Datasets:

ternarytx
/

flock

---
license: cc-by-4.0
pretty_name: Flock
configs:
- config_name: flock_pairs
  data_files: flock_pairs_v1_*.csv
- config_name: leakage_free_pairs
  data_files: leakage_free_pairs_v1_*.csv
- config_name: leaked_pairs
  data_files: leaked_pairs_v1_*.csv
- config_name: benchmark_targets
  data_files: benchmark_targets_v1_*.csv
- config_name: literature_negatives_detail
  data_files: literature_negatives_detail_v1_*.csv
- config_name: sequences
  data_files: sequences_v1_*.csv
- config_name: esmfold2_metrics
  data_files: esmfold2_metrics_v1_*.csv
- config_name: esmc_metrics
  data_files: esmc_metrics_v1_*.csv
- config_name: zhang_pairs
  data_files: zhang_pairs_v1_*.csv
- config_name: zhang_sequences
  data_files: zhang_sequences_v1_*.csv
- config_name: zhang_esmc_metrics
  data_files: zhang_esmc_metrics_v1_*.csv
# TODO: swap the globs for a typed dataset_info block (features + per-config row
# counts) so the schema renders on the dataset page. See
# Synthyra/bernett_gold_ppi for the shape. Note the globs match any dated build,
# so keep only one date per file in the repo.
---
# Flock: A negative-enriched protein-protein interaction dataset - Dataset Card

**Code:** https://github.com/TernaryTx/flock

## Overview

## Files

| file | rows | contents |
|---|---|---|
| `flock_pairs` | 404,577 | the full dataset: one row per unordered pair, with label, source and leakage flags and examples |
| `leakage_free_pairs` | 16,210 | a benchmark for cofolding models: 335 targets, leakage-filtered |
| `leaked_pairs` | 14,402 | 298 targets whose pairs are leaked, as a contrast set |
| `benchmark_targets` | 610 | per-target structural metadata across both leaked and leakage-free sets |
| `literature_negatives_detail` | 10,480 | evidence behind each literature-mined negative pair |
| `sequences` | 5,981 | exact sequences used in paper benchmarks |
| `esmfold2_metrics` | 24,022 | ESMFold2-Fast scores on both benchmark sets, labelled by `set` |
| `esmc_metrics` | 48,044 | ESMC scores on the same pairs, labelled by `set` and by `feature_corpus` |
| `zhang_pairs` | 12,635 | the external Zhang et al. test set, homology-filtered |
| `zhang_sequences` | 9,820 | exact sequences scored over the Zhang et al. set |
| `zhang_esmc_metrics` | 12,635 | ESMC scores on the Zhang et al. set |

**`flock_pairs`** is ordered so `protein_a < protein_b` alphabetically, and no pair
appears twice. 26,934 positive and 377,643 negative pairs. 8,310 pairs from PINDER alone, 4,980 from PPI3D alone, and 13,644 pairs from both (indicated in the `positive_source` column). `negative_source` is `pdb` for 368,937 and `literature` for 8,706. Each label's source column is empty on the other label. Gene names are present for 383,801 of the 404,577 pairs. The four leakage columns are described under [Leakage](#leakage).
**`leakage_free_pairs`** and **`leaked_pairs`** carry `target`, `partner` and
`type` only. They are directed; the orientation of pairs is meaningful since we calculate per-target metrics. Everything else about a pair lives in `flock_pairs` and can be joined on the unordered pair.

**`benchmark_targets`** gives `min_num_chains` and `max_num_chains` over the
target's source complexes, `min_buried_sasa` and `max_buried_sasa` in Å², and one
representative `example_pdb` so a reader can look at a real structure. Gene names
are present for 546 of the 610 targets.
**`literature_negatives_detail`** holds 10,480 rows over 10,082 pairs. A
pair supported by two papers gets a row for each. Alongside the accessions it
contains the source paper (`pmid`, `pmcid`, `text_source`), the sentence relied on
and where it appeared (`evidence_sentence`, `evidence_location`), the model's
call and its stated `reasoning`, and the curation verdict.

> `usable_as_negative` and `published_as_negative` are not the same. Curation accepting a pair is not the last word — a final filter drops some of them. 8,896 rows, covering all 8,706 published literature negatives, are `published_as_negative`; the other 1,584 rows describe pairs that did not make it. Every published literature negative has at least one evidence row.

