DL (GNN) | SMILES | NIST | [![Star](https://img.shields.io/github/stars/nj-saquer/IR-Spectra-Prediction-Graph-Models.svg?style=social&label=Star)](https://github.com/nj-saquer/IR-Spectra-Prediction-Graph-Models) | — |
| [Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach (2024)](https://doi.org/10.1021/acs.jpca.4c06745) | J. Phys. Chem. A | DL (MPNN + FFNN) | SMILES | GDB | <a href="https://zenodo.org/records/13844305"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | <a href="https://zenodo.org/records/13844305"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Unlocking the Potential of Machine Learning in Enhancing Quantum Chemical Calculations for Infrared Spectral Prediction (2025)](https://doi.org/10.1021/acsomega.5c02405) | ACS Omega | ML (Multioutput Regressor + Random Forest Regressor) | 3D Molecular Structure | Gaussian 16 | Supporting Information | — |

### 4.2 Inverse Task (Spectrum → Molecule)  
*Molecular characterization from infrared spectra*  
| Paper Title & Link | Venue | Method Type | Output | Data Source | Code | CKPT |
|------------|---------------|-------|-------|--------------------|------|------|
| [Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation (2024)](https://kehanguo2.github.io/Molpuzzle.io/) | NeurIPS2024 | LLM (GPT‑4o, Claude‑3, Gemini, etc.) | SMILES | — | [![Star](https://img.shields.io/github/stars/KehanGuo2/MolPuzzle.svg?style=social&label=Star)](https://github.com/KehanGuo2/MolPuzzle) | — |
| [Leveraging Infrared Spectroscopy for Automated Structure Elucidation (2024)](https://doi.org/10.1038/s42004-024-01341-w) | Commun. Chem. | DL (Transformer) | SMILES | **Simulated:** PubChem<br>**Experimental:** NIST | [![Star](https://img.shields.io/github/stars/rxn4chemistry/rxn-ir-to-structure.svg?style=social&label=Star)](https://github.com/rxn4chemistry/rxn-ir-to-structure) | <a href="https://zenodo.org/records/7928396"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Transformer-Based Models for Predicting Molecular Structures from Infrared Spectra Using Patch-Based Self-Attention (2025)](https://doi.org/10.1021/acs.jpca.4c05665) | J. Phys. Chem. A | DL (Transformer + Patch-based Self-Attention) | SMILES | **Simulated:** PubChem, QM9S <br>**Experimental:** NIST  | [![Star](https://img.shields.io/github/stars/wenjin886/PatchBasedSelfAttention.svg?style=social&label=Star)](https://github.com/wenjin886/PatchBasedSelfAttention) | <a href="https://zenodo.org/records/12789777"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> |
| [Revolutionizing Spectroscopic Analysis Using Sequence-to-Sequence Models I: From Infrared Spectra to Molecular Structures (2025)](https://doi.org/10.26434/chemrxiv-2025-n4q84) | ChemRxiv | DL (GRU, LSTM, GPT, Transformer) | SELFIES | QM9, PC9 | — | — |
| [Setting new benchmarks in AI‑driven infrared structure elucidation (2025)](https://doi.org/10.1039/d5dd00131e) | Digit. Discov. | DL (Transformer + Patch‑based Self‑Attention) | SMILES | **Simulated:** IRtoMol, Multimodal Spectroscopic Dataset <br>**Experimental:** NIST | [![Star](https://img.shields.io/github/stars/rxn4chemistry/MultimodalAnalytical.svg?style=social&label=Star)](https://github.com/rxn4chemistry/MultimodalAnalytical) | [GitHub](https://github.com/rxn4chemistry/MultimodalAnalytical/tree/main/paper_replication/ir)  |

