metadata

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 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. 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.

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}
}