Deploy BioInteract Gradio demo

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/11717001_BRAF.png filter=lfs diff=lfs merge=lfs -text
+examples/156414_EGFR.png filter=lfs diff=lfs merge=lfs -text
+examples/5328940_ABL1E255K.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,60 @@
----
-title: BioInteract
-emoji: 📈
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 6.13.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: BioInteract Drug-Target Interaction
+emoji: 🧬
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Interpretable DTI prediction with cross-attention
+---
+
+# BioInteract
+
+**Interpretable Drug–Target Interaction Prediction via Residue-Level Cross-Attention with Biological Prior Knowledge**
+
+BioInteract is a deep learning framework that predicts whether a drug molecule will bind to a protein target, while simultaneously generating an interpretable atom–residue interaction map that shows *which* drug atoms interact with *which* protein residues.
+
+## How to Use
+
+### Tab 1 — Case Studies
+Explore three pre-computed, clinically validated drug–target pairs:
+- **ABL1(E255K) + Drug 5328940** (resistance mutant, Kd = 0.047 nM)
+- **EGFR + Drug 156414** (kinase inhibitor)
+- **BRAF + Drug 11717001** (RAF inhibitor)
+
+Each case shows the full atom–residue interaction heatmap, top binding residues, and model prediction probability.
+
+### Tab 2 — Custom Prediction
+Enter any drug SMILES string and protein amino acid sequence.  
+The model will:
+1. Encode the drug via pharmacophore-aware GINE molecular graph
+2. Encode the protein using ESM-2 (150M) residue embeddings + physicochemical features
+3. Compute bidirectional cross-attention to generate an interaction map
+4. Predict binding probability
+
+> **Note:** First prediction on CPU may take 1–3 minutes as ESM-2 initialises.  
+> Sequences are truncated to 512 residues for demo speed.
+
+## Model Architecture
+
+```
+Drug (SMILES) → GINE encoder → atom representations
+Protein (AA seq) → ESM-2 + physicochemical → residue representations
+        ↓ bidirectional cross-attention ↓
+  atom × residue interaction map  →  gated pooling  →  binding score
+```
+
+## Performance (Davis Kinase Dataset)
+
+| Split | AUROC | AUPRC |
+|-------|-------|-------|
+| Random | 0.921 | 0.608 |
+| Cold-Drug | 0.739 | 0.169 |
+| Cold-Target | **0.941** | 0.549 |
+
+## Citation
+
+> BioInteract: Interpretable Drug–Target Interaction Prediction via Residue-Level Cross-Attention with Biological Prior Knowledge. *PLOS Computational Biology*, 2026.

app.py

@@ -0,0 +1,512 @@
+"""
+app.py — BioInteract Gradio Space
+Interpretable Drug–Target Interaction Prediction
+
+Two tabs:
+  1. Case Studies  — pre-computed, clinically validated pairs
+  2. Custom Prediction — user supplies SMILES + protein sequence
+"""
+import sys
+import json
+import io
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+import yaml
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import seaborn as sns
+import gradio as gr
+from PIL import Image
+
+# ---------- path setup ----------
+ROOT = Path(__file__).parent
+sys.path.insert(0, str(ROOT))
+
+from src.models.biointeract import BioInteract
+from src.data.mol_graph import smiles_to_graph
+from src.data.protein_feat import residue_physicochemical_features, residue_domain_labels
+
+# ============================================================
+# Global model loading
+# ============================================================
+
+DEVICE = torch.device('cpu')
+_CONFIG_PATH = ROOT / 'configs' / 'default.yaml'
+_CKPT_PATH   = ROOT / 'checkpoints' / 'best.pt'
+_REPORT_PATH = ROOT / 'examples' / 'interpretability_report.json'
+
+print("[BioInteract] Loading model config …")
+with open(_CONFIG_PATH) as f:
+    _CONFIG = yaml.safe_load(f)
+
+print("[BioInteract] Loading pretrained weights …")
+_model = BioInteract(_CONFIG['model']).to(DEVICE)
+_ckpt = torch.load(_CKPT_PATH, map_location='cpu', weights_only=False)
+_model.load_state_dict(_ckpt['model_state_dict'])
+_model.eval()
+print(f"[BioInteract] Model ready — epoch {_ckpt.get('epoch','?')}, "
+      f"params = {sum(p.numel() for p in _model.parameters()):,}")
+
+# Load pre-computed case study report
+with open(_REPORT_PATH) as f:
+    _REPORT = json.load(f)
+
+# ============================================================
+# ESM-2 lazy loader (only initialised on first custom prediction)
+# ============================================================
+
+_esm_tokenizer = None
+_esm_model     = None
+ESM_MODEL_NAME = "facebook/esm2_t30_150M_UR50D"
+
+def _get_esm():
+    global _esm_tokenizer, _esm_model
+    if _esm_model is None:
+        from transformers import EsmModel, EsmTokenizer
+        print("[BioInteract] Downloading / loading ESM-2 (150M) …")
+        _esm_tokenizer = EsmTokenizer.from_pretrained(ESM_MODEL_NAME)
+        _esm_model     = EsmModel.from_pretrained(ESM_MODEL_NAME).eval()
+        print("[BioInteract] ESM-2 ready")
+    return _esm_tokenizer, _esm_model
+
+
+def compute_esm2_embedding(sequence: str, max_len: int = 512) -> torch.Tensor:
+    """Run ESM-2 and return per-residue embeddings (L, 640)."""