### 4.3 IR Datasets
| Dataset / Method Name & Link | Size | Data Source | Real / Simulated | Element Coverage | 
|---------------------|----------------|------------------|-----|----------------------|
| [Chemprop-IR](https://doi.org/10.1021/acs.jcim.1c00055) <a href="https://zenodo.org/records/4698943"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | 85,232 | PubChem (SMILES for molecular structures) | Simulated (GFN2-xTB) | C, H, O, N, S, P, Si, F, Cl, Br, I |
| [CMPNN](https://doi.org/10.1021/acs.jpca.4c06745) <a href="https://zenodo.org/records/13844305"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | 31,570 | GDB (SMILES for molecular structures) | Simulated (DFT) | C, H |
| [Multimodal Spectroscopic Dataset](https://proceedings.neurips.cc/paper_files/paper/2024/file/e38e60b33bb2c6993e0865160cdb5cf1-Paper-Datasets_and_Benchmarks_Track.pdf) <a href="https://zenodo.org/records/11611178"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | 794,403 | USPTO reaction dataset (SMILES for molecular structures) | Simulated (MD) | C, H, O, N, S, P, Si, B, F, Cl, Br, I |
| [IRtoMol](https://doi.org/10.1038/s42004-024-01341-w) <a href="https://zenodo.org/records/7928396"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | 634,585 | PubChem (SMILES for molecular structures) | Simulated (MD + PCFF forcefield) | C, H, O, N, S, P, F, Cl, Br, I |
| [MolPuzzle](https://proceedings.neurips.cc/paper_files/paper/2024/file/f2b9e8e7a36d43ddfd3d55113d56b1e0-Paper-Datasets_and_Benchmarks_Track.pdf) <a href="https://github.com/KehanGuo2/MolPuzzle"><img src="https://api.iconify.design/mdi:github.svg" width="20"/></a> | 216 (Picture format) | — | Mixed | — |
| [IR–NMR Multimodal Computational Spectra Dataset](https://www.nature.com/articles/s41597-025-05729-8) <a href="https://zenodo.org/records/16417648"><img src="https://api.iconify.design/academicons:zenodo.svg" width="20"/></a> | 177,461 | USPTO (SMILES for molecular structures) | Simulated (MD + DFT + ML) | C, H, O, N, S, P, Si, B, F, Cl, Br, I |
| [QM9S](https://www.nature.com/articles/s43588-023-00550-y) <a href="https://figshare.com/articles/dataset/QM9S_dataset/24235333"><img src="https://api.iconify.design/academicons:figshare.svg" width="20"/></a> | 133,885 | QM9 (re-optimized geometries) | Simulated (DFT) | C, H, O, N, F  |
| SRD 35 <a href="https://www.nist.gov/srd/nist-standard-reference-database-35"><img src="https://cdn.worldvectorlogo.com/logos/nist.svg" width="20"/></a> | 5,228 (gas-phase) | — | Real | — |
| NIST Chemistry WebBook <a href="https://webbook.nist.gov/chemistry/"><img src="https://cdn.worldvectorlogo.com/logos/nist.svg" width="20"/></a> | >16,000 | — | Real | — |
| Coblentz Society Spectral Database <a href="https://webbook.nist.gov/chemistry/coblentz/"><img src="https://cdn.worldvectorlogo.com/logos/nist.svg" width="20"/></a> | >9,500 | — | Real | — |
| [NWIR](https://www.pnnl.gov/publications/northwest-infrared-nwir-gas-phase-spectral-database-industrial-and-environmental) | ~1,000–1,500 (gas-phase) | — | Real | — |
| [AIST SDBS](https://sdbs.db.aist.go.jp) | ~54,100 | — | Real | — |

### 4.4 General Tools
| Tool  & Link      | Type                          | Main Functions / Uses                                      | 
|--------------------|-------------------------------|------------------------------------------------------------|
| RDKit [![Star](https://img.shields.io/github/stars/rdkit/rdkit.svg?style=social&label=Star)](https://github.com/rdkit/rdkit) | Open-source library (Python/C++) | Molecular descriptors for IR–structure fusion, Morgan/ECFP fingerprints for IR-guided search, SMARTS substructure from IR functional-group hints, Conformers + MMFF/UFF for vibrational context, InChI/SMILES I/O to link spectra and structures |
| Psi4  [![Star](https://img.shields.io/github/stars/psi4/psi4.svg?style=social&label=Star)](https://github.com/psi4/psi4) | Open-source library (Python/C++)  | High‑accuracy, open‑source ab initio quantum chemistry suite for molecular properties |
| GFN2-xTB [![Star](https://img.shields.io/github/stars/grimme-lab/xtb.svg?style=social&label=Star)](https://github.com/grimme-lab/xtb)  |  Open-source semiempirical QM (Fortran/Python) | Fast IR frequency estimation, IR peak-to-structure attribution, Conformer effects on IR bands, Batch geometry preparation, Solvent-adjusted IR shifts |
| cclib [![Star](https://img.shields.io/github/stars/cclib/cclib.svg?style=social&label=Star)](https://github.com/cclib/cclib) | Open-source library (Python) | Provides parsers for output files of computational chemistry packages|
| SELFIES [![Star](https://img.shields.io/github/stars/aspuru-guzik-group/selfies.svg?style=social&label=Star)](https://github.com/aspuru-guzik-group/selfies) | Open-source library (Python)  | SMILES↔SELFIES conversion   |
| [Open Babel](https://openbabel.org)     | Open-source chemistry toolbox (C++/Python) | Format conversion, 3D generation and conformer search (MMFF94/UFF), SMARTS substructure and fingerprints (FP2/FP3/MACCS)|
| [Gaussian 16](https://gaussian.com/g16) | Commercial quantum chemistry software | DFT IR frequencies/intensities, Harmonic and anharmonic IR (VPT2), Normal‑mode peak assignment, Solvent models (PCM/SMD) for IR |
| [ChemCraft](https://www.chemcraftprog.com/)     | Commercial visualization software | Visualize vibrational spectra with Lorentzian/Gaussian broadening, Compare calculated vs. experimental spectra, View MOs/SCF graphs, Parse outputs from Gaussian/ORCA/GAMESS/NWChem etc. |
| [Q‑Chem](https://www.q-chem.com/)   | Commercial quantum chemistry software   | DFT IR frequencies/intensities, Harmonic and anharmonic IR (VPT2/VCI/TOSH), Normal‑mode and Raman analysis|