**`sequences`** gives `accession`, `sequence` and `length` for the two benchmark
sets.

**`esmfold2_metrics`** contains ESMFold2-Fast [1] scores for both benchmark
sets in one file, with the benchmark set labelled in the `set` column: 10,573 pairs over 318 targets
for `leakage_free`, and 13,449 pairs over 298 targets for `leaked`. It contains
`ptm`, `iptm`, `avg_plddt`, `pair_chains_iptm`, the interface-restricted
`iptm_d0chn_A_P` and three ipSAE [2] variants, alongside `n_residues` and
`predict_seconds`.
**`esmc_metrics`** contains ESMC [1] scores for the same pairs as
`esmfold2_metrics`, with the ESMC features normalized over two different feature corpora (see below). 48,044 rows in total. Its score column is `npmi_score`, alongside `set`, `feature_corpus`, `method`, `target`,
`partner`, `pair_key`, `n_residues`, `type`, `status` and `imputed_zero`.
`status` is `OK` on every row.

ESMC scores a pair from the normalised pointwise mutual information (NPMI)
between the sparse autoencoder features of the two proteins, counted over a
corpus of PDB structures [1]. We provide ESMC scores normalized against two different feature corpora, both drawn from PPI3D [6] and labelled in `feature_corpus`:

- `full` — the corpus up to 2026-07-29, 631,509 feature pairs.
- `pre_2023-06-01_strict` — the corpus restricted to structures released before
  the benchmark cutoff, 478,830 feature pairs. This allows fair comparisons to
  ESMFold2 on the Leakage-Free set. The date cut is taken per structure cluster
  rather than per structure: PPI3D clusters interfaces at 40% sequence identity
  and 50% interface contact similarity, and any cluster holding a member
  released on or after the cutoff is dropped whole rather than falling back to
  its best surviving pre-cutoff member, so no interface family left in the
  corpus grew after the cutoff.

Each corpus covers the exact same 24,022 pairs. 426 pairs per corpus (188 `leakage_free` and 238 `leaked`) are scored `0` and
marked `imputed_zero`: ESMC found no residue in one of the two proteins whose
features passed the activation threshold.

**`zhang_pairs`**, **`zhang_sequences`** and **`zhang_esmc_metrics`** are an
external test set and our result on it, they share no pairs, targets or leakage flags with any file above.

`zhang_pairs` is the human PPI control set of Zhang et al. [15], parsed from
their Data S1. Their positives are 3,000 pairs sampled from the 3,988 confident
PPIs that STRING, BioGRID and UniProt all agree on; their negatives are 30,000
random human protein pairs with no evidence of interaction. We then filtered that
set for homology against the PPI3D [6] structures that fed into our NPMI feature
table, using MMseqs2 clustering [16] at 30% sequence identity and 80% coverage. 6,409 of its
17,266 proteins matched the corpus at that threshold, taking it from 32,747 pairs
(3,000 positive and 29,747 negative) to the 12,635 published here: 738 positive
and 11,897 negative over 9,820 proteins. It carries `protein_a`, `protein_b` and
`type`, with the accessions sorted as in `flock_pairs`.

`zhang_sequences` gives `accession`, `sequence` and `length` for all 9,820
proteins, the exact sequences that were scored.

`zhang_esmc_metrics` holds ESMC's NPMI score over that set against the `full`
feature corpus, one row per pair. Alongside `npmi_score` it carries the per-side
active feature counts `n_active_a`, `n_active_b`, `n_selected` and `n_positive`,
plus `status` and `imputed_zero`. Pooled AUROC is 0.9015 imputing the 67
no-feature pairs to zero, or 0.9010 dropping them.

> **Reproduction note:** We endeavoured to reproduce the published ESMC method as best as possible; however some details and parameters were not described in their paper. In particular the exact date cutoff behind their feature corpus is not provided; our PPI3D cutoff of 2026-07-29 may be slightly later than theirs and as such our NPMI set may be slightly bigger. In our paper we report comparable performance on this set.

