chenchaozhao/solubility_prediction

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:

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.

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}
}