---

## 5. Multimodal Spectroscopy (Small Molecules)
### 5.1 Forward Task (Molecule → Multiple Spectra)
*Joint prediction of multiple spectral modalities from molecular structures*
| Paper Title & Link | Modalities | Method Type | Venue/Year | Code | Notes |
|---|---|---|---|---|---|
| [Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry](https://proceedings.neurips.cc/paper_files/paper/2024/file/e38e60b33bb2c6993e0865160cdb5cf1-Paper-Datasets_and_Benchmarks_Track.pdf) | IR + MS + ^1H/^13C/HSQC-NMR (simulated) | Dataset + baselines (CNN/XGBoost) | NeurIPS D&B 2024 | [![Star](https://img.shields.io/github/stars/rxn4chemistry/multimodal-spectroscopic-dataset.svg?style=social&label=Star)](https://github.com/rxn4chemistry/multimodal-spectroscopic-dataset) | Provides ~790k multimodal spectra and forward-prediction baselines to pretrain joint predictors. |
| [IR–NMR Multimodal Computational Spectra Dataset](https://www.nature.com/articles/s41597-025-05729-8) | IR + NMR (simulated) | Dataset (MD/DFT/ML) | Sci. Data 2025 | — | Curated paired IR–NMR set enabling multi-task forward models and cross-modal transfer. |

### 5.2 Inverse Task (Multiple Spectra → Molecule)
*Multimodal integration for enhanced molecular identification*

| Paper Title & Link | Modalities | Method Type | Venue/Year | Code | Notes |
|---|---|---|---|---|---|
| [A Transformer-Based Generative Chemical Language AI Model for Structural Elucidation of Organic Compounds](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01016-1) | IR + UV/Vis + ^1H NMR | Encoder–decoder Transformer (generative CASE) | J. Cheminformatics 2025 | — | End-to-end spectra→structure generation; strong single-paper reference for joint use of IR/UV/NMR. |
| [Leveraging Infrared Spectroscopy for Automated Structure Elucidation](https://www.nature.com/articles/s42004-024-01341-w) | IR | Transformer | Commun. Chem. 2024 | [![Star](https://img.shields.io/github/stars/rxn4chemistry/rxn-ir-to-structure.svg?style=social&label=Star)](https://github.com/rxn4chemistry/rxn-ir-to-structure) | Single-modality IR baseline frequently paired with NMR/MS to form multimodal pipelines. |

