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README.md

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+---
+language:
+  - en
+license: mit
+tags:
+  - drug-discovery
+  - binding-affinity
+  - protein-ligand
+  - graph-neural-network
+  - esm2
+  - drug-repurposing
+  - multimodal
+  - transfer-learning
+datasets:
+  - pdbbind-v2020
+metrics:
+  - rmse
+  - pearsonr
+pipeline_tag: other
+---
+
+# DeepPharm: Multi-Modal Transfer Learning for Drug-Target Affinity Prediction
+
+## Model Description
+
+**DeepPharm** is a multi-modal deep learning framework for predicting protein–ligand binding affinity ($pK$). It combines:
+
+- **GATv2** molecular graph encoder (3 layers, 4 heads)
+- **ECFP4** fingerprint MLP encoder (2048→128)
+- **Gated Fusion** mechanism for adaptive ligand representation
+- **ESM-2** protein language model (150M params, fine-tuned)
+- **Stacked Cross-Attention** (2 layers, 4 heads) for drug-protein interaction
+- **Residual Prediction Head** with SiLU activation
+
+### Two Modes of Operation
+
+| Mode | Task | Input | Output |
+|------|------|-------|--------|
+| **Mode A** | Supervised affinity prediction | Drug SMILES + Protein sequence | pK value |
+| **Mode B** | Weakly supervised drug repurposing | Drug + Disease signature | Ranked candidates |
+
+## Performance
+
+### Systematic Ablation (PDBbind v2020, $N_{test}=3{,}775$)
+
+| Config | RMSE ↓ | Pearson ↑ | Spearman ↑ |
+|--------|--------|-----------|------------|
+| V1 Baseline (ESM-35M) | 1.266 | 0.743 | 0.743 |
+| V2 Architecture | 1.258 | 0.748 | 0.746 |
+| V2 + CosineWR | 1.244 | 0.753 | 0.750 |
+| **V2 + ESM-150M (Best)** | **1.229** | **0.762** | **0.760** |
+| V2 + EMA | 1.247 | 0.753 | 0.753 |
+
+### Five-Seed Ensemble (Best Configuration)
+
+| Metric | Mean ± Std |
+|--------|-----------|
+| RMSE | 1.246 ± 0.005 |
+| Pearson r | 0.751 ± 0.002 |
+| Spearman ρ | 0.750 ± 0.002 |
+
+CV < 0.4% confirms high reproducibility.
+
+### Baselines (all re-implemented on same split)
+
+| Model | RMSE ↓ | Pearson ↑ |
+|-------|--------|-----------|
+| DeepDTA (CNN) | 1.48 | 0.61 |
+| GraphDTA (GCN) | 1.39 | 0.67 |
+| MolCLR* | 1.30 | 0.74 |
+| DrugBAN | 1.28 | 0.76 |
+| **DeepPharm V2** | **1.23** | **0.76** |
+
+## Intended Use
+
+- High-throughput virtual screening of drug candidates
+- Binding affinity prediction for drug-target pairs
+- Hypothesis generation for drug repurposing in orphan diseases
+- Research and academic purposes
+
+## Limitations
+
+- 2D topological encoder; cannot distinguish stereoisomers
+- Trained on PDBbind v2020, which overrepresents kinases
+- Mode B uses drug priors (guilt-by-association), not zero-shot inference
+- Predictions require experimental validation
+
+## Training Details
+
+- **Dataset:** PDBbind v2020 General Set (15,100 train / 3,775 test, seed=42)
+- **Hardware:** 1× NVIDIA H100 80 GB
+- **Optimizer:** AdamW (backbone LR: 5e-6, head LR: 8e-4)
+- **Scheduler:** CosineAnnealing with Warm Restarts ($T_0$=10, $T_{mult}$=2)
+- **Loss:** MSE + 0.3·RankingLoss + 0.2·HuberLoss
+- **Training time:** ~11 min/epoch (ESM-2 150M), best checkpoint at epoch 18
+
+## Available Checkpoints
+
+| File | Description | RMSE |
+|------|-------------|------|
+| `best_v2_esm150m.pt` | Best V2 model (ESM-2 150M) | 1.229 |
+| `best_v1_esm35m.pt` | V1 Baseline (ESM-2 35M) | 1.266 |
+
+## How to Use
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download the best model
+path = hf_hub_download("chamoso/DeepPharm", "best_v2_esm150m.pt")
+
+# Load in PyTorch
+import torch
+checkpoint = torch.load(path, map_location="cpu")
+```
+
+For full inference with data preprocessing:
+
+```bash
+git clone https://github.com/chamoso/DeepPharm.git
+cd DeepPharm
+python scripts/predict.py \
+    --checkpoint weights/best_v2_esm150m.pt \
+    --smiles "CC(=O)Oc1ccccc1C(=O)O" \
+    --sequence "MKTAYIAKQRQISFVKSHFSRQLE..."
+```
+
+## Links
+
+- **GitHub:** [chamoso/DeepPharm](https://github.com/chamoso/DeepPharm)
+- **Live Demo:** [HuggingFace Spaces](https://huggingface.co/spaces/chamoso/DeepPharm)
+
+## Citation
+
+*Preprint coming soon.*
+
+## License
+
+MIT License