Datasets:

fedeotto
/

nmiracle-datasets

@@ -1,278 +1,27 @@
 ---
-license: mit
-task_categories:
-  - text-generation
-  - feature-extraction
-language:
-  - en
 tags:
   - chemistry
   - spectroscopy
-  - molecular-generation
   - nmr
   - infrared
   - smiles
-  - deep-learning
-  - multimodal
-size_categories:
-  - 1M<n<10M
----
-# NMIRacle Dataset
-## Dataset Description
-This dataset supports the **NMIRacle** (NMr-IR orACLE) framework for *de novo* molecular structure elucidation from multi-modal spectroscopic data. It contains paired molecular structures (SMILES) with simulated spectroscopic measurements (IR, ¹H-NMR, ¹³C-NMR) and fragment annotations.
-### Dataset Summary
-The dataset is organized into two subsets supporting the two-stage training paradigm of NMIRacle:
-1. **Pretrain Dataset** (~3.7M molecules): For fragment-to-molecule pre-training (Stage 1)
-2. **Multispectra Dataset** (~790K molecules): For spectra-to-molecule fine-tuning (Stage 2)
-### Supported Tasks
-- **Molecular Structure Generation**: Generate SMILES from spectroscopic inputs
-- **Fragment Prediction**: Predict substructure counts from spectra
-- **Multi-modal Learning**: Learn joint representations across IR and NMR modalities
-### Languages
-Chemical notation (SMILES, SMARTS)
----
-## Dataset Structure
-```
-nmiracle-dataset/
-├── pretrain/                          # Stage 1: Fragment pre-training (~3.7M molecules)
-│   ├── smiles.npy                     # Molecular SMILES strings
-│   ├── substructures.h5               # Binary fragment presence vectors
-│   ├── substructure_counts.h5         # Fragment occurrence counts
-│   ├── split_indices.p                # Train/val/test split indices
-│   ├── pretrain_train_indices.npy     # Train split indices (legacy)
-│   ├── pretrain_val_indices.npy       # Validation split indices (legacy)
-│   └── pretrain_test_indices.npy      # Test split indices (legacy)
-│
-└── multispectra/                      # Stage 2: Spectra fine-tuning (~790K molecules)
-    ├── smiles.npy                     # Molecular SMILES strings
-    ├── spectra.h5                     # Multi-modal spectra (IR + ¹H-NMR + ¹³C-NMR)
-    ├── substructures.h5               # Binary fragment presence vectors
-    ├── substructure_counts.h5         # Fragment occurrence counts
-    └── split_indices.p                # Train/val/test split indices
-```
----
-## Data Fields
-### Molecular Data
-| Field | Type | Description |
-|-------|------|-------------|
-| `smiles.npy` | numpy array (str) | Canonical SMILES strings representing molecular structures |
-### Spectroscopic Data (`spectra.h5`)
-| Field | Shape | Description |
-|-------|-------|-------------|
-| `spectra` | (N, 21800) | Concatenated spectral intensities: IR (1800) + ¹H-NMR (10000) + ¹³C-NMR (10000) |
-**Spectral Modalities:**
-| Modality | Features | Range | Description |
-|----------|----------|-------|-------------|
-| **IR** | 1,800 | 400-4000 cm⁻¹ | Infrared absorption spectra (vibrational modes) |
-| **¹H-NMR** | 10,000 | 0-14 ppm | Proton NMR spectra (hydrogen environments) |
-| **¹³C-NMR** | 10,000 | 0-220 ppm | Carbon-13 NMR spectra (carbon backbone) |
-### Fragment Data
-| Field | Type | Description |
-|-------|------|-------------|
-| `substructures.h5` | (N, 991) binary | Presence/absence of 991 SMARTS-defined fragments |
-| `substructure_counts.h5` | (N, 991) int | Occurrence counts of each fragment in the molecule |
-### Split Indices
-| Field | Type | Description |
-|-------|------|-------------|
-| `split_indices.p` | pickle dict | Dictionary with 'train', 'val', 'test' keys containing index arrays |
----
-## Dataset Statistics
-### Pretrain Dataset (Stage 1)
-| Statistic | Value |
-|-----------|-------|
-| **Total molecules** | ~3,700,000 |
-| **Train split** | ~2,960,000 (80%) |
-| **Validation split** | ~370,000 (10%) |
-| **Test split** | ~370,000 (10%) |
-| **Max heavy atoms** | 35 |
-| **Element types** | C, N, O, S, F, Cl, Br, P, I (9 elements) |
-| **Fragment vocabulary** | 991 SMARTS patterns |
-| **Max fragment count** | 232 |
-### Multispectra Dataset (Stage 2)
-| Statistic | Value |
-|-----------|-------|
-| **Total molecules** | ~790,000 |
-| **Train split** | ~632,000 (80%) |
-| **Validation split** | ~79,000 (10%) |
-| **Test split** | ~79,000 (10%) |
-| **Spectral features** | 21,800 total |
-| **IR features** | 1,800 |
-| **¹H-NMR features** | 10,000 |
-| **¹³C-NMR features** | 10,000 |
----
-## Data Preprocessing
-### Spectra Normalization
-- **IR and ¹H-NMR**: Normalized to [0, 1] range; peak shapes and relative intensities preserved
-- **¹³C-NMR**: Peak detection with 10% threshold, discretized into 80 bins (~2.75 ppm each)
-### Fragment Vocabulary
-The dataset uses a curated vocabulary of **991 SMARTS patterns** covering common organic motifs including:
-- Functional groups (hydroxyl, carbonyl, amine, etc.)
-- Ring systems (aromatic, aliphatic, hetero)
-- Chain patterns and substituents
----
-## Usage
-### Loading with Python
-```python
-import numpy as np
-import h5py
-import pickle
-# Load SMILES
-smiles = np.load('multispectra/smiles.npy', allow_pickle=True)
-# Load spectra
-with h5py.File('multispectra/spectra.h5', 'r') as f:
-    spectra = f['spectra'][:]  # Shape: (N, 21800)
-# Split spectra into modalities
-ir_spectra = spectra[:, :1800]           # IR: 1800 features
-hnmr_spectra = spectra[:, 1800:11800]    # ¹H-NMR: 10000 features
-cnmr_spectra = spectra[:, 11800:]        # ¹³C-NMR: 10000 features
-# Load fragment counts
-with h5py.File('multispectra/substructure_counts.h5', 'r') as f:
-    fragment_counts = f['substructure_counts'][:]  # Shape: (N, 991)
-# Load splits
-with open('multispectra/split_indices.p', 'rb') as f:
-    splits = pickle.load(f)
-train_idx = splits['train']
-val_idx = splits['val']
-test_idx = splits['test']
-```
-### Using with NMIRacle Framework
-```python
-from nmiracle.data.datamodule import SpectralDataModule
-from nmiracle.data.tokenizer import BasicSmilesTokenizer
-tokenizer = BasicSmilesTokenizer()
-tokenizer.setup_alphabet(alphabet)
-datamodule = SpectralDataModule(
-    config=config.data,
-    tokenizer=tokenizer
-)
-datamodule.setup()
-```
 ---
-## Dataset Creation
-### Source Data
-- **Pretrain molecules**: Combined from GDB-17 database (~3M) and SpectraBase (~140K), augmented with molecules from Alberts et al. dataset (~670K)
-- **Multispectra**: Derived from Alberts et al. (2024) multimodal spectroscopic dataset
-### Spectra Simulation
-All spectra are computationally simulated:
-- **IR**: Simulated infrared absorption using quantum chemical methods
-- **NMR**: Simulated using established chemical shift prediction algorithms
-### Fragment Extraction
-Fragment presence and counts are computed using RDKit's `GetSubstructMatches` function with the 991 SMARTS patterns in the vocabulary.
----
-## Considerations for Using the Data
-### Social Impact
-This dataset supports automated molecular structure elucidation, which can:
-- **Accelerate drug discovery** by reducing manual spectral interpretation
-- **Democratize chemistry** by making structure elucidation accessible to non-experts
-- **Enable metabolomics research** through faster identification of unknown compounds
-### Biases
-- **Simulated spectra**: All spectra are computationally simulated and may not capture all experimental artifacts (noise, baseline drift, solvent effects)
-- **Chemical space coverage**: Dataset is biased toward drug-like organic molecules; may not generalize to organometallic compounds, polymers, or inorganic species
-- **Element diversity**: Limited to 9 element types (C, N, O, S, F, Cl, Br, P, I)
-### Limitations
-- **Stereochemistry**: IR and NMR spectra cannot distinguish absolute stereochemistry (enantiomers)
-- **Experimental gap**: Models trained on simulated spectra may require domain adaptation for experimental data
-- **Molecule size**: Limited to molecules with ≤35 heavy atoms
----
-## Citation
-If you use this dataset in your research, please cite:
-```bibtex
-@article{nmiracle2025,
-  title={NMIRacle: Scalable Structure Elucidation from IR and NMR Spectra},
-  author={[Author Names]},
-  journal={arXiv preprint arXiv:XXXX.XXXXX},
-  year={2025}
-}
-```
-### Related Datasets
-- Alberts et al. (2024): Original multimodal spectroscopic dataset
-- GDB-17: Chemical universe database
----
-## License
-This dataset is released under the **MIT License**.
----
-## Dataset Curators
-- [Your Name] - Imperial College London
-## Contact
-For questions or issues regarding the dataset, please open an issue on the [NMIRacle GitHub repository](https://github.com/yourusername/nmiracle) or contact [your.email@example.com].

 ---
+license: other
 tags:
   - chemistry
   - spectroscopy
+  - multimodal
   - nmr
   - infrared
   - smiles
+private: true
 ---
+# NMIRacle (Derived Dataset)
+This dataset repository contains **derived data** used internally for the development and evaluation of the NMIRacle framework.
+The data is **not original**. It is constructed from, and depends on, the following publicly available Zenodo datasets:
+- **Dataset A** (License: CDLA–Sharing 1.0)
+  [Zenodo link]
+- **Dataset B** (License: CC-BY-4.0)
+  [Zenodo link]
+Please refer to the *original Zenodo repositories* for the authoritative source of the data, the full licensing terms, and the recommended citation for each dataset.
+This Hugging Face repository is currently **private** and intended only for internal research use. Licensing, redistribution conditions, and documentation will be finalized before any public release.