### 5.3 Representation Learning & Fusion (Model-Agnostic Methods used for Spectra)

| Paper Title & Link | What it Brings | Method Type | Venue/Year | Code | Notes |
|---|---|---|---|---|---|
| [Ming-Omni: A Unified Multimodal Model for Perception and Generation](https://arxiv.org/abs/2506.09344) | Unified encoder/decoders; modality-specific MoE routers | MoE-UMM (image/text/audio/video) | arXiv 2025 | [![Star](https://img.shields.io/github/stars/inclusionAI/Ming.svg?style=social&label=Star)](https://github.com/inclusionAI/Ming) | Useful design for preventing cross-modal conflicts when adding spectral modalities as new “experts”. |
| [Boosting Multimodal Learning via Disentangled Gradient Learning (DGL)](https://arxiv.org/abs/2507.10213) | Decouples encoder vs. fusion optimization to avoid gradient interference | Training framework | arXiv 2025 | [![Star](https://img.shields.io/github/stars/shicaiwei123/ICCV2025-GDL.svg?style=social&label=Star)](https://github.com/shicaiwei123/ICCV2025-GDL) | Practical recipe when IR/NMR/MS encoders underperform once fused. |
| [I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts](https://arxiv.org/abs/2505.19190) | Interaction-specialized experts + sample-level explanations | MoE fusion | arXiv/ICML 2025 | [![Star](https://img.shields.io/github/stars/Raina-Xin/I2MoE.svg?style=social&label=Star)](https://github.com/Raina-Xin/I2MoE) | Lets you see which modality interactions (e.g., IR×NMR) drive a prediction. |
| [MM-Embed: Universal Multimodal Retrieval with MLLMs](https://arxiv.org/abs/2411.02571) | Bi-encoder + MLLM re-ranker for flexible text↔image retrieval | Multimodal embeddings | ICLR 2025 | [HF Card](https://huggingface.co/nvidia/MM-Embed) | Template for spectra/text retrieval (e.g., query by IR peaks + structural hints). |
| **SpecEmbedding: Supervised Contrastive Learning Leads to More Reasonable Spectral Embeddings** | MS spectral encoder with supervised contrastive learning | Transformer encoder + SupCon | *Analytical Chemistry* ASAP 2025 | [![Star](https://img.shields.io/github/stars/sword-nan/SpecEmbedding.svg?style=social&label=Star)](https://github.com/sword-nan/SpecEmbedding) · [Demo](https://huggingface.co/spaces/xp113280/SpecEmbedding) | High-quality MS embeddings usable as one branch in IR+NMR+MS fusion. |
| [UAE: Can Understanding and Generation Truly Benefit Together — or Just Coexist?](https://arxiv.org/abs/2509.09666) | Unified auto-encoder view (I2T ↔ T2I) with RL to align understanding & generation | Unified multimodal learning (UMM) | arXiv 2025 | [![Star](https://img.shields.io/github/stars/PKU-YuanGroup/UAE.svg?style=social&label=Star)](https://github.com/PKU-YuanGroup/UAE) | Useful recipe for tying inverse (spectra→structure) with forward (structure→spectra) via reconstruction. |

### 5.4 Datasets & Benchmarks (Multimodal, Small Molecules)
| Name & Link | Modalities | Type | Size | Real / Simulated | Website / Code | Notes |
|---|---|---|---|---|---|---|
| **Multimodal Spectroscopic Dataset (MSD)** | IR + MS + ^1H/^13C/HSQC-NMR | Benchmark + Dataset | ~790k molecules | Simulated | [Site](https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/) · [Zenodo](https://zenodo.org/records/11611178) · [GitHub](https://github.com/rxn4chemistry/multimodal-spectroscopic-dataset) | Common pretraining/eval source for multimodal spectral models. |
| **IR–NMR Multimodal Computational Spectra Dataset** | IR + NMR | Dataset | 177,461 spectra | Simulated | [Article](https://www.nature.com/articles/s41597-025-05729-8) · [Zenodo](https://zenodo.org/records/16417648) | Paired IR–NMR for cross-modal learning and evaluation. |
| **SDBS (AIST)** | IR + MS + ^1H/^13C-NMR + Raman + ESR | Database | ~34k molecules | Real | [SDBS](https://sdbs.db.aist.go.jp/) | Broad real-world spectra across modalities for validation. |
| **NIST Chemistry WebBook / SRD-35** | IR (gas-phase) + MS + UV/Vis | Database | 5,228 IR (SRD-35); 16k+ IR & 33k+ MS overall | Real | [WebBook](https://webbook.nist.gov/chemistry/) · [SRD-35](https://www.nist.gov/srd/nist-standard-reference-database-35) | Standard references and downloadable IR libraries. |
| **SimXRD-4M** *(related)* | Powder XRD | Benchmark + Dataset | 4.06M patterns | Simulated | [OpenReview](https://openreview.net/forum?id=mkuB677eMM) | Not small-molecule spectra, but relevant template for large-scale physics-faithful simulation & benchmarking. |

---

## 6. X-ray Diffraction (XRD) (Crystals)

### 6.1 Forward Task (Crystal → Pattern)  
*Prediction of XRD patterns from crystal structures*

### 6.2 Inverse Task (Pattern → Crystal)  
*Crystal structure determination from XRD patterns*

---

## License  
📄 This project is licensed under the MIT License — see the LICENSE file for details.

---

Awesome-SpectraAI-Resources

Awesome-SpectraAI-Resources Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Awesome-SpectraAI-Resources

Awesome-SpectraAI-Resources Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README