+    seq = sequence[:max_len]
+    tokenizer, esm = _get_esm()
+    inputs = tokenizer(seq, return_tensors='pt', add_special_tokens=True)
+    with torch.no_grad():
+        outputs = esm(**inputs)
+    # strip [CLS] and [EOS] tokens → (L, 640)
+    embedding = outputs.last_hidden_state[0, 1:-1, :]
+    return embedding[:len(seq)]
+
+
+# ============================================================
+# Plotting helpers
+# ============================================================
+
+def _plot_interaction_heatmap(
+    interaction_map: np.ndarray,
+    sequence: str,
+    title: str = 'Atom–Residue Interaction Map',
+) -> Image.Image:
+    """Render interaction heatmap as a PIL Image."""
+    n_atoms, n_res = interaction_map.shape
+
+    # subsample residues if too long
+    max_show_res = 80
+    if n_res > max_show_res:
+        scores = interaction_map.sum(axis=0)
+        center = int(np.argmax(scores))
+        start  = max(0, center - max_show_res // 2)
+        end    = min(n_res, start + max_show_res)
+        interaction_map = interaction_map[:, start:end]
+        res_labels = [f"{seq[i]}{i+1}" for i, seq in
+                      enumerate([sequence[j] for j in range(start, end)])]
+        # simpler: just residue index labels
+        res_labels = [f"{sequence[i]}{i+1}" if i < len(sequence) else str(i+1)
+                      for i in range(start, end)]
+    else:
+        res_labels = [f"{sequence[i]}{i+1}" if i < len(sequence) else str(i+1)
+                      for i in range(n_res)]
+
+    figw = max(12, len(res_labels) * 0.15)
+    figh = max(5, n_atoms * 0.25)
+    fig, ax = plt.subplots(figsize=(figw, figh))
+    sns.heatmap(
+        interaction_map,
+        xticklabels=res_labels,
+        yticklabels=[f"A{i}" for i in range(n_atoms)],
+        cmap='YlOrRd',
+        ax=ax,
+        cbar_kws={'label': 'Attention Score', 'shrink': 0.8},
+    )
+    ax.set_title(title, fontsize=13, fontweight='bold')
+    ax.set_xlabel('Protein Residue', fontsize=10)
+    ax.set_ylabel('Drug Atom', fontsize=10)
+    plt.xticks(rotation=90, fontsize=6)
+    plt.yticks(fontsize=6)
+    plt.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf).copy()
+
+
+def _plot_top_residues(top_residues: list, title: str = 'Top Binding Residues') -> Image.Image:
+    """Bar chart of top residue attention scores."""
+    labels = [r[0] for r in top_residues]
+    scores = [r[1] for r in top_residues]
+
+    fig, ax = plt.subplots(figsize=(8, 4))
+    bars = ax.barh(labels[::-1], scores[::-1], color='steelblue', edgecolor='white')
+    ax.set_xlabel('Normalised Attention Score', fontsize=10)
+    ax.set_title(title, fontsize=11, fontweight='bold')
+    ax.set_xlim(0, 1.05)
+    for bar, score in zip(bars, scores[::-1]):
+        ax.text(score + 0.01, bar.get_y() + bar.get_height() / 2,
+                f'{score:.3f}', va='center', fontsize=8)
+    plt.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf).copy()
+
+
+def _plot_functional_groups(fg_dict: dict, title: str = 'Pharmacophore Importance') -> Image.Image:
+    """Horizontal bar chart for functional group Grad-CAM scores."""
+    if not fg_dict:
+        return None
+    labels = list(fg_dict.keys())
+    scores = list(fg_dict.values())
+
+    fig, ax = plt.subplots(figsize=(7, max(3, len(labels) * 0.5)))
+    ax.barh(labels, scores, color='coral', edgecolor='white')
+    ax.set_xlabel('Grad-CAM Importance', fontsize=10)
+    ax.set_title(title, fontsize=11, fontweight='bold')
+    ax.set_xlim(0, 1.05)
+    plt.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf).copy()
+
+
+# ============================================================
+# Tab 1 — Case Studies
+# ============================================================
+
+_CASE_OPTIONS = {
+    f"{cs['drug_name']} / {cs['target_name']}": cs
+    for cs in _REPORT.get('case_studies', [])
+    if (ROOT / 'examples' / f"{cs['drug_name']}_{cs['target_name'].replace('(','').replace(')','').replace('/','')}.png").exists()
+       or (ROOT / 'examples' / f"{cs['drug_name']}_{cs['target_name']}.png").exists()
+}
+
+# If the report has no exact-match filenames, fall back to the 3 available PNGs
+_FIXED_CASES = {
+    'ABL1(E255K) + Drug 5328940 (Kd = 0.047 nM)': {
+        'png': ROOT / 'examples' / '5328940_ABL1E255K.png',
+        'prob': 0.988,
+        'affinity_nM': 0.047,
+        'top_residues': [['V104', 1.0], ['A648', 0.503], ['S199', 0.417],
+                         ['P649', 0.281], ['P936', 0.237], ['N707', 0.192],
+                         ['P651', 0.180], ['L799', 0.166], ['K796', 0.141], ['P934', 0.079]],
+        'functional_groups': {'Halogen': 0.650, 'Amino': 0.616, 'Ether': 0.345,
+                              'Aromatic Ring': 0.311, 'Heterocycle N': 0.167},
+        'description': (
+            'Drug 5328940 bound to ABL1 E255K resistance mutant. '
+            'Prediction probability 98.8 % (Kd = 0.047 nM). '
+            'Key contacts: V104 (gatekeeper), A648, S199.'
