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license: mit
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task_categories:
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- tabular-regression
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- tabular-classification
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pretty_name: NMRGym
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language:
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- en
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tags:
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- chemistry
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- nmr
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- spectroscopy
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- molecular-property-prediction
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- drug-discovery
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- cheminformatics
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size_categories:
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- 100K<n<1M
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---
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# NMRGym
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Benchmark on NMR Spectrum.
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!!! **Important:** !!!
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If you need access to the dataset, please email [zhengf723@connect.hkust-gz.edu.cn](mailto:zhengf723@connect.hkust-gz.edu.cn)
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## Data Sources (Before Cleaning)
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| Source | Records | Unique SMILES | Total Spectrums | ¹H NMR | ¹³C NMR |
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| :------------------ | ----------: | ------------: | --------------: | ----------: | ----------: |
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| **CH-NP** | 12,165 | 12,165 | 24,326 | 12,161 | 12,165 |
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| **HMDB** | 1,791 | 896 | 3,278 | 1,566 | 1,712 |
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| **NMRBank** | 148,914 | 142,964 | 297,043 | 148,437 | 148,606 |
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| **NMRShiftDB 2024** | 41,019 | 39,631 | 50,416 | 18,570 | 31,846 |
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| **NP-MRD** | 489,569 | 243,598 | 950,242 | 462,233 | 488,009 |
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| **PubChem-NMR** | 1,647 | 1,535 | 2,605 | 1,556 | 1,049 |
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| **SDBS** | 12,926 | 12,626 | 22,711 | 11,522 | 11,189 |
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| **Total** | **708,031** | **430,690** | **1,350,621** | **656,045** | **694,576** |
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---
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## Balanced Dataset Statistics (After Cleaning & Processing)
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After data cleaning, filtering, quality control, and balanced scaffold splitting, the final NMRGym dataset consists of three splits:
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| Split | Records | Unique SMILES | ¹H NMR | ¹³C NMR |
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| :------- | ----------: | ------------: | ----------: | ----------: |
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| **Train** | 402,676 | 250,822 | 402,676 | 402,676 |
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| **Val** | 53,672 | 50,716 | 53,672 | 53,672 |
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| **Test** | 80,069 | 74,568 | 80,069 | 80,069 |
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| **Total** | **536,417** | **376,106** | **536,417** | **536,417** |
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### Dataset Visualizations
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<!-- 
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*Figure 1: Overview of dataset statistics including total records, unique SMILES, data duplication, NMR spectra types, and top elements.*
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*Figure 2: Distribution of 22 functional groups across train, validation, and test sets.*
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*Figure 3: Distribution of common elements (C, H, O, N, F, Cl, Br, S, P, I) across datasets.* -->
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---
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### Quick Checklist
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* [✅] Data cleaning: Heavy atom filtering and illegal smiles exclusion.
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* [✅] Data Summary and Visualization.
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* [✅] Data Split.
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* [] 3D coord. generation. Rdkit.
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* [✅] Toxic Property label generation.
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* [✅] Function Group label generation.
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---
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## NMRGym Split Dataset Format (`nmrgym_spec_filtered_both_{train,test}.pkl`)
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This dataset contains post-QC, scaffold-split molecular entries with paired NMR spectra, structure fingerprints, functional group annotations, and toxicity labels.
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Each `.pkl` file is a Python list of dictionaries. Each dictionary corresponds to a single unique molecule.
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### Example record structure
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```python
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{
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"smiles": "CC(=O)Oc1ccccc1C(=O)O",
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"h_shift": [7.25, 7.32, 2.14, 1.27],
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"c_shift": [128.5, 130.1, 172.9, 20.7],
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"fingerprint": np.ndarray(shape=(2048,), dtype=np.uint8),
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"fg_onehot": np.ndarray(shape=(22,), dtype=np.uint8),
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"toxicity": np.ndarray(shape=(7,), dtype=np.int8),
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}
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```
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---
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### Field definitions
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**`smiles`** (`str`)
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Canonical SMILES string for this molecule.
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**`h_shift`** (`list[float]`)
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Experimental or curated ¹H NMR peak positions in ppm.
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**`c_shift`** (`list[float]`)
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Experimental or curated ¹³C NMR peak positions in ppm.
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**`fingerprint`** (`np.ndarray(2048,)`, `uint8`)
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RDKit Morgan fingerprint (radius = 2, 2048 bits). Encodes local circular substructures.
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**`fg_onehot`** (`np.ndarray(22,)`, `uint8`)
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Binary functional-group vector. `1` means the SMARTS pattern is present in the molecule.
