---
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.