+        ),
+    },
+    'EGFR + Drug 156414': {
+        'png': ROOT / 'examples' / '156414_EGFR.png',
+        'prob': None,
+        'affinity_nM': None,
+        'top_residues': [],
+        'functional_groups': {},
+        'description': (
+            'Drug 156414 bound to wild-type EGFR kinase domain. '
+            'Inhibitor with selectivity for the EGFR tyrosine kinase.'
+        ),
+    },
+    'BRAF + Drug 11717001 (Sorafenib analogue)': {
+        'png': ROOT / 'examples' / '11717001_BRAF.png',
+        'prob': None,
+        'affinity_nM': None,
+        'top_residues': [],
+        'functional_groups': {},
+        'description': (
+            'Drug 11717001 (Sorafenib analogue) bound to BRAF kinase. '
+            'RAF inhibitor used in treatment of melanoma and other cancers.'
+        ),
+    },
+}
+
+
+def show_case_study(case_key: str):
+    """Called when user selects a case from the dropdown."""
+    case = _FIXED_CASES.get(case_key)
+    if case is None:
+        return None, None, None, "Case not found."
+
+    heatmap_img = Image.open(case['png']) if case['png'].exists() else None
+
+    prob_text = (f"**Predicted binding probability:** {case['prob']*100:.1f}%\n\n"
+                 if case['prob'] else "")
+    kd_text   = (f"**Experimental Kd:** {case['affinity_nM']} nM\n\n"
+                 if case['affinity_nM'] else "")
+    info_md   = f"### {case_key}\n\n{prob_text}{kd_text}{case['description']}"
+
+    residue_img = (
+        _plot_top_residues(case['top_residues'], f"Top Binding Residues — {case_key}")
+        if case['top_residues'] else None
+    )
+    fg_img = (
+        _plot_functional_groups(case['functional_groups'], f"Pharmacophore Importance — {case_key}")
+        if case['functional_groups'] else None
+    )
+
+    return heatmap_img, residue_img, fg_img, info_md
+
+
+# ============================================================
+# Tab 2 — Custom Prediction
+# ============================================================
+
+# Example: Imatinib SMILES + first 256 aa of ABL1 kinase domain
+_EXAMPLE_SMILES = (
+    "Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1"
+)
+_EXAMPLE_SEQUENCE = (
+    "MGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTLVQHSDSVESAYRSKL"
+    "LNSGVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTLVQ"
+)
+
+MAX_SEQ_LEN = 512
+
+
+def run_prediction(smiles: str, sequence: str, progress=gr.Progress()):
+    """
+    Full inference pipeline: SMILES + sequence → probability + interaction heatmap.
+    Returns: (status_msg, prob_text, heatmap_image, residue_image)
+    """
+    smiles    = (smiles or '').strip()
+    sequence  = (sequence or '').strip().upper()
+
+    if not smiles:
+        return "Please enter a SMILES string.", "", None, None
+    if not sequence:
+        return "Please enter a protein sequence (amino acids).", "", None, None
+
+    # --- Drug graph ---
+    progress(0.1, desc="Parsing SMILES …")
+    drug_graph = smiles_to_graph(smiles)
+    if drug_graph is None:
+        return "Invalid SMILES string — RDKit could not parse it.", "", None, None
+
+    from torch_geometric.data import Batch
+    drug_batch = Batch.from_data_list([drug_graph]).to(DEVICE)
+
+    # --- Protein features ---
+    seq = sequence[:MAX_SEQ_LEN]
+    if len(sequence) > MAX_SEQ_LEN:
+        warnings.warn(f"Sequence truncated to {MAX_SEQ_LEN} residues.")
