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license: mit
library_name: pytorch
tags:
- protein-ligand
- drug-target-interaction
- contrastive-learning
---
# LogiCA — pretrained protein-ligand backbones (anonymous, under review)
Anonymous artifact for double-blind review. Pretrained checkpoints for a
logit-space contrastive alignment model: a bidirectional ESM-2 (protein) +
SELFormer (ligand) backbone with a cross-attention adapter, pretrained on
BindingDB with a token-likelihood contrastive objective (40 epochs).
| Size | Protein backbone | File | Weights |
| --- | --- | --- | --- |
| 8M | `esm2_t6_8M_UR50D` | `checkpoints/8m/best.pt` | 19 MB |
| 35M | `esm2_t12_35M_UR50D` | `checkpoints/35m/best.pt` | 38 MB |
| 150M | `esm2_t30_150M_UR50D` | `checkpoints/150m/best.pt` | 64 MB |
| 650M | `esm2_t33_650M_UR50D` | `checkpoints/650m/best.pt` | 97 MB |
Ligand encoder: `HUBioDataLab/SELFormer` (all sizes).
Checkpoints are weights-only (`model_state_dict` + `epoch` + `metrics`); load with
`strict=False` into the bidirectional model. Download the size you need and pass it
to the training code as the pretrained checkpoint:
```python
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(REPO_ID, "checkpoints/150m/best.pt") # REPO_ID withheld for review
```
Training and fine-tuning code is provided as an anonymized supplement with the
submission. Repository, authors, and citation are withheld for double-blind review.
|