---
license: mit
tags:
- chemistry
- biology
- medical
- molecular
- proteins
- smiles
- dna
- rna
- nucleotide
- amino_acid
pretty_name: Atomica Molecular Interactions | Sequence Data
size_categories:
- 100K<n<1M
---

## Dataset Card: Atomica Molecular Interactions | Sequence Data

### Summary  
A dataset of atomic‑scale molecular interaction interfaces, ready for ML workflows. Each row contains up to five interacting sequences (or SMILES), their modalities, an example ID, and the original data split.

### Citation  
```
@article{
  Fang2025ATOMICA,
  author = {Fang, Ada and Zhang, Zaixi and Zhou, Andrew and Zitnik, Marinka},
  title = {ATOMICA: Learning Universal Representations of Intermolecular Interactions},
  year = {2025},
  journal = {bioRxiv},
  doi = {10.1101/2025.04.02.646906},
}
```

### Raw files from ATOMICA Havard Dataverse

Available in: `karina-zadorozhny/ATOMICA/raw/`

| Interaction Type                  |   Count |
|---------------------|---------:|
| small_molecule-small_molecule            | 1,767,710 (86% self-pairs) |
| protein-protein     |   124,541 |
| protein-small_molecule     |   119,017 |
| protein-ion         |    74,514 |
| protein-peptide     |     8,475 |
| rna-small_molecule     |     5,185 |
| protein-rna         |     3,511 |
| protein-dna         |     2,750 |


Retrieved using:
```python
IDENTIFIERS = {
    "protein-peptide": "11033993",
    "protein-rna": "11033983",
    "protein-protein": "11033984",
    "rna-small_molecule": "11033985",
    "protein-small_molecule": "11033996",
    "protein-ion": "11033989",
    "protein-dna": "11033982",
    "small_molecule-small_molecule": "11033997",
}

for fname, identifier in IDENTIFIERS.items():
    pooch.retrieve(
                f"https://dataverse.harvard.edu/api/access/datafile/{identifier}",
                fname=f"{fname}.csv",
                known_hash=None,
                path=".",
                progressbar=True,
            )
```

## Processed Files

Imporant note: 
* Only contains sequences! This processed version does not contain any structural information unlike the original ATOMICA dataset.
* Only contains a subset of avaialable interactions
* See extra filtering steps applied to each interaction modality


| Interaction Type                  |   Count |
|-----------------------------------|--------:|
| small_molecule-small_molecule     | 246,728 |
| protein-protein                   |  60,624 |
| protein-small_molecule            |  32,120 |
| protein-dna                       |   2,087 |
| protein-rna                       |   1,785 |

The data is stored as Parquet files partitioned by:
- **split**: original data split (e.g. `train`, `validation`, `test`)  


Each file contains columns:
- `id` – unique interaction identifier  
- `sequence1`…`sequence5` – up to five extracted sequences or SMILES (strings, null if unused)  
- `modality1`…`modality5` – corresponding modalities (`amino_acid`, `nucleotide`, `smiles`)  

```
| interaction_type                  | sequence1                                                    | modality1    | sequence2                                                   | modality2   | sequence3                                                   | modality3   | sequence4 | modality4 | sequence5 | modality5 | split |
|-----------------------------------|--------------------------------------------------------------|--------------|-------------------------------------------------------------|-------------|-------------------------------------------------------------|-------------|-----------|-----------|-----------|-----------|-------|
| protein-dna                       | MAGVKNSIIWFRKGLRLHDNPALLEACKDAKHVYPVFVLDPHFLQQ...           | amino_acid   | CAGCGGTTGCCGTG                                             | nucleotide  | CACGGCAACCGCTG                                             | nucleotide  | None      | None      | None      | None      | test  |
| protein-protein                   | GPLGSPEFGRPGWVIGVNPDIGGAIAVLSPDGSSQVFDNPFVHIVV...           | amino_acid   | GPLGSPEFGRPGWVIGVNPDIGGAIAVLSPDGSSQVFDNPFVHIVV...           | amino_acid  | None                                                        | None        | None      | None      | None      | None      | test  |
| protein-rna                       | MHHHHHHENLYFQGSGMAGSVGLALCGQTLVVRGGSRFLATSIASS...           | amino_acid   | MAAETRNVAGAEAPPPQKRYYRQRAHSNPMADHTLRYPVKPEEMDW...           | amino_acid  | GCCCGGAUAGCUCAGUCGGUAGAGCAUCAGACUUUUAAUCUGAGGG...           | nucleotide  | None      | None      | None      | None      | test  |
| protein-small_molecule            | MTSNNLPTVLESIVEGRRGHLEEIRARIAHVDVDALPKSTRSLFDS...           | amino_acid   | O=C(O)c1ccccc1NC[C@@H](O)[C@H](O)[C@H](O)COP(=...           | smiles      | None                                                        | None        | None      | None      | None      | None      | test  |
| small_molecule-small_molecule     | c1ccccc1                                                    | smiles       | Cc1ccc([NH+]=P(C)(C)c2ccccc2)c(c1)S(=O)(=O)[O-]             | smiles      | None                                                        | None        | None      | None      | None      | None      | test  |
```

### Processing Overview

1. **Raw data retrieval**  
   - Download CSVs per interaction type from Harvard Dataverse via file IDs.  

2. **Sequence & modality extraction**  
   - **Protein–nucleotide / protein–RNA**  
     • Fetch all molecule entries for each PDB ID from PDBe REST API.  
     • Extract `sequence` and `molecule_type`.  
   - **Protein–protein**  
     • Identify the two chains in each interface ID from `id` 
     • Fetch PDB entries and select sequences for those chains.  
   - **Protein–small molecule / protein–ion**  
     • Extract protein sequences as above.  
     • Fetch ligand SMILES from PDBe API, canonicalize via RDKit.  
   - **Small molecule–small molecule**  
     • Split the raw ID string into two SMILES, drop self‑pairs (since we're only using sequence information)

3. **Data cleaning & formatting**  
   - Pad or truncate to exactly five sequence/modality slots.  
   - Map raw molecule types to `{amino_acid, nucleotide, smiles}`.
   - Random train,test,val split stratified on interaction type.