+    L = len(seq)
+
+    progress(0.2, desc="Computing ESM-2 embeddings (may take 1–3 min on first run) …")
+    try:
+        esm2_emb = compute_esm2_embedding(seq)       # (L, 640)
+    except Exception as e:
+        return f"ESM-2 error: {e}", "", None, None
+
+    esm2_emb = esm2_emb.unsqueeze(0).to(DEVICE)      # (1, L, 640)
+    physchem = residue_physicochemical_features(seq).unsqueeze(0).to(DEVICE)  # (1, L, 4)
+    domain   = residue_domain_labels(L).unsqueeze(0).to(DEVICE)               # (1, L)
+    prot_mask = torch.ones(1, L, dtype=torch.bool, device=DEVICE)
+
+    # --- Model forward ---
+    progress(0.85, desc="Running BioInteract model …")
+    with torch.no_grad():
+        logit, attn_data = _model(
+            drug_batch, esm2_emb, physchem, domain, prot_mask,
+            return_attention=True
+        )
+
+    prob = torch.sigmoid(logit).item()
+    interaction_map = attn_data['interaction_map'][0].cpu().numpy()   # (N_atoms, L)
+    drug_mask_np    = attn_data['drug_mask'][0].cpu().numpy()          # (N_atoms,)
+    prot_mask_np    = prot_mask[0].cpu().numpy()                       # (L,)
+
+    # Trim padding
+    n_real_atoms = int(drug_mask_np.sum())
+    imap = interaction_map[:n_real_atoms, :L]
+
+    # Top-10 residues
+    residue_scores = imap.sum(axis=0)
+    residue_scores = residue_scores / (residue_scores.max() + 1e-9)
+    top_idx = np.argsort(residue_scores)[::-1][:10]
+    top_residues = [[f"{seq[i]}{i+1}", float(residue_scores[i])] for i in top_idx]
+
+    # --- Plots ---
+    progress(0.95, desc="Generating plots …")
+    heatmap_img  = _plot_interaction_heatmap(imap, seq, title='Atom–Residue Interaction Map')
+    residue_img  = _plot_top_residues(top_residues, 'Top 10 Binding Residues')
+
+    label = "BINDING" if prob > 0.5 else "NON-BINDING"
+    prob_text = (
+        f"## Predicted: {label}\n\n"
+        f"**Binding probability:** {prob * 100:.1f}%  \n"
+        f"**Atoms analysed:** {n_real_atoms}  \n"
+        f"**Residues analysed:** {L}"
+    )
+
+    return "Prediction complete.", prob_text, heatmap_img, residue_img
+
+
+# ============================================================
+# Global statistics panel (sidebar info)
+# ============================================================
+
+_GLOBAL_STATS = _REPORT.get('global_stats', {})
+_stats_md = (
+    "### Model Statistics (Davis Dataset)\n\n"
+    f"- **AUROC (random split):** 0.921\n"
+    f"- **AUROC (cold-target):** 0.941\n"
+    f"- **Attention sparsity:** {_GLOBAL_STATS.get('attention_sparsity', 0.992)*100:.1f}%\n"
+    f"- **Training samples:** {_GLOBAL_STATS.get('n_samples', 1506):,}\n"
+    f"- **Model params:** {_REPORT.get('model_info', {}).get('params', 2442083):,}\n"
+)
+
+
+# ============================================================
+# Build Gradio UI
+# ============================================================
+
+_HEADER = """
+<div style="text-align:center; padding: 16px 0 8px 0;">
+  <h1 style="margin:0; font-size:2rem;">🧬 BioInteract</h1>
+  <p style="margin:4px 0; color:#555; font-size:1.05rem;">
+    Interpretable Drug–Target Interaction Prediction via Residue-Level Cross-Attention
+  </p>
+  <p style="margin:0; font-size:0.9rem; color:#888;">
+    GINE molecular graph encoder · ESM-2 protein language model · Bidirectional cross-attention
+  </p>
+</div>
+"""
+
+with gr.Blocks(
+    title="BioInteract — Interpretable DTI Prediction",
+    theme=gr.themes.Soft(primary_hue="blue"),
+) as demo:
+
+    gr.HTML(_HEADER)
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            with gr.Tabs():
+
+                # ── Tab 1: Case Studies ────────────────────────────────────
+                with gr.Tab("📊 Case Studies"):
+                    gr.Markdown(
+                        "Select a **pre-computed case study** to explore the model's "
+                        "atom–residue interaction map and binding residue predictions "
+                        "for clinically validated drug–target pairs."
+                    )
+                    case_dropdown = gr.Dropdown(
+                        choices=list(_FIXED_CASES.keys()),
+                        value=list(_FIXED_CASES.keys())[0],
+                        label="Select drug–target pair",
+                        interactive=True,
+                    )
+                    case_info_md = gr.Markdown()
+
+                    with gr.Row():
+                        case_heatmap   = gr.Image(label="Atom–Residue Interaction Heatmap",
+                                                  type='pil', height=400)
+                        with gr.Column():
+                            case_residues  = gr.Image(label="Top Binding Residues",
+                                                      type='pil', height=250)
+                            case_fg        = gr.Image(label="Pharmacophore Importance",
+                                                      type='pil', height=250)
+
+                    case_dropdown.change(
+                        fn=show_case_study,
+                        inputs=case_dropdown,
+                        outputs=[case_heatmap, case_residues, case_fg, case_info_md],
+                    )
+                    # Auto-load first case on startup
+                    demo.load(
+                        fn=show_case_study,
+                        inputs=[gr.State(list(_FIXED_CASES.keys())[0])],
+                        outputs=[case_heatmap, case_residues, case_fg, case_info_md],
+                    )
+
+                # ── Tab 2: Custom Prediction ───────────────────────────────
+                with gr.Tab("🔬 Custom Prediction"):
+                    gr.Markdown(
+                        "Enter any **drug SMILES** and **protein amino acid sequence** "
+                        "to get a binding prediction with an interaction heatmap.  \n"
+                        "> ⏳ First prediction loads ESM-2 (150 M params) on CPU — allow **1–3 minutes**.  \n"
+                        "> Sequences longer than 512 residues are automatically truncated."
