ukaikotaro/UNKAI-dataset

UNKAI Protein Pair Dataset

This repository contains protein-pair datasets used for UNKAI, a binary classification model that predicts whether two proteins are associated with the same enzymatic reaction.

Three dataset variants are provided:

• —original
• —seen_unseen
• —strict

Each dataset is divided into training, validation, and test splits.

Data format

Each TSV file contains two columns:

pair    label
A0RQT4_C1EZA3    1

pair

A pair of protein accession IDs represented as:

PROTEIN1_PROTEIN2

label

Binary classification label:

1 = same enzymatic reaction
0 = different enzymatic reaction

Repository structure

original/
├── train.tsv
├── validation.tsv
└── test.tsv

seen_unseen/
├── train.tsv
├── validation.tsv
└── test.tsv

strict/
├── train.tsv
├── validation.tsv
└── test.tsv

Dataset variants

Original

The original dataset uses a pair-level random split.

The positive and negative classes are balanced within each split.

No exact protein-pair duplicates are shared between train, validation, and test.

This split provides the least restrictive evaluation setting among the three released variants.

Seen-unseen

The seen-unseen dataset was constructed using protein clusters.

Protein clusters are first assigned to train, validation, or test groups.

Pairs are then assigned according to the following rules:

Training

Both proteins must belong to clusters assigned to training.

train cluster + train cluster

Validation

Exactly one protein belongs to a training cluster and the other belongs to a validation cluster.

train cluster + validation cluster

Test

Exactly one protein belongs to a training cluster and the other belongs to a test cluster.

train cluster + test cluster

Therefore, each validation/test pair contains one side from a cluster distribution observed during training and one side from an unseen cluster distribution.

The released dataset contains:

No exact protein-pair duplicates are shared between train, validation, and test.

Same-cluster pairs are excluded from the released seen-unseen setting because validation and test pairs necessarily consist of proteins from two differently assigned clusters.

Exact protein accessions from training may appear on the seen side of validation/test pairs by design.

Strict

The strict dataset provides the strongest cluster-separation setting.

Protein clusters are first divided into mutually exclusive train, validation, and test groups.

A protein pair is included in a split only when both proteins belong to clusters assigned to that same split.

Conceptually:

Train:
train cluster + train cluster

Validation:
validation cluster + validation cluster

Test:
test cluster + test cluster

Therefore, clusters used in validation and test are not present in the training cluster set.

The released strict dataset contains:

No exact protein-pair duplicates are shared between train, validation, and test.

Sampling controls

The cluster-based datasets were constructed with additional sampling controls to reduce excessive representation of individual proteins or clusters.

Reverse protein pairs are treated as identical:

A_B == B_A

This is appropriate for UNKAI because its pair representation is based on:

|v1 - v2|

which is symmetric with respect to protein order.

During final dataset selection, the following frequency limits were used:

During candidate collection, at most 40 examples per cluster-pair per label were retained before final sampling.

Cluster split

Protein clusters were randomly divided using seed 42.

Approximately:

80% training clusters
10% validation clusters
10% test clusters

were assigned before constructing the strict and seen-unseen datasets.

Pair overlap verification

The released TSV files were independently checked for exact pair overlap between splits.

For all three dataset variants:

train ∩ validation = 0
train ∩ test       = 0
validation ∩ test  = 0

Relationship between the datasets

The three datasets are intended to represent increasingly challenging generalization settings.

Original
   |
   | pair-level split
   v
Seen-unseen
   |
   | one unseen cluster side
   v
Strict
   |
   | completely cluster-separated
   v
stronger distribution shift

The strict dataset is therefore intended to provide a more conservative estimate of generalization to proteins from clusters not represented during training.

Models

Pretrained UNKAI checkpoints are available separately:

ukaikotaro/UNKAI

Source code and inference utilities:

https://github.com/ukai3313/UNKAI

Limitations

The labels describe enzymatic-reaction association according to the source data used to construct the protein pairs.

The datasets should not be interpreted as a complete representation of all protein functions or all enzyme reaction relationships.

Results obtained using different splitting strategies are not directly interchangeable. In particular, random pair-level evaluation can be substantially easier than evaluation under cluster separation.

Citation

Citation information for the associated publication will be added here.

License

MIT License.