ChemDataExtractor TADF

ChemDataExtractor TADF is an adaption of ChemDataExtractor v2, which is a toolkit for extracting chemical information from the scientific literature. The adaption was made for text-mining the thermally-activated delayed fluorescence (TADF) domain.

Prerequisites

• git: Install git for downloading the package https://git-scm.com/book/en/v2/Getting-Started-Installing-Git.
• conda: Dependency manager used to create Python virtual environments https://conda.io/projects/conda/en/latest/user-guide/install/index.html.

Installation

Link to BiliBili video How to Install CDE from scratch here.

Download and go to the directory of the repository.

git clone https://github.com/Dingyun-Huang/chemdataextractorTADF.git
cd chemdataextractorTADF

Create and activate a new Python 3.7 environment.

conda create --name cde_tadf python=3.7
conda activate cde_tadf

When you are in the repository directory, install ChemDataExtractor and tadf_models.

pip install -e .
pip install -e tadf_models
cde data download

Run all tests to ensure correct installation.

python -m pytest -W ignore::DeprecationWarning

Usage

An example run can be found in example_extraction.ipynb, where you should put the documents (Elsevier XMLs and RSC HTMLs) in the papers_for_extraction folder. The notebook does not save the extracted records by default, but you can do so with json.dumps.

The helper functions for data cleaning and post-processing are given in data_cleaning_utils.ipynb. You can copy the functions that you would like to use into your scripts.

An example of converting IUPAC names into SMILES is given in iupac_to_smiles.ipynb. You need to change the path of the opsin_cli.jar file to the actual path in your computer.

Citing

If you use the database or chemdataextractorTADF in your work, please cite the following works:

Huang, D., Cole, J.M. A database of thermally activated delayed fluorescent molecules auto-generated from scientific literature with ChemDataExtractor. Sci Data 11, 80 (2024). https://doi.org/10.1038/s41597-023-02897-3

Huang, D. & Cole, J. M. A Database of Thermally Activated Delayed Fluorescent Molecules Auto-generated from Scientific Literature with ChemDataExtractor. (2023) https://doi.org/10.6084/m9.figshare.24004182.

Mavračić, J., Court, C. J., Isazawa, T., Elliott, S. R. & Cole, J. M. ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science. J. Chem. Inf. Model. 61, 4280–4289 (2021). https://doi.org/acs.jcim.1c00446

Funding

This project was financially supported by the Science and Technology Facilities Council (STFC) and the Royal Academy of Engineering (RCSRF1819\7\10). Dingyun Huang recieves his PhD scholarship from the Cambridge Commonwealth, European and International Trust and the China Scholarship Council for a PhD scholarship.