You can deploy this model and start earning money today!

Kiria-Nozan/
ApexOracle

Name: ApexOracle
Rating: 2.5 (127 reviews)

safetensors

mol_emb_raw

model_hub_mixin

pytorch_model_hub_mixin

2507.07862

Views

No views yet

ApexOracle molecule embedding DLM

This repository publishes the frozen molecule encoder used by ApexOracle for downstream embedding extraction. It is not the DLM pretraining repository and does not contain the guided molecule-generation pipeline.

The release contains a 12-block, 768-hidden-size diffusion transformer and the SELFIES tokenizer files needed to reproduce its token-level hidden states. The returned tensor includes tokenizer special-token positions. Use attention_mask when pooling or selecting valid positions.

Installation

The runtime requires a CUDA GPU and FlashAttention:

bash

pip install -r requirements.txt
git clone https://huggingface.co/Kiria-Nozan/ApexOracle
cd ApexOracle
python example.py

Direct use

python

import torch
from transformers import AutoTokenizer

from DLM_emb_model import MolEmbDLM

model_dir = "Kiria-Nozan/ApexOracle"
device = torch.device("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = MolEmbDLM.from_pretrained(model_dir).eval().to(device)

batch = tokenizer(
    ["[C] [C] [O]", "[C] [=C] [C] [=C] [C] [=C] [Ring1] [=Branch1]"],
    padding=True,
    truncation=False,
    return_tensors="pt",
).to(device)

with torch.no_grad():
    hidden_states = model(**batch)

print(hidden_states.shape)  # [batch, padded_sequence_length, 768]

attention_mask may be the ordinary integer mask returned by Transformers; the wrapper validates and converts it to the boolean mask required by the non-padding FlashAttention backbone. A complete tokenizer batch, including token_type_ids, can be passed directly with model(**batch).

Scope and provenance

Frozen model artifact SHA-256: b472f7508aaf0fdab4c935caf221415b48a5f8afd4d104a731c9d72d410c2c44
Tokenizer: ibm-research/materials.selfies-ted, audited revision 55e83392264cb998f7aa5014847df29868aefeb8
Canonical source module: DragonDescentZerotsu/ApexOracle-MDLM
ApexOracle umbrella repository: DragonDescentZerotsu/ApexOracle

The ApexOracle wrapper and frozen weights are released under the MIT License. The attributed MDLM runtime and IBM tokenizer assets retain their Apache-2.0 terms; see THIRD_PARTY_NOTICES.md and LICENSES/Apache-2.0.txt.

Citation

bibtex

@article{leng2025predicting,
  title={Predicting and generating antibiotics against future pathogens with ApexOracle},
  author={Leng, Tianang and Wan, Fangping and Torres, Marcelo Der Torossian and de la Fuente-Nunez, Cesar},
  journal={arXiv preprint arXiv:2507.07862},
  year={2025}
}