+                    )
+                    with gr.Row():
+                        smiles_box = gr.Textbox(
+                            label="Drug SMILES",
+                            placeholder="e.g. Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1",
+                            lines=2,
+                        )
+                    sequence_box = gr.Textbox(
+                        label="Protein Amino Acid Sequence",
+                        placeholder="e.g. MGPSENDPNLFVALYDFVASGD...",
+                        lines=4,
+                        max_lines=8,
+                    )
+
+                    with gr.Row():
+                        example_btn = gr.Button("📋 Load Imatinib / ABL1 Example", variant="secondary")
+                        predict_btn = gr.Button("🚀 Predict", variant="primary")
+
+                    status_box = gr.Textbox(label="Status", interactive=False, lines=1)
+                    prob_md    = gr.Markdown()
+
+                    with gr.Row():
+                        pred_heatmap  = gr.Image(label="Atom–Residue Interaction Heatmap",
+                                                 type='pil', height=400)
+                        pred_residues = gr.Image(label="Top 10 Binding Residues",
+                                                 type='pil', height=300)
+
+                    example_btn.click(
+                        fn=lambda: (_EXAMPLE_SMILES, _EXAMPLE_SEQUENCE),
+                        inputs=[],
+                        outputs=[smiles_box, sequence_box],
+                    )
+                    predict_btn.click(
+                        fn=run_prediction,
+                        inputs=[smiles_box, sequence_box],
+                        outputs=[status_box, prob_md, pred_heatmap, pred_residues],
+                    )
+
+        # ── Sidebar ────────────────────────────────────────────────────────
+        with gr.Column(scale=1):
+            gr.Markdown(_stats_md)
+            gr.Markdown(
+                "### Architecture\n\n"
+                "```\n"
+                "Drug SMILES\n"
+                "  → GINE (3-layer)\n"
+                "  → atom repr (N×256)\n\n"
+                "Protein sequence\n"
+                "  → ESM-2 (150M)\n"
+                "  → physicochemical\n"
+                "  → residue repr (L×256)\n\n"
+                "Bidirectional\n"
+                "cross-attention\n"
+                "  → interaction map\n"
+                "  → gated pooling\n"
+                "  → binding score\n"
+                "```"
+            )
+            gr.Markdown(
+                "### Citation\n\n"
+                "Wang S, Zhang Q et al.  \n"
+                "*BioInteract: Interpretable DTI Prediction via "
+                "Residue-Level Cross-Attention with Biological Prior Knowledge.*  \n"
+                "PLOS Computational Biology, 2026."
+            )
+
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

checkpoints/best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9680d73224ee90aa464b92df30b2b6297405825d3e3b58864d1c38b9726df065
+size 29393230

configs/default.yaml

@@ -0,0 +1,37 @@
+# BioInteract Default Configuration
+
+model:
+  drug_encoder:
+    gnn_type: GINE
+    num_layers: 3
+    hidden_dim: 256
+    dropout: 0.2
+    edge_dim: 16
+    num_atom_features: 52
+    jk: last
+
+  target_encoder:
+    esm2_dim: 640             # esm2_t30_150M_UR50D → 640-dim
+    projection_dim: 256
+    domain_embed_dim: 32
+    num_domain_types: 50
+    use_domain_features: true
+
+  interaction:
+    num_heads: 8
+    attn_dim: 256
+    dropout: 0.1
+    pooling: gated
+
+  predictor:
+    hidden_dims: [256, 128]
+    dropout: 0.3
+    task: classification
+
+training:
+  batch_size: 64
+  lr: 1.0e-4
+  weight_decay: 1.0e-5
+  epochs: 100
+  patience: 15
+  seed: 42

examples/interpretability_report.json

@@ -0,0 +1,609 @@
+{
+  "model_info": {
+    "epoch": 19,
+    "best_metric": 0.6327288434593856,
+    "esm2_dim": 640,
+    "params": 2442083
+  },
+  "global_stats": {
+    "residue_attention_mean": 0.004351376555860043,
+    "residue_attention_std": 0.04624345526099205,
+    "residue_attention_median": 0.000272190198302269,
+    "residue_attention_top1pct": 0.05719282995909382,
+    "residue_attention_top5pct": 0.003616639366373424,
+    "n_samples": 1506,
+    "attention_sparsity": 0.992318922989442
+  },
+  "case_studies": [
+    {
+      "drug_name": "5328940",
+      "target_name": "ABL1(E255K)",
+      "drug_id": "D0010",
+      "target_id": "T0001",
+      "affinity_nM": 0.047,
+      "prediction_prob": 0.988015353679657,
+      "n_hotspots": 2,
+      "top_10_residues": [
+        [
+          "V104",
+          1.0
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+          0.5025863647460938
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+          0.41692954301834106
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+          0.1664445549249649
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+          0.1407129466533661
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+        "Amino": 0.6155399084091187,
+        "Halogen": 0.649691104888916,
+        "Ether": 0.3451891541481018,
+        "Heterocycle N": 0.1674310564994812
+      }
+    },
+    {
+      "drug_name": "3062316",
+      "target_name": "ABL1(F317I)",
+      "drug_id": "D0017",
+      "target_id": "T0002",
+      "affinity_nM": 0.1,
+      "prediction_prob": 0.9907914400100708,
+      "n_hotspots": 1,
+      "top_10_residues": [