## Loading

To train models on Flock, load the data using HuggingFace`Datasets`:

```python
from datasets import load_dataset
pairs = load_dataset("ternarytx/flock", "flock_pairs", split="train")
```

Other configurations are `leakage_free_pairs`, `leaked_pairs`, `benchmark_targets`,
`literature_negatives_detail`, `sequences`, `esmfold2_metrics`, `esmc_metrics`,
`zhang_pairs`, `zhang_sequences` and `zhang_esmc_metrics`.

```python
print(pairs)
# Dataset({
#     features: ['protein_a', 'protein_b', 'gene_a', 'gene_b', 'label',
#                'positive_source', 'negative_source', 'in_training_set',
#                'homolog_in_training_set', 'in_training_set_entry',
#                'homolog_in_training_set_entry'],
#     num_rows: 404577
# })
print(pairs[0])
# {'protein_a': 'A0A009I821', 'protein_b': 'A0A1V3DIZ9',
#  'gene_a': 'rplV', 'gene_b': 'rpsP',
#  'label': 'Negative', 'positive_source': None, 'negative_source': 'pdb',
#  'in_training_set': True, 'homolog_in_training_set': True,
#  'in_training_set_entry': '7M4W', 'homolog_in_training_set_entry': '7M4W'}
from collections import Counter
print(Counter(pairs["label"]))
# Counter({'Negative': 377643, 'Positive': 26934})
print(Counter(pairs["negative_source"]))
# Counter({'pdb': 368937, None: 26934, 'literature': 8706})
# "None" pairs are positives
```

The CSVs can also be read directly for analysis and plots:

```python
import pandas as pd
from huggingface_hub import hf_hub_download
def flock_csv(name: str) -> pd.DataFrame:
    return pd.read_csv(
        hf_hub_download("ternarytx/flock", name, repo_type="dataset"),
    )
flock = flock_csv("flock_pairs_v1_2026-08-24.csv")
benchmark = flock_csv("leakage_free_pairs_v1_2026-08-24.csv")
sequences = flock_csv("sequences_v1_2026-08-24.csv")
```

`leakage_free_pairs` and `leaked_pairs` are keyed on `target` and `partner`, while `flock_pairs` is
keyed on unordered pairs. To join them, first sort on the two accessions:

```python
# Sort alphabetically to match Flock pairs
benchmark["protein_a"] = benchmark[["target", "partner"]].min(axis=1)
benchmark["protein_b"] = benchmark[["target", "partner"]].max(axis=1)
annotated = benchmark.merge(flock, on=["protein_a", "protein_b"], how="left")
```

To attach sequences to a benchmark pair, map each side in turn:

```python
lookup = sequences.set_index("accession")["sequence"]
benchmark["target_sequence"] = benchmark["target"].map(lookup)
benchmark["partner_sequence"] = benchmark["partner"].map(lookup)
```

## Leakage
A pair in Flock is labelled as leaked if it (or any homologous pair) could have appeared in the training set of a cofolding model. The Boltz-2 [3] cutoff date of `2023-06-01` is used, which also covers any model matching the AlphaFold 3 [4] training cutoff date (OpenFold3, ESMFold2, Protenix, Chai-1 etc.).

### Leakage Flags

- `in_training_set` denotes pairs where a pre-cutoff PDB entry contains both exact proteins: the complex itself for a positive, or co-presence in one entry for a negative.
    - Positives 21,244 True / 5,690 False. Negatives 223,227 True / 154,416 False.
- `homolog_in_training_set` denotes pairs where a similar pair exists in a pre-cutoff PDB entry. Interface-cluster match to a pre-cutoff complex for positives; sequence identity at 40% or above to a pre-cutoff pair for negatives.
    - Positives 22,204 True / 4,730 False. Negatives 283,232 True / 94,411 False.

- Clean on both flags: 3,275 positives and 86,827 negatives.

Only pairs with both flags `False` are kept. Then, we filter to a set of targets such that each target has at least 2 clean positive and 20 clean negative partners.

This gives `leakage_free_pairs`, 16,210 rows over 335 targets.

### Pre-Cutoff Entry Examples

`flock_pairs` contains columns `in_training_set_entry` and `homolog_in_training_set_entry`. Each names a PDB entry that supports the flag.

**They are populated for negatives only.** A negative's flags are decided per pair, against a specific PDB entry: the one putting both proteins in a single pre-cutoff structure, or the one holding the homologous pair.  Every negative pair has at least one example leaked entry.

A positive's flags are decided against a set; the release dates of all its source complexes for `in_training_set`, and membership of an interface cluster for `homolog_in_training_set`. As such there is no single entry to point at and these two example entry columns are unpopulated.

### Leaked Pairs

The `leaked_pairs` set is a contrast dataset built from pairs that failed the leakage filter, matched on length distribution and with the same filters (at least 2 positives and 20 negatives) as the benchmark experiment ran on the leakage free set. This is so that scores can be compared against pairs a model may have memorised (see paper for details). The scores behind that comparison are in `esmfold2_metrics` and `esmc_metrics`, each holding both sets.

