---
dataset_info:
  features:
  - name: seq
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 19408437
    num_examples: 62478
  - name: test
    num_bytes: 2176357
    num_examples: 6942
  download_size: 21064069
  dataset_size: 21584794
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- text-classification
tags:
- chemistry
- biology
---


# Dataset Card for Solubility Prediction Dataset

### Dataset Summary

This solubility prediction task involves a binary classification of a heterogenous set of proteins, assessing them as either soluble or insoluble. The solubility metric is a crucial design parameter in ensuring protein efficacy, with particular relevance in the pharmaceutical domain. 

## Dataset Structure

### Data Instances
For each instance, there is a string representing the protein sequence and an integer label indicating that the protein sequence is soluble or insoluble. See the [solubility prediction dataset viewer](https://huggingface.co/datasets/Bo1015/solubility_prediction/viewer) to explore more examples.

```
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':1}
```

The average  for the `seq` and the `label` are provided below:

| Feature    | Mean Count |
| ---------- | ---------------- |
| seq    |    298   |
| label (0)   |    0.58   |
| label (1)   |    0.42   |




### Data Fields

- `seq`: a string containing the protein sequence
- `label`: an integer label indicating that the protein sequence is soluble or insoluble.

### Data Splits

The solubility prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.

| Dataset Split | Number of Instances in Split                |
| ------------- | ------------------------------------------- |
| Train         | 62,478                         |
| Test          | 6,942                                   |

### Source Data

#### Initial Data Collection and Normalization

The initialized dataset is adapted from [DeepSol](https://academic.oup.com/bioinformatics/article/34/15/2605/4938490). Within this framework, any protein exhibiting a sequence identity of 30% or greater to any protein within the test subset is eliminated from both the training subsets, ensuring robust and unbiased evaluation.

### Licensing Information

The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 

### Citation
If you find our work useful, please consider citing the following paper:

```
@misc{chen2024xtrimopglm,
  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
  year={2024},
  eprint={2401.06199},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  note={arXiv preprint arXiv:2401.06199}
}
```