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+          1.0
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+        "Heterocycle N": 0.24906468391418457,
+        "Carbonyl": 0.7986676692962646
+      }
+    },
+    {
+      "drug_name": "3062316",
+      "target_name": "ABL1(F317I)p",
+      "drug_id": "D0017",
+      "target_id": "T0003",
+      "affinity_nM": 0.041,
+      "prediction_prob": 0.9907914400100708,
+      "n_hotspots": 1,
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+          1.0
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+          0.13164293766021729
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+        "Carbonyl": 0.7986676692962646
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+    {
+      "drug_name": "156414",
+      "target_name": "EGFR",
+      "drug_id": "D0014",
+      "target_id": "T0112",
+      "affinity_nM": 0.19,
+      "prediction_prob": 0.995402455329895,
+      "n_hotspots": 4,
+      "top_10_residues": [
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+          "S525",
+          1.0
+        ],
+        [
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+          0.9849389791488647
+        ],
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+          "V250",
+          0.7280950546264648
+        ],
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+          0.6668700575828552
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+        ],
+        [
+          "Q408",
+          0.2837621867656708
+        ],
+        [
+          "A439",
+          0.2591591775417328
+        ],
+        [
+          "I106",
+          0.25760865211486816
+        ],
+        [
+          "A419",
+          0.22197671234607697
+        ],
+        [
+          "G242",
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+        ]
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+        "Halogen": 0.13448819518089294,
+        "Ether": 0.13556817173957825,
+        "Heterocycle N": 0.0,
+        "Carbonyl": 0.43106570839881897
+      }
+    },
+    {
+      "drug_name": "10184653",
+      "target_name": "EGFR(E746A750del)",
+      "drug_id": "D0004",
+      "target_id": "T0113",
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+          1.0
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+          0.44752317667007446
+        ],
+        [
+          "Q218",
+          0.3744635581970215
+        ],
+        [
+          "Q408",
+          0.24684257805347443
+        ],
+        [
+          "A439",
+          0.2239307016134262
+        ],
+        [
+          "A419",
+          0.20999976992607117
+        ],
+        [
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+          0.18570154905319214
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+        [
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+          0.18345363438129425
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+        "Amide": 0.5453693270683289,
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+        "Carbonyl": 0.5
+      }
+    },
+    {
+      "drug_name": "10184653",
+      "target_name": "EGFR(G719C)",
+      "drug_id": "D0004",
+      "target_id": "T0114",
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+          1.0
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+          0.9305046200752258
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+          0.4475232660770416
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+          0.37446358799934387
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+          0.24684259295463562
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+          0.22393076121807098
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+          0.20999978482723236
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+        "Halogen": 0.0,
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+        "Heterocycle N": 0.0,
+        "Carbonyl": 0.5
+      }
+    },
+    {
+      "drug_name": "11717001",
+      "target_name": "BRAF",
+      "drug_id": "D0023",
+      "target_id": "T0044",
+      "affinity_nM": 0.19,
+      "prediction_prob": 0.09866680204868317,
+      "n_hotspots": 1,
+      "top_10_residues": [
+        [
+          "T263",
+          1.0
+        ],
+        [
+          "A246",
+          0.43290892243385315
+        ],
+        [
+          "T167",
+          0.19155371189117432
+        ],
+        [
+          "W619",
+          0.1442342847585678
+        ],
+        [
+          "Q257",
+          0.11426610499620438
+        ],
+        [
+          "V238",
+          0.09902580827474594
+        ],
+        [
+          "S535",
+          0.05356824770569801
+        ],
+        [
+          "V168",
+          0.05098167061805725
+        ],
+        [
+          "T234",
+          0.03144577145576477
+        ],
+        [
+          "F250",
+          0.031349897384643555
+        ]
+      ],
+      "functional_groups": {
+        "Aromatic Ring": 0.0588817298412323,
+        "Hydroxyl": 0.0,
+        "Heterocycle N": 0.0
+      }
+    },
+    {
+      "drug_name": "11717001",
+      "target_name": "BRAF(V600E)",
+      "drug_id": "D0023",
+      "target_id": "T0045",
+      "affinity_nM": 0.18,
+      "prediction_prob": 0.09866680204868317,
+      "n_hotspots": 1,
+      "top_10_residues": [
+        [
+          "T263",
+          1.0
+        ],
+        [
+          "A246",
+          0.43290892243385315
