Update model card

README.md

@@ -5,61 +5,32 @@ tags:
   - protein-ligand
   - drug-target-interaction
   - contrastive-learning
-  - esm2
-  - selformer
-  - logica
 ---
 
-# LogiCA — pretrained protein–ligand backbones
+# LogiCA — pretrained protein-ligand backbones (anonymous, under review)
 
-Pretrained checkpoints for **LogiCA** (*Contextualizing Biological Language Models
-across Modalities via Logit-Space Contrastive Alignment*). Each checkpoint is a
-bidirectional ESM-2 (protein) + SELFormer (ligand) model with a cross-attention
-adapter, pretrained on BindingDB with the logit-space contrastive objective
-(`--mode logicl`, 40-epoch `logicl40_clean` track).
+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).
 
-Code: **https://github.com/Yale-CompBio/logica**
+| 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 |
 
-| Size | Protein backbone | File | Weights | Epoch | Val loss |
-| --- | --- | --- | --- | --- | --- |
-| 8M | `esm2_t6_8M_UR50D` | `checkpoints/8m/best.pt` | 19 MB | 100 | 1.8036 |
-| 35M | `esm2_t12_35M_UR50D` | `checkpoints/35m/best.pt` | 38 MB | 99 | 1.6101 |
-| 150M | `esm2_t30_150M_UR50D` | `checkpoints/150m/best.pt` | 64 MB | 98 | 1.3963 |
-| 650M | `esm2_t33_650M_UR50D` | `checkpoints/650m/best.pt` | 97 MB | 92 | 1.0994 |
+Ligand encoder: `HUBioDataLab/SELFormer` (all sizes).
 
-Ligand encoder: `HUBioDataLab/SELFormer` for all sizes.
-
-## Contents
-
-Checkpoints are **weights-only** (`trainable_only=True`): a dict with
-`model_state_dict` (+ `epoch`, `metrics`, `rng_state`). They load into the
-`BiDirectionalDrugProteinModel` with `strict=False`.
-
-## Usage
+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("Yale-CompBio/logica", "checkpoints/150m/best.pt")
+ckpt = hf_hub_download(REPO_ID, "checkpoints/150m/best.pt")  # REPO_ID withheld for review
 ```
 
-Then fine-tune with the released code (the entry points take `--pretrained_checkpoint`):
-
-```bash
-python dti.py --method logica --dataset DAVIS \
-  --esm_model esm2_t30_150M_UR50D --pretrained_checkpoint $ckpt --output_dir runs/dti_davis
-python variant.py --objective pairwise --held_out EGFR --split_strategy lopo \
-  --esm_model esm2_t30_150M_UR50D --pretrained_checkpoint $ckpt \
-  --data_csv splits/data.csv --fasta_path data/proteins.fasta --drugs_selfies data/drugs.selfies \
-  --use_lora --output_dir runs/variant_egfr
-```
-
-## Citation
-
-```
-@inproceedings{logica2026,
-  title  = {Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment},
-  author = {anonymous},
-  booktitle = {NeurIPS},
-  year   = {2026}
-}
-```
+Training and fine-tuning code is provided as an anonymized supplement with the
+submission. Repository, authors, and citation are withheld for double-blind review.