DeepBioisostere

Model weights and fragment library for DeepBioisostere, a deep generative model that performs end-to-end bioisosteric replacement: it selects which fragment of a molecule to replace, what to replace it with, and how to attach it, so as to satisfy several property targets at once.

• Paper: Nature Communications (2026)
• Code: https://github.com/Hwoo-Kim/DeepBioisostere
• Project page: https://hwoo-kim.github.io/DeepBioisostere/

Usage

These files are fetched automatically; you do not normally download them by hand.

pip install deepbioisostere
deepbioisostere generate -s "Cc1ccc2cnc(N(C)CCc3ccccn3)nc2c1" -t mw=0 -t logp=-1

from deepbioisostere import Conditioner, DeepBioisostere, Generator

properties = ["logp", "mw"]
model = DeepBioisostere.from_pretrained(properties=properties)
generator = Generator(
    model=model,
    conditioner=Conditioner(phase="generation", properties=properties),
    properties=properties,
)
df = generator.generate([("Cc1ccc2cnc(N(C)CCc3ccccn3)nc2c1", {"mw": 0, "logp": -1})])

Contents

Checkpoints

Each checkpoint is conditioned on a specific set of properties. The filename encodes the property set, sorted alphabetically.

File	Properties	Variant
DeepBioisostere_logp.pt	logP	standard
DeepBioisostere_mw.pt	MW	standard
DeepBioisostere_qed.pt	QED	standard
DeepBioisostere_sa.pt	SA	standard
DeepBioisostere_logp_mw.pt	logP + MW	standard
DeepBioisostere_mw_qed.pt	MW + QED	standard
DeepBioisostere_qed_sa.pt	QED + SA	standard
DeepBioisostere_logp_mw_ablation.pt	logP + MW	ablation
DeepBioisostere_mw_qed_ablation.pt	MW + QED	ablation
DeepBioisostere_qed_sa_ablation.pt	QED + SA	ablation

The remaining pairs (logP+QED, logP+SA, MW+SA) were not trained. Ablation variants set use_subgraph_AMPN=False, which changes the architecture, and are selected with from_pretrained(..., ablation=True).

Fragment library

File	Description
fragment_library.csv	145,854 insertion fragments with frequency, BRICS type and train/val/test split
frag_features.pkl	Pre-parsed graph features, keyed by the csv SMILES
frag_brics_maskings.pkl	BRICS-type compatibility masks (training only)

The two .pkl files are derived from the csv and are regenerated automatically if absent, which takes roughly an hour of CPU. They are published so that a first run is a download instead.

Note on fragment identity. The features are keyed on the exact SMILES string in fragment_library.csv, not on a re-canonicalised form. RDKit's canonical SMILES writer changed after the library was built under 2022.03: under 2026.03, 1,712 of the fragments are written differently (stereocentre descriptors flip). The underlying molecules and their parsed features are unchanged — this is a serialisation difference only — but the csv string is the authoritative key, since training data references fragments by row index.

Citation

@article{kim2026deepbioisostere,
  title   = {Autonomous bioisosteric replacement for multi-property optimization in drug design},
  author  = {Kim, Hyeongwoo and Moon, Seokhyun and Zhung, Wonho and
             Kim, Shinwoo and Lim, Jaechang and Kim, Woo Youn},
  journal = {Nature Communications},
  year    = {2026},
  doi     = {10.1038/s41467-026-75512-9},
}

License: MIT.