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mseok/
DeepBioisostere

Name: DeepBioisostere
Availability: InStock

deepbioisostere

chemistry

drug-discovery

molecular-optimization

bioisostere

graph-neural-network

mit

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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.

bash

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

python

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})])

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

bibtex

@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.

mseok/DeepBioisostere