| --- |
| license: mit |
| task_categories: |
| - other |
| tags: |
| - drug-target-interaction |
| - bioinformatics |
| - chemistry |
| - proteins |
| pretty_name: MERGED (SPRINT) — preprocessed for DTI model training |
| size_categories: |
| - 10M<n<100M |
| configs: |
| - config_name: train_positive |
| data_files: |
| - split: train |
| path: merged_train_pos.parquet |
| - config_name: train_negative_pool |
| data_files: |
| - split: train |
| path: merged_train_neg_pool.parquet |
| - config_name: validation |
| data_files: |
| - split: validation |
| path: merged_val_eval.parquet |
| - config_name: test |
| data_files: |
| - split: test |
| path: merged_test_eval.parquet |
| - config_name: targets |
| data_files: |
| - split: train |
| path: merged_targets.parquet |
| --- |
| |
| # MERGED (SPRINT) — preprocessed for DTI model training |
|
|
| Preprocessed, ready-to-train version of the **MERGED** drug-target interaction dataset |
| introduced by **SPRINT**: |
|
|
| > McNutt, A. T., Adduri, A., Ellington, C. N., et al. (2024). |
| > *Sprint: Vector-based screening of protein-ligand interactions.* |
| > arXiv:[2411.15418](https://arxiv.org/abs/2411.15418) |
| > Code: [abhinadduri/panspecies-dti](https://github.com/abhinadduri/panspecies-dti) (MIT) |
|
|
| MERGED compiles interactions from **PubChem BioAssay, BindingDB, and ChEMBL** into a |
| single binary (binder / non-binder) benchmark with MMSeqs2-clustered train/val/test |
| splits, covering ~9,067 targets (paper-reported) and 3.5M ligands. |
|
|
| This repo is a **derived, filtered, feature-extracted** version of that data, |
| produced for pretraining drug-target interaction models with a pre-computed ligand |
| fingerprint matrix and cleaned, split-ready interaction tables. |
|
|
| ## What was done to the raw data |
|
|
| 1. **Target filtering**: of 12,065 raw UniProt target sequences, 2,678 (22%) were |
| junk (< 20 residues) and dropped, leaving 9,387 usable targets — matches the |
| paper's reported ~9,067 (small gap likely a stricter/different filter threshold). |
| 2. **Row filtering**: all six raw interaction tables (`{pos,neg} x {train,val,test}`) |
| filtered to rows referencing only non-junk targets. |
| 3. **Fixed eval sets**: `val`/`test` are each pre-sampled once (seed 42) at a 1:1 |
| positive:negative ratio and shuffled, so checkpoint selection is comparable across |
| training epochs. `train` keeps the **full** filtered positive set and the **full** |
| filtered negative pool on disk — a common recipe for this kind of heavily |
| imbalanced (94:1) binary DTI data is to resample a fresh negative subset from the |
| pool every training epoch rather than training on all ~64M negatives at once, so |
| that resampling is left to train time, not baked into this preprocessing. |
| 4. **Ligand features**: 2048-bit ECFP4 (Morgan, radius 2) fingerprints computed for |
| every one of the 2,005,744 ligands actually referenced by a kept interaction row |
| (out of 3.5M total in the raw dataset) via RDKit. |
|
|
| Verified against the paper's reported totals before processing (854,118 positive / |
| 80,681,825 negative raw interactions, 3,529,822 drugs) — exact match, i.e. this is |
| the complete, untampered dataset upstream of the filtering above. |
|
|
| ## Files |
|
|
| | File | Rows / shape | Description | |
| |---|---|---| |
| | `merged_train_pos.parquet` | 574,754 | Full filtered positive training interactions (`Drug_ID`, `Target_ID`) | |
| | `merged_train_neg_pool.parquet` | 64,361,902 | Full filtered negative training pool — resample from this per epoch | |
| | `merged_val_eval.parquet` | 215,950 | Fixed val set, 1:1 pos:neg, pre-shuffled, `Y` label included | |
| | `merged_test_eval.parquet` | 342,776 | Fixed test set, 1:1 pos:neg, pre-shuffled, `Y` label included | |
| | `merged_targets.parquet` | 5,488 | `Target_ID` (UniProt accession) → `Target` (AA sequence), restricted to targets actually referenced by kept rows | |
| | `merged_drugs_order.csv` | 2,005,744 | Row order for `merged_ligand_features.npy` (`Drug_ID` per row index) | |
| | `merged_ligand_features.npy` | (2,005,744, 2048) float32 | ECFP4 fingerprints, one row per `merged_drugs_order.csv` entry | |
|
|
| `Drug_ID` is an integer ligand ID (not a raw SMILES string — join against |
| `merged_drugs_order.csv`'s row index to recover the fingerprint). `Target_ID` is a |
| UniProt accession string despite the raw column being named `aa_seq` upstream. |
|
|
| ## License |
|
|
| MIT, inherited from the source `panspecies-dti` repository. Please cite the SPRINT |
| paper (above) if you use this data. |
|
|
