Dataset Card for ChemBL34-HS221-DrugTarget

This dataset contains ~221K bioactive compounds represented in SELFIES format, labeled with 221 human protein target IDs from ChEMBL34.

The dataset is designed for fine-tuning a classifier based on the pretrained ChemFIE base model to predict potential drug targets for 221 human proteins using chemical structures encoded as SELFIES (Self-Referencing Embedded Strings).

Key features:

• Source: bioactive compounds from ChemBL34 (Zdrazil et al., 2023) using its PostgreSQL dump
• Format: label, SELFIES
• Size: 221,000
• Purpose: fine-tuning a classifier for predicting potential drug targets using chemical structures represented as SELFIES
• Labels: ChemBL34's Target IDs (list of classes available in the "label_dict.json")
• Number of Labels: 221 classes

Disclaimer: For Academic Purposes Only

The information and dataset provided is for academic purposes only. It is intended for educational and research use, and should not be used for any commercial or legal purposes. The author do not guarantee the accuracy, completeness, or reliability of the information.

Dataset Details

Out-of-Scope Use

Any purposes that could lead to the creation or promotion of harmful substances.

Dataset

Source Data

Bioactive compounds from ChemBL34 (Zdrazil et al., 2023) using its PostgreSQL dump

Who are the source data producers?

The source data producers are the contributors to ChemBL34 database.

Data Collection and Processing

Dataset Details:

• Total training examples: 154,700
• Number of classes: 221 distinct human protein drug targets
• Organism: Homo sapiens
• Number of train examples for each class: 700
• Number of validation examples for each class: 100
• Number of held out test examples for each class: 200

Data Selection Criteria:

• Activity types: IC50, Ki, EC50
• Activity threshold: ≤ 10 µM (10,000 nM)
• Assay confidence score: ≥ 7
• Standard relation: Exact matches only ('=')
• SMILES: Canonical representations only

Data Preprocessing

• Conversion from Canonical SMILES into SELFIES readable by base model's tokenizer, any failed entries are removed
• Balancing the data distribution by setting up min max (1000)

Bias, Risks, and Limitations

• The dataset may be biased towards sampled types of molecules based on the focus of ChemBL34 and data selection criteria.
• The sampling methods may lead to certain bias and imbalance, the data distribution should be examined before use.

Recommendations

Users should be aware of the potential biases in the dataset and consider the implications for their specific use case.

Citation

If you find this project useful in your research and wish to cite it, please use the following BibTex entry:

ChemFIE Project:

@software{chemfie_basebertmlm,
  author = {GP Bayu},
  title = {{ChemFIE Base}: Pretraining A Lightweight BERT-like model on Molecular SELFIES},
  url = {https://huggingface.co/gbyuvd/chemselfies-base-bertmlm},
  version = {1.0},
  year = {2024},
}

ChEMBL34:

@misc{chembl34,
  title={ChemBL34},
  year={2023},
  doi={10.6019/CHEMBL.database.34}
}

@article{zdrazil2023chembl,
  title={The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods},
  author={Zdrazil, Barbara and Felix, Eloy and Hunter, Fiona and Manners, Emma J and Blackshaw, James and Corbett, Sybilla and de Veij, Marleen and Ioannidis, Harris and Lopez, David Mendez and Mosquera, Juan F and Magarinos, Maria Paula and Bosc, Nicolas and Arcila, Ricardo and Kizil{\"o}ren, Tevfik and Gaulton, Anna and Bento, A Patr{\'i}cia and Adasme, Melissa F and Monecke, Peter and Landrum, Gregory A and Leach, Andrew R},
  journal={Nucleic Acids Research},
  year={2023},
  volume={gkad1004},
  doi={10.1093/nar/gkad1004}
}

For the SELFIES representation used in data processing:

@article{krenn2020selfies,
  title={Self-referencing embedded strings (SELFIES): A 100\% robust molecular string representation},
  author={Krenn, Mario and H{\"a}se, Florian and Nigam, AkshatKumar and Friederich, Pascal and Aspuru-Guzik, Alan},
  journal={Machine Learning: Science and Technology},
  volume={1},
  number={4},
  pages={045024},
  year={2020},
  doi={10.1088/2632-2153/aba947}
}

Dataset Card Contact

G Bayu (gbyuvd@proton.me)

This project has been quiet a journey for me, I’ve dedicated hours on this and I would like to improve myself, this data, and future projects. However, financial and computational constraints can be challenging.

If you find my work valuable and would like to support my journey, please consider supporting me here. Your support will help me cover costs for computational resources, data acquisition, and further development of this project. Any amount, big or small, is greatly appreciated and will enable me to continue learning and explore more.

Thank you for checking out this project, I am more than happy to receive any feedback, so that I can improve myself and the future projects I will be working on.