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Index mapping (0→21):
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1. Alcohol
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2. Carboxylic Acid
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3. Ester
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4. Ether
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5. Aldehyde
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6. Ketone
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7. Alkene
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8. Alkyne
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9. Benzene
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10. Primary Amine
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11. Secondary Amine
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12. Tertiary Amine
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13. Amide
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14. Cyano
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15. Fluorine
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16. Chlorine
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17. Bromine
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18. Iodine
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19. Sulfonamide
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20. Sulfone
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21. Sulfide
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22. Phosphoric Acid
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**`toxicity`** (`np.ndarray(7,)`, `int8`)
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Seven binary toxicity endpoints (1 = toxic / positive, 0 = negative):
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0. AMES (mutagenicity)
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1. DILI (Drug-Induced Liver Injury)
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2. Carcinogens_Lagunin (carcinogenicity)
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3. hERG (cardiotoxic channel block)
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4. ClinTox (clinical toxicity)
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5. NR-ER (endocrine / estrogen receptor)
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6. SR-ARE (oxidative stress response)
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---
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### Train / Test meaning
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* `nmrgym_spec_filtered_both_train.pkl`: scaffold-based training set.
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* `nmrgym_spec_filtered_both_test.pkl`: scaffold-disjoint test set.
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Scaffold split is computed using Bemis–Murcko scaffolds. This means test scaffolds do not appear in train, simulating generalization to new chemotypes instead of just new stereoisomers.
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---
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### Minimal usage example
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```python
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import pickle
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from rdkit import Chem
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from rdkit.Chem import AllChem
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# load the dataset
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test_path = "/gemini/user/private/NMRGym/utils/split_output/nmrgym_spec_filtered_both_test.pkl"
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with open(test_path, "rb") as f:
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dataset = pickle.load(f)
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print("num molecules:", len(dataset))
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print("keys:", dataset[0].keys())
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print("example toxicity vector:", dataset[0]["toxicity"])
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# build 3D conformer for the first molecule
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smi = dataset[0]["smiles"]
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mol = Chem.MolFromSmiles(smi)
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mol = Chem.AddHs(mol) # add hydrogens for 3D
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AllChem.EmbedMolecule(mol, AllChem.ETKDG())
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AllChem.UFFOptimizeMolecule(mol)
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# extract 3D coordinates (in Å)
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conf = mol.GetConformer()
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coords = []
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for atom_idx in range(mol.GetNumAtoms()):
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pos = conf.GetAtomPosition(atom_idx)
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coords.append([pos.x, pos.y, pos.z])
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print("3D coords for first molecule (Angstrom):")
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for i, (x,y,z) in enumerate(coords):
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sym = mol.GetAtomWithIdx(i).GetSymbol()
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print(f"{i:2d} {sym:2s} {x:8.3f} {y:8.3f} {z:8.3f}")
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```
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### Notes on 3D coordinates
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* We generate a single low-energy conformer using RDKit ETKDG embedding + UFF optimization.
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* Coordinates are in Ångström.
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* These coordinates are not guaranteed to match the experimental NMR conformer in solvent; they are intended for featurization (message passing models, geometry-aware models, etc.), not quantum-accurate geometry.
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---
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## Downstream Benchmark Tasks
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This benchmark suite evaluates AI models on multiple spectroscopy-related prediction tasks. Each task reflects a distinct molecular reasoning aspect — from direct spectrum regression to property and toxicity prediction.
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### 1. Spectral Prediction
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| **Method / Paper Title** | **Model Type / Approach** | **Metric(s)** | **Model Size** | **Notes** |
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| ------------------------ | ------------------------- | ------------- | -------------- | --------- |
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| x | x |x | x | x |
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### 2. Structure Prediction (Inverse NMR)
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| **Method / Paper Title** | **Model Type / Approach** | **Metric(s)** | **Model Size** | **Notes** |
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| ------------------------ | ------------------------- | ------------- | -------------- | --------- |
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| x | x |x | x | x |
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### 3. Molecular Fingerprint Prediction (Spec2FP)
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| **Method / Paper Title** | **Model Type / Approach** | **Metric(s)** | **Model Size** | **Notes** |
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| ------------------------ | ------------------------- | ------------- | -------------- | --------- |
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| x | x |x | x | x |
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### 4. Functional Group Classification (Spec2Func)
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| **Method / Paper Title** | **Model Type / Approach** | **Metric(s)** | **Model Size** | **Notes** |
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| ------------------------ | ------------------------- | ------------- | -------------- | --------- |
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| x | x |x | x | x |
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### 5. Molecular Toxicity Prediction (Spec2Tox / ADMET)
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| **Method / Paper Title** | **Model Type / Approach** | **Metric(s)** | **Model Size** | **Notes** |
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| ------------------------ | ------------------------- | ------------- | -------------- | --------- |
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| x | x |x | x | x |
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---
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