Hyformer

Model Details

• Paper: Synergistic Benefits of Joint Molecule Generation and Property Prediction
• Authors: Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek
• License: BSD 3-Clause
• Repository: https://github.com/szczurek-lab/hyformer

Model checkpoints

• Hyformer_molecules_8M: Trained on GuacaMol dataset (Brown et al., 2019)
• Hyformer_molecules_50M: Trained on 19M molecules from ZINC, ChEMBL, and other purchasable molecular datasets (Zhou et al., 2023)
• Hyformer_peptides_34M: Trained on 3.5M general-purpose and antimicrobial peptides
• Hyformer_peptides_34M_MIC: Hyformer_peptides_34M jointly fine-tuned on minimal inhibitory concentration values (MIC) against E. coli bacteria

Gated Access

Citation

@misc{izdebski2025synergisticbenefitsjointmolecule,
      title={Synergistic Benefits of Joint Molecule Generation and Property Prediction}, 
      author={Adam Izdebski and Jan Olszewski and Pankhil Gawade and Krzysztof Koras and Serra Korkmaz and Valentin Rauscher and Jakub M. Tomczak and Ewa Szczurek},
      year={2025},
      eprint={2504.16559},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.16559}, 
}

References

• Brown, Nathan, et al. "GuacaMol: benchmarking models for de novo molecular design." Journal of chemical information and modeling, 2019.
• Zhou, Gengmo, et al. "Uni-mol: A universal 3d molecular representation learning framework." ICLR, 2023.