| --- |
| 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. |
|
|