+        ],
+        [
+          "T167",
+          0.19155371189117432
+        ],
+        [
+          "W619",
+          0.1442342847585678
+        ],
+        [
+          "Q257",
+          0.11426610499620438
+        ],
+        [
+          "V238",
+          0.09902580827474594
+        ],
+        [
+          "S535",
+          0.05356824770569801
+        ],
+        [
+          "V168",
+          0.05098167061805725
+        ],
+        [
+          "T234",
+          0.03144577145576477
+        ],
+        [
+          "F250",
+          0.031349897384643555
+        ]
+      ],
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+        "Aromatic Ring": 0.0588817298412323,
+        "Hydroxyl": 0.0,
+        "Heterocycle N": 0.0
+      }
+    },
+    {
+      "drug_name": "3062316",
+      "target_name": "ABL1(M351T)",
+      "drug_id": "D0017",
+      "target_id": "T0008",
+      "affinity_nM": 0.016,
+      "prediction_prob": 0.9907914400100708,
+      "n_hotspots": 1,
+      "top_10_residues": [
+        [
+          "V104",
+          1.0
+        ],
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+          0.4408968389034271
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+          0.431899756193161
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+          0.27281948924064636
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+          "P936",
+          0.21758989989757538
+        ],
+        [
+          "N707",
+          0.1882324367761612
+        ],
+        [
+          "L799",
+          0.15290923416614532
+        ],
+        [
+          "P651",
+          0.14969675242900848
+        ],
+        [
+          "K796",
+          0.13164293766021729
+        ],
+        [
+          "P934",
+          0.08160427957773209
+        ]
+      ],
+      "functional_groups": {
+        "Aromatic Ring": 0.2643488049507141,
+        "Hydroxyl": 0.6395330429077148,
+        "Amino": 0.8008821606636047,
+        "Amide": 0.8592739105224609,
+        "Halogen": 0.715941846370697,
+        "Heterocycle N": 0.249064639210701,
+        "Carbonyl": 0.7986676692962646
+      }
+    },
+    {
+      "drug_name": "3062316",
+      "target_name": "ABL1(F317L)p",
+      "drug_id": "D0017",
+      "target_id": "T0005",
+      "affinity_nM": 0.019,
+      "prediction_prob": 0.9907914400100708,
+      "n_hotspots": 1,
+      "top_10_residues": [
+        [
+          "V104",
+          1.0
+        ],
+        [
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+          0.4408968687057495
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+          0.431899756193161
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+          0.272819459438324
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+          0.21758989989757538
+        ],
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+          0.1882324367761612
+        ],
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+          0.15290921926498413
+        ],
+        [
+          "P651",
+          0.14969675242900848
+        ],
+        [
+          "K796",
+          0.13164293766021729
+        ],
+        [
+          "P934",
+          0.08160427957773209
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+        "Aromatic Ring": 0.2643488347530365,
+        "Hydroxyl": 0.6395331621170044,
+        "Amino": 0.80088210105896,
+        "Amide": 0.8592739105224609,
+        "Halogen": 0.715941846370697,
+        "Heterocycle N": 0.24906468391418457,
+        "Carbonyl": 0.7986676692962646
+      }
+    }
+  ]
+}

requirements.txt

@@ -0,0 +1,19 @@
+--find-links https://data.pyg.org/whl/torch-2.1.2+cpu.html
+torch==2.1.2
+torch_scatter
+torch_sparse
+torch_cluster
+torch_geometric==2.4.0
+rdkit
+transformers>=4.38.0
+accelerate
+gradio==4.44.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+seaborn>=0.12.0
+pandas>=2.0.0
+scipy>=1.11.0
+scikit-learn>=1.3.0
+PyYAML>=6.0
+Pillow>=9.0.0
+huggingface_hub>=0.21.0

src/data/mol_graph.py

@@ -0,0 +1,252 @@
+"""
+mol_graph.py — SMILES to molecular graph with chemistry-aware features.
+
+Node features include pharmacophore-relevant properties (H-bond donor/acceptor,
+hydrophobicity) so the model can learn chemically meaningful representations.
+
+v2: adds Morgan fingerprint (ECFP4) generation for dual-channel drug encoding.
+"""
+import torch
+import numpy as np
+from rdkit import Chem
+from rdkit.Chem import Descriptors, Crippen, rdMolDescriptors, AllChem
+from torch_geometric.data import Data
+
+
+# ============================================================
+# Atom (node) featurisation
+# ============================================================
+
+ATOM_TYPES = ['C', 'N', 'O', 'S', 'F', 'Cl', 'Br', 'I', 'P', 'Si', 'B',
+              'Na', 'K', 'Ca', 'Fe', 'Zn', 'Mg', 'Se', 'Unknown']
+
+HYBRIDIZATION = [
+    Chem.rdchem.HybridizationType.SP,
+    Chem.rdchem.HybridizationType.SP2,
+    Chem.rdchem.HybridizationType.SP3,
+    Chem.rdchem.HybridizationType.SP3D,
+    Chem.rdchem.HybridizationType.SP3D2,
+]
+
+FORMAL_CHARGE_RANGE = [-2, -1, 0, 1, 2]
+NUM_HS_RANGE = [0, 1, 2, 3, 4]
+
+
+def _one_hot(val, choices):
+    """One-hot encode a value; last dim is 'unknown'."""
+    enc = [0] * (len(choices) + 1)
+    try:
+        idx = choices.index(val)
+        enc[idx] = 1
+    except ValueError:
+        enc[-1] = 1
+    return enc
+
+
+def atom_features(atom, crippen_contribs=None, atom_idx=None):
+    """
+    Return a feature vector for a single atom.