***Note***: a protein can appear in both leakage-free and leaked sets. Leakage is a property of pairs, not individual proteins. For example, A8JFK6 has 64 pairs in Flock, 33 clean and 31 leaked, so it is a clean target with 33 partners and a leaked target with 31 different ones. **No pair appears in both leakage-free and leaked sets**.

## Sources

The access dates bound what could have leaked, so they are given rather than
implied. The PPI3D pull is eight months later than the PINDER snapshot, and
supplies the structures released in between.

| Source | Used for | Date accessed | Reference |
|---|---|---|---|
| PINDER | positive pairs | 2025-12-01 | [5] |
| PPI3D | positive pairs | 2026-08-04 | [6] |
| RCSB PDB | source structures, and the release dates behind the date leakage flag | 2026-08-24 | [7] |
| IntAct | filtering known interactions out of the negatives | 2026-08-17 | [8] |
| Europe PMC | full-text corpus the literature negatives were mined from | 2026-08-05 | [9, 10] |
| UniProt | sequences, accessions and gene names | 2026-08-24 | [11] |
| AlphaFold DB | sequence fallback where UniProt returns empty | 2026-08-24 | [12, 13] |
| Zhang et al. | the external test set in `zhang_pairs`, parsed from Data S1 | 2026-08-21 | [15] |

## Licence

Flock is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Use it for anything, including commercially, provided you credit this dataset and its sources by citing the paper and the references below.

The sources carry their own terms, all of which permit redistribution here:

| Source | Terms |
|---|---|
| PINDER [5] | Apache-2.0; no separate licence is stated for the data |
| PPI3D [6] | CC BY 4.0, with a request to cite the PDB alongside it |
| RCSB PDB [7] | CC0 |
| UniProt [11] | CC BY 4.0 |
| AlphaFold DB [12, 13] | CC BY 4.0 |

The pairs in `zhang_pairs` and `zhang_esmc_metrics` are a filtered subset of the control sets in Data S1 of Zhang et al. [15], redistributed here with attribution so that the scores published beside them can be checked.

Two additional sources were used to generate this dataset but do not appear in it. **IntAct** [8] is used only as a filter, to remove pairs with curated evidence of interaction from the negatives, so no IntAct records are redistributed here. **Negatome 2.0** [14] inspired this paper, and while this work builds on the methods used there, the data presented here is generated from different inputs, sources, and code.

`literature_negatives_detail` quotes short passages from the articles it cites by PMID [9]. Rights in that quoted text remain with the original publishers.

## References

1. Candido, S. et al. Language Modeling Materializes a World Model of Protein Biology. 2026.06.03.729735 Preprint at https://doi.org/10.64898/2026.06.03.729735 (2026).
2. Dunbrack, R. L. Rēs ipSAE loquuntur: What's wrong with AlphaFold's ipTM score and how to fix it. 2025.02.10.637595 Preprint at https://doi.org/10.1101/2025.02.10.637595 (2025).
3. Passaro, S. et al. Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. bioRxiv 2025.06.14.659707 (2025) doi:10.1101/2025.06.14.659707.
4. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
5. Kovtun, D. et al. PINDER: The protein interaction dataset and evaluation resource. 2024.07.17.603980 Preprint at https://doi.org/10.1101/2024.07.17.603980 (2024).
6. Dapkūnas, J., Timinskas, A., Olechnovič, K., Tomkuvienė, M. & Venclovas, Č. PPI3D: a web server for searching, analyzing and modeling protein–protein, protein–peptide and protein–nucleic acid interactions. Nucleic Acids Res 52, W264–W271 (2024).
7. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res 28, 235–242 (2000).
8. Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42, D358–D363 (2014).
9. Rosonovski, S. et al. Europe PMC in 2023. Nucleic Acids Res 52, D1668–D1676 (2024).
10. Kafkas, Ş. et al. Section level search functionality in Europe PMC. J Biomed Semant 6, 7 (2015).
11. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res 53, D609–D617 (2025).
12. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
13. Varadi, M. et al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Res 52, D368–D375 (2024).
14. Blohm, P. et al. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res 42, D396-400 (2014).
15. Zhang, J. et al. Predicting protein-protein interactions in the human proteome. Science 390, eadt1630 (2025).
16. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017).

