HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)

Piotr Gainski, Lukasz Maziarka, Tomasz Danel, Stanislaw Jastrzebski

AAAI 2022 pp. 12949-12950

doi:10.1609/AAAI.V36I11.21611 /aaai/2022/gainski2022aaai-huggingmolecules/

Abstract

Large-scale transformer-based methods are gaining popularity as a tool for predicting the properties of chemical compounds, which is of central importance to the drug discovery process. To accelerate their development and dissemination among the community, we are releasing HuggingMolecules -- an open-source library, with a simple and unified API, that provides the implementation of several state-of-the-art transformers for molecular property prediction. In addition, we add a comparison of these methods on several regression and classification datasets. HuggingMolecules package is available at: github.com/gmum/huggingmolecules.

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Cite

Text

Gainski et al. "HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21611

Markdown

[Gainski et al. "HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/gainski2022aaai-huggingmolecules/) doi:10.1609/AAAI.V36I11.21611

BibTeX

@inproceedings{gainski2022aaai-huggingmolecules,
  title     = {{HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract)}},
  author    = {Gainski, Piotr and Maziarka, Lukasz and Danel, Tomasz and Jastrzebski, Stanislaw},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12949-12950},
  doi       = {10.1609/AAAI.V36I11.21611},
  url       = {https://mlanthology.org/aaai/2022/gainski2022aaai-huggingmolecules/}
}