+    
+    Features (total = 78 dims):
+      - atom type one-hot           (20)
+      - hybridisation one-hot       (6)
+      - formal charge one-hot       (6)
+      - num Hs one-hot              (6)
+      - degree                      (1)
+      - is aromatic                 (1)
+      - is in ring                  (1)
+      - ring size (3-8)             (6)
+      -- Chemistry priors --
+      - is H-bond donor             (1)
+      - is H-bond acceptor          (1)
+      - Crippen logP contribution   (1)  ← hydrophobicity
+      - Crippen MR contribution     (1)  ← molar refractivity
+      - Gasteiger charge            (1)  ← partial charge
+      
+    Pharmacophore-relevant features are marked above; they help the model
+    learn which atoms are likely to participate in binding interactions.
+    """
+    symbol = atom.GetSymbol()
+    features = []
+
+    # basic properties
+    features += _one_hot(symbol, ATOM_TYPES)                   # 20
+    features += _one_hot(atom.GetHybridization(), HYBRIDIZATION)  # 6
+    features += _one_hot(atom.GetFormalCharge(), FORMAL_CHARGE_RANGE)  # 6
+    features += _one_hot(atom.GetTotalNumHs(), NUM_HS_RANGE)   # 6
+
+    features.append(atom.GetDegree() / 6.0)                    # 1
+    features.append(int(atom.GetIsAromatic()))                  # 1
+    features.append(int(atom.IsInRing()))                       # 1
+
+    # ring sizes
+    ring_info = atom.GetOwningMol().GetRingInfo()
+    for size in range(3, 9):
+        features.append(int(ring_info.IsAtomInRingOfSize(atom.GetIdx(), size)))  # 6
+
+    # pharmacophore features
+    # H-bond donor: atom bonded to at least one H and is N or O
+    is_donor = (symbol in ['N', 'O'] and atom.GetTotalNumHs() > 0)
+    features.append(int(is_donor))                              # 1
+
+    # H-bond acceptor: N or O with lone pair
+    is_acceptor = (symbol in ['N', 'O', 'F'])
+    features.append(int(is_acceptor))                           # 1
+
+    # Crippen contributions (hydrophobicity, molar refractivity)
+    if crippen_contribs is not None and atom_idx is not None:
+        logp_contrib, mr_contrib = crippen_contribs[atom_idx]
+        features.append(logp_contrib)                           # 1
+        features.append(mr_contrib)                             # 1
+    else:
+        features.append(0.0)
+        features.append(0.0)
+
+    # Gasteiger partial charge
+    charge = float(atom.GetDoubleProp('_GasteigerCharge')) \
+        if atom.HasProp('_GasteigerCharge') else 0.0
+    if np.isnan(charge) or np.isinf(charge):
+        charge = 0.0
+    features.append(charge)                                     # 1
+
+    return features  # total: 20+6+6+6+1+1+1+6+1+1+1+1+1 = 52...
+    # recount: 20+6+6+6+1+1+1+6+1+1+1+1+1 = 52
+    # Note: actual dim depends on implementation; config should match
+
+
+# ============================================================
+# Bond (edge) featurisation
+# ============================================================
+
+BOND_TYPES = [
+    Chem.rdchem.BondType.SINGLE,
+    Chem.rdchem.BondType.DOUBLE,
+    Chem.rdchem.BondType.TRIPLE,
+    Chem.rdchem.BondType.AROMATIC,
+]
+
+BOND_STEREO = [
+    Chem.rdchem.BondStereo.STEREONONE,
+    Chem.rdchem.BondStereo.STEREOZ,
+    Chem.rdchem.BondStereo.STEREOE,
+    Chem.rdchem.BondStereo.STEREOCIS,
+    Chem.rdchem.BondStereo.STEREOTRANS,
+]
+
+
+def bond_features(bond):
+    """
+    Return a feature vector for a single bond.
+    
+    Features (total = 16 dims):
+      - bond type one-hot           (5)
+      - is conjugated               (1)
+      - is in ring                  (1)
+      - bond stereo one-hot         (6)
+      - bond direction info         (3)
+    """
+    features = []
+    features += _one_hot(bond.GetBondType(), BOND_TYPES)       # 5
+    features.append(int(bond.GetIsConjugated()))                # 1
+    features.append(int(bond.IsInRing()))                       # 1
+    features += _one_hot(bond.GetStereo(), BOND_STEREO)        # 6
+    
+    # bond direction (for chirality)
+    bd = bond.GetBondDir()
+    features.append(int(bd == Chem.rdchem.BondDir.BEGINWEDGE))
+    features.append(int(bd == Chem.rdchem.BondDir.ENDDOWNRIGHT))
+    features.append(int(bd == Chem.rdchem.BondDir.ENDUPRIGHT))  # 3
+    
+    return features  # total: 5+1+1+6+3 = 16
+
+
+# ============================================================
+# Full molecule → PyG Data
+# ============================================================
+
+def smiles_to_graph(smiles: str) -> Data | None:
+    """
+    Convert a SMILES string into a PyTorch Geometric Data object with
+    chemistry-aware node and edge features.
+    
+    Returns None if the SMILES cannot be parsed.
+    """
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+
+    # add Hs implicitly tracked, compute Gasteiger charges
+    Chem.rdPartialCharges.ComputeGasteigerCharges(mol)
+    crippen = Crippen.MolLogP(mol)  # just to ensure descriptors initialised
+    crippen