	---
	license: cc-by-4.0
	pretty_name: Flock
	configs:
	- config_name: flock_pairs
	data_files: flock_pairs_v1_*.csv
	- config_name: leakage_free_pairs
	data_files: leakage_free_pairs_v1_*.csv
	- config_name: leaked_pairs
	data_files: leaked_pairs_v1_*.csv
	- config_name: benchmark_targets
	data_files: benchmark_targets_v1_*.csv
	- config_name: literature_negatives_detail
	data_files: literature_negatives_detail_v1_*.csv
	- config_name: sequences
	data_files: sequences_v1_*.csv
	- config_name: esmfold2_metrics
	data_files: esmfold2_metrics_v1_*.csv
	- config_name: esmc_metrics
	data_files: esmc_metrics_v1_*.csv
	- config_name: zhang_pairs
	data_files: zhang_pairs_v1_*.csv
	- config_name: zhang_sequences
	data_files: zhang_sequences_v1_*.csv
	- config_name: zhang_esmc_metrics
	data_files: zhang_esmc_metrics_v1_*.csv
	# TODO: swap the globs for a typed dataset_info block (features + per-config row
	# counts) so the schema renders on the dataset page. See
	# Synthyra/bernett_gold_ppi for the shape. Note the globs match any dated build,
	# so keep only one date per file in the repo.
	---

	# Flock: A negative-enriched protein-protein interaction dataset - Dataset Card

	Code: https://github.com/TernaryTx/flock

	## Overview

	## Files

	\| file \| rows \| contents \|
	\|---\|---\|---\|
	\| `flock_pairs` \| 404,577 \| the full dataset: one row per unordered pair, with label, source and leakage flags and examples \|
	\| `leakage_free_pairs` \| 16,210 \| a benchmark for cofolding models: 335 targets, leakage-filtered \|
	\| `leaked_pairs` \| 14,402 \| 298 targets whose pairs are leaked, as a contrast set \|
	\| `benchmark_targets` \| 610 \| per-target structural metadata across both leaked and leakage-free sets \|
	\| `literature_negatives_detail` \| 10,480 \| evidence behind each literature-mined negative pair \|
	\| `sequences` \| 5,981 \| exact sequences used in paper benchmarks \|
	\| `esmfold2_metrics` \| 24,022 \| ESMFold2-Fast scores on both benchmark sets, labelled by `set` \|
	\| `esmc_metrics` \| 48,044 \| ESMC scores on the same pairs, labelled by `set` and by `feature_corpus` \|
	\| `zhang_pairs` \| 12,635 \| the external Zhang et al. test set, homology-filtered \|
	\| `zhang_sequences` \| 9,820 \| exact sequences scored over the Zhang et al. set \|
	\| `zhang_esmc_metrics` \| 12,635 \| ESMC scores on the Zhang et al. set \|

	`flock_pairs` is ordered so `protein_a < protein_b` alphabetically, and no pair
	appears twice. 26,934 positive and 377,643 negative pairs. 8,310 pairs from PINDER alone, 4,980 from PPI3D alone, and 13,644 pairs from both (indicated in the `positive_source` column). `negative_source` is `pdb` for 368,937 and `literature` for 8,706. Each label's source column is empty on the other label. Gene names are present for 383,801 of the 404,577 pairs. The four leakage columns are described under [Leakage](#leakage).

	`leakage_free_pairs` and `leaked_pairs` carry `target`, `partner` and
	`type` only. They are directed; the orientation of pairs is meaningful since we calculate per-target metrics. Everything else about a pair lives in `flock_pairs` and can be joined on the unordered pair.

	`benchmark_targets` gives `min_num_chains` and `max_num_chains` over the
	target's source complexes, `min_buried_sasa` and `max_buried_sasa` in Å², and one
	representative `example_pdb` so a reader can look at a real structure. Gene names
	are present for 546 of the 610 targets.

	`literature_negatives_detail` holds 10,480 rows over 10,082 pairs. A
	pair supported by two papers gets a row for each. Alongside the accessions it
	contains the source paper (`pmid`, `pmcid`, `text_source`), the sentence relied on
	and where it appeared (`evidence_sentence`, `evidence_location`), the model's
	call and its stated `reasoning`, and the curation verdict.

	> `usable_as_negative` and `published_as_negative` are not the same. Curation accepting a pair is not the last word — a final filter drops some of them. 8,896 rows, covering all 8,706 published literature negatives, are `published_as_negative`; the other 1,584 rows describe pairs that did not make it. Every published literature negative has at least one evidence row.

	`sequences` gives `accession`, `sequence` and `length` for the two benchmark
	sets.

