gbyuvd/coconut-chembl34-selfies-mlm

Dataset Card for COCONUT+ChemBL34 SELFIES for MLM training (unmasked)

This dataset is a collection of molecular structures represented as SELFIES (Self-Referencing Embedded Strings), created by combining and processing data from COCONUTDB and ChemBL34. It contains 2,700,462 unique molecules across 13 chunks.

The dataset is specifically designed for pre-training language models on molecular representations using the Masked Language Model (MLM) approach. It consists of a single column of unmasked SELFIES strings, serving as raw data for generating masked examples during the training process.

Key features:

• Source: Combined data from COCONUTDB (natural compounds) and ChemBL34 (bioactive compounds)
• Format: Single column of SELFIES representations
• Size: 2,700,462 unique molecules. (It will generate 7,291,197 training examples if using the method used to train this model)
• Purpose: Pre-training molecular language models using MLM
• Unique aspect: Unmasked dataset for flexible masking strategies during training

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

Uses

Direct Use

This dataset was specifically created and used as raw training data to generate masked examples for pre-training a base model for SELFIES (Self-Referencing Embedded Strings) representations. Its primary and intended use is:

1. Pre-training language models for molecular representation learning, specifically using the Masked Language Model (MLM) approach with SELFIES. It was used to train this model.

The dataset contains only one column of SELFIES representations of molecules, without any additional properties or labels.

Indirect Use

While the dataset's primary purpose is for MLM training, it could potentially be used for:

1. Analyzing the distribution and characteristics of SELFIES representations in a large molecular dataset.
2. Generating new molecular structures using trained models (though this would require careful validation).
3. Serving as a base dataset for other researchers to develop and test SELFIES-based molecular representation learning techniques.

Out-of-Scope Use

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

Dataset Creation

Source Data

The dataset combines two sources of molecular data:

1. Natural compounds from COCONUTDB (Sorokina et al., 2021)
2. Bioactive compounds from ChemBL34 (Zdrazil et al., 2023)

Data Collection and Processing

1. Fetching: Canonical SMILES were extracted from both databases.
2. De-duplication:
   • Each dataset was de-duplicated internally.
   • The combined dataset ("All") was further de-duplicated to ensure unique entries.
3. Validity Check and Conversion: A dual validity check was performed using RDKit and by converting them into SELFIES

Filtering and Chunking

• Filtering by Lipinski's Rule of Five or its subsets (e.g., Mw < 500 and LogP < 5) was omitted to maintain broader coverage for potential future expansion to organic and inorganic molecules such in PubCHEM and ZINC20.
• The dataset was chunked into 13 parts, each containing 203,458 molecules, to accommodate the 6-hour time limit on Paperspace's Gradient.
• Any leftover data was randomly distributed across the 13 chunks to ensure even distribution.

Validation Set

• 10% of each chunk was set aside for validation.
• These validation sets were combined into a main test set

Dataset	Number of Valid Unique Molecules
Chunk I	207,727
Chunk II	207,727
Chunk III	207,727
Chunk IV	207,727
Chunk V	207,727
Chunk VI	207,727
Chunk VII	207,727
Chunk VIII	207,727
Chunk IX	207,727
Chunk X	207,727
Chunk XII	207,727
Chunk XI	207,727
Chunk XIII	207,738
Total	2,700,462

Who are the source data producers?

The source data producers are the contributors to COCONUTDB and ChemBL34 databases.

Bias, Risks, and Limitations

• The dataset may be biased towards certain types of molecules based on the focus of COCONUTDB and ChemBL34.
• The dataset does not include filtering by Lipinski's Rule of Five, which may include molecules that are not typically considered drug-like.
• The chunking process may introduce some artifacts in the data distribution.

Recommendations

Users should be aware of the potential biases in the dataset and consider the implications for their specific use case. It's recommended to evaluate model performance on external datasets to ensure generalizability.

Citation

If you use this dataset, please cite the following papers:

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}
}

COCONUTDB:

@article{sorokina2021coconut,
  title={COCONUT online: Collection of Open Natural Products database},
  author={Sorokina, Maria and Merseburger, Peter and Rajan, Kohulan and Yirik, Mehmet Aziz and Steinbeck, Christoph},
  journal={Journal of Cheminformatics},
  volume={13},
  number={1},
  pages={2},
  year={2021},
  doi={10.1186/s13321-020-00478-9}
}

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 ([email protected])