	`esmfold2_metrics` contains ESMFold2-Fast [1] scores for both benchmark
	sets in one file, with the benchmark set labelled in the `set` column: 10,573 pairs over 318 targets
	for `leakage_free`, and 13,449 pairs over 298 targets for `leaked`. It contains
	`ptm`, `iptm`, `avg_plddt`, `pair_chains_iptm`, the interface-restricted
	`iptm_d0chn_A_P` and three ipSAE [2] variants, alongside `n_residues` and
	`predict_seconds`.

	`esmc_metrics` contains ESMC [1] scores for the same pairs as
	`esmfold2_metrics`, with the ESMC features normalized over two different feature corpora (see below). 48,044 rows in total. Its score column is `npmi_score`, alongside `set`, `feature_corpus`, `method`, `target`,
	`partner`, `pair_key`, `n_residues`, `type`, `status` and `imputed_zero`.
	`status` is `OK` on every row.

	ESMC scores a pair from the normalised pointwise mutual information (NPMI)
	between the sparse autoencoder features of the two proteins, counted over a
	corpus of PDB structures [1]. We provide ESMC scores normalized against two different feature corpora, both drawn from PPI3D [6] and labelled in `feature_corpus`:

	- `full` — the corpus up to 2026-07-29, 631,509 feature pairs.
	- `pre_2023-06-01_strict` — the corpus restricted to structures released before
	the benchmark cutoff, 478,830 feature pairs. This allows fair comparisons to
	ESMFold2 on the Leakage-Free set. The date cut is taken per structure cluster
	rather than per structure: PPI3D clusters interfaces at 40% sequence identity
	and 50% interface contact similarity, and any cluster holding a member
	released on or after the cutoff is dropped whole rather than falling back to
	its best surviving pre-cutoff member, so no interface family left in the
	corpus grew after the cutoff.

	Each corpus covers the exact same 24,022 pairs. 426 pairs per corpus (188 `leakage_free` and 238 `leaked`) are scored `0` and
	marked `imputed_zero`: ESMC found no residue in one of the two proteins whose
	features passed the activation threshold.

	`zhang_pairs`, `zhang_sequences` and `zhang_esmc_metrics` are an
	external test set and our result on it, they share no pairs, targets or leakage flags with any file above.

	`zhang_pairs` is the human PPI control set of Zhang et al. [15], parsed from
	their Data S1. Their positives are 3,000 pairs sampled from the 3,988 confident
	PPIs that STRING, BioGRID and UniProt all agree on; their negatives are 30,000
	random human protein pairs with no evidence of interaction. We then filtered that
	set for homology against the PPI3D [6] structures that fed into our NPMI feature
	table, using MMseqs2 clustering [16] at 30% sequence identity and 80% coverage. 6,409 of its
	17,266 proteins matched the corpus at that threshold, taking it from 32,747 pairs
	(3,000 positive and 29,747 negative) to the 12,635 published here: 738 positive
	and 11,897 negative over 9,820 proteins. It carries `protein_a`, `protein_b` and
	`type`, with the accessions sorted as in `flock_pairs`.

	`zhang_sequences` gives `accession`, `sequence` and `length` for all 9,820
	proteins, the exact sequences that were scored.

	`zhang_esmc_metrics` holds ESMC's NPMI score over that set against the `full`
	feature corpus, one row per pair. Alongside `npmi_score` it carries the per-side
	active feature counts `n_active_a`, `n_active_b`, `n_selected` and `n_positive`,
	plus `status` and `imputed_zero`. Pooled AUROC is 0.9015 imputing the 67
	no-feature pairs to zero, or 0.9010 dropping them.

	> Reproduction note: We endeavoured to reproduce the published ESMC method as best as possible; however some details and parameters were not described in their paper. In particular the exact date cutoff behind their feature corpus is not provided; our PPI3D cutoff of 2026-07-29 may be slightly later than theirs and as such our NPMI set may be slightly bigger. In our paper we report comparable performance on this set.

	## Loading

	To train models on Flock, load the data using HuggingFace`Datasets`:

	```python
	from datasets import load_dataset

	pairs = load_dataset("ternarytx/flock", "flock_pairs", split="train")
	```

	Other configurations are `leakage_free_pairs`, `leaked_pairs`, `benchmark_targets`,
	`literature_negatives_detail`, `sequences`, `esmfold2_metrics`, `esmc_metrics`,
	`zhang_pairs`, `zhang_sequences` and `zhang_esmc_metrics`.

	```python
	print(pairs)
	# Dataset({
	# features: ['protein_a', 'protein_b', 'gene_a', 'gene_b', 'label',
	# 'positive_source', 'negative_source', 'in_training_set',
	# 'homolog_in_training_set', 'in_training_set_entry',
	# 'homolog_in_training_set_entry'],
	# num_rows: 404577
	# })

	print(pairs[0])
	# {'protein_a': 'A0A009I821', 'protein_b': 'A0A1V3DIZ9',
	# 'gene_a': 'rplV', 'gene_b': 'rpsP',
	# 'label': 'Negative', 'positive_source': None, 'negative_source': 'pdb',
	# 'in_training_set': True, 'homolog_in_training_set': True,
	# 'in_training_set_entry': '7M4W', 'homolog_in_training_set_entry': '7M4W'}

	from collections import Counter

	print(Counter(pairs["label"]))
	# Counter({'Negative': 377643, 'Positive': 26934})

	print(Counter(pairs["negative_source"]))
	# Counter({'pdb': 368937, None: 26934, 'literature': 8706})
	# "None" pairs are positives
	```

	The CSVs can also be read directly for analysis and plots:

	```python
	import pandas as pd
	from huggingface_hub import hf_hub_download


	def flock_csv(name: str) -> pd.DataFrame:
	return pd.read_csv(
	hf_hub_download("ternarytx/flock", name, repo_type="dataset"),
	)


	flock = flock_csv("flock_pairs_v1_2026-08-24.csv")
	benchmark = flock_csv("leakage_free_pairs_v1_2026-08-24.csv")
	sequences = flock_csv("sequences_v1_2026-08-24.csv")
	```

	`leakage_free_pairs` and `leaked_pairs` are keyed on `target` and `partner`, while `flock_pairs` is
	keyed on unordered pairs. To join them, first sort on the two accessions:

	```python
	# Sort alphabetically to match Flock pairs
	benchmark["protein_a"] = benchmark[["target", "partner"]].min(axis=1)
	benchmark["protein_b"] = benchmark[["target", "partner"]].max(axis=1)
	annotated = benchmark.merge(flock, on=["protein_a", "protein_b"], how="left")
	```

	To attach sequences to a benchmark pair, map each side in turn:

	```python
	lookup = sequences.set_index("accession")["sequence"]
	benchmark["target_sequence"] = benchmark["target"].map(lookup)
	benchmark["partner_sequence"] = benchmark["partner"].map(lookup)
	```

	## Leakage
	A pair in Flock is labelled as leaked if it (or any homologous pair) could have appeared in the training set of a cofolding model. The Boltz-2 [3] cutoff date of `2023-06-01` is used, which also covers any model matching the AlphaFold 3 [4] training cutoff date (OpenFold3, ESMFold2, Protenix, Chai-1 etc.).

	### Leakage Flags

	- `in_training_set` denotes pairs where a pre-cutoff PDB entry contains both exact proteins: the complex itself for a positive, or co-presence in one entry for a negative.
	- Positives 21,244 True / 5,690 False. Negatives 223,227 True / 154,416 False.
	- `homolog_in_training_set` denotes pairs where a similar pair exists in a pre-cutoff PDB entry. Interface-cluster match to a pre-cutoff complex for positives; sequence identity at 40% or above to a pre-cutoff pair for negatives.
	- Positives 22,204 True / 4,730 False. Negatives 283,232 True / 94,411 False.

	- Clean on both flags: 3,275 positives and 86,827 negatives.

	Only pairs with both flags `False` are kept. Then, we filter to a set of targets such that each target has at least 2 clean positive and 20 clean negative partners.

	This gives `leakage_free_pairs`, 16,210 rows over 335 targets.

	### Pre-Cutoff Entry Examples

	`flock_pairs` contains columns `in_training_set_entry` and `homolog_in_training_set_entry`. Each names a PDB entry that supports the flag.

	They are populated for negatives only. A negative's flags are decided per pair, against a specific PDB entry: the one putting both proteins in a single pre-cutoff structure, or the one holding the homologous pair. Every negative pair has at least one example leaked entry.

	A positive's flags are decided against a set; the release dates of all its source complexes for `in_training_set`, and membership of an interface cluster for `homolog_in_training_set`. As such there is no single entry to point at and these two example entry columns are unpopulated.

	### Leaked Pairs

	The `leaked_pairs` set is a contrast dataset built from pairs that failed the leakage filter, matched on length distribution and with the same filters (at least 2 positives and 20 negatives) as the benchmark experiment ran on the leakage free set. This is so that scores can be compared against pairs a model may have memorised (see paper for details). The scores behind that comparison are in `esmfold2_metrics` and `esmc_metrics`, each holding both sets.

	*Note: a protein can appear in both leakage-free and leaked sets. Leakage is a property of pairs, not individual proteins. For example, A8JFK6 has 64 pairs in Flock, 33 clean and 31 leaked, so it is a clean target with 33 partners and a leaked target with 31 different ones. No pair appears in both leakage-free and leaked sets*.

	## Sources

	The access dates bound what could have leaked, so they are given rather than
	implied. The PPI3D pull is eight months later than the PINDER snapshot, and
	supplies the structures released in between.

	\| Source \| Used for \| Date accessed \| Reference \|
	\|---\|---\|---\|---\|
	\| PINDER \| positive pairs \| 2025-12-01 \| [5] \|
	\| PPI3D \| positive pairs \| 2026-08-04 \| [6] \|
	\| RCSB PDB \| source structures, and the release dates behind the date leakage flag \| 2026-08-24 \| [7] \|
	\| IntAct \| filtering known interactions out of the negatives \| 2026-08-17 \| [8] \|
	\| Europe PMC \| full-text corpus the literature negatives were mined from \| 2026-08-05 \| [9, 10] \|
	\| UniProt \| sequences, accessions and gene names \| 2026-08-24 \| [11] \|
	\| AlphaFold DB \| sequence fallback where UniProt returns empty \| 2026-08-24 \| [12, 13] \|
	\| Zhang et al. \| the external test set in `zhang_pairs`, parsed from Data S1 \| 2026-08-21 \| [15] \|

	## Licence

	Flock is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Use it for anything, including commercially, provided you credit this dataset and its sources by citing the paper and the references below.

	The sources carry their own terms, all of which permit redistribution here:

	\| Source \| Terms \|
	\|---\|---\|
	\| PINDER [5] \| Apache-2.0; no separate licence is stated for the data \|
	\| PPI3D [6] \| CC BY 4.0, with a request to cite the PDB alongside it \|
	\| RCSB PDB [7] \| CC0 \|
	\| UniProt [11] \| CC BY 4.0 \|
	\| AlphaFold DB [12, 13] \| CC BY 4.0 \|

	The pairs in `zhang_pairs` and `zhang_esmc_metrics` are a filtered subset of the control sets in Data S1 of Zhang et al. [15], redistributed here with attribution so that the scores published beside them can be checked.

	Two additional sources were used to generate this dataset but do not appear in it. IntAct [8] is used only as a filter, to remove pairs with curated evidence of interaction from the negatives, so no IntAct records are redistributed here. Negatome 2.0 [14] inspired this paper, and while this work builds on the methods used there, the data presented here is generated from different inputs, sources, and code.

	`literature_negatives_detail` quotes short passages from the articles it cites by PMID [9]. Rights in that quoted text remain with the original publishers.

	## References

	1. Candido, S. et al. Language Modeling Materializes a World Model of Protein Biology. 2026.06.03.729735 Preprint at https://doi.org/10.64898/2026.06.03.729735 (2026).
	2. Dunbrack, R. L. Rēs ipSAE loquuntur: What's wrong with AlphaFold's ipTM score and how to fix it. 2025.02.10.637595 Preprint at https://doi.org/10.1101/2025.02.10.637595 (2025).
	3. Passaro, S. et al. Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. bioRxiv 2025.06.14.659707 (2025) doi:10.1101/2025.06.14.659707.
	4. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
	5. Kovtun, D. et al. PINDER: The protein interaction dataset and evaluation resource. 2024.07.17.603980 Preprint at https://doi.org/10.1101/2024.07.17.603980 (2024).
	6. Dapkūnas, J., Timinskas, A., Olechnovič, K., Tomkuvienė, M. & Venclovas, Č. PPI3D: a web server for searching, analyzing and modeling protein–protein, protein–peptide and protein–nucleic acid interactions. Nucleic Acids Res 52, W264–W271 (2024).
	7. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res 28, 235–242 (2000).
	8. Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42, D358–D363 (2014).
	9. Rosonovski, S. et al. Europe PMC in 2023. Nucleic Acids Res 52, D1668–D1676 (2024).
	10. Kafkas, Ş. et al. Section level search functionality in Europe PMC. J Biomed Semant 6, 7 (2015).
	11. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res 53, D609–D617 (2025).
	12. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
	13. Varadi, M. et al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Res 52, D368–D375 (2024).
	14. Blohm, P. et al. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res 42, D396-400 (2014).
	15. Zhang, J. et al. Predicting protein-protein interactions in the human proteome. Science 390, eadt1630 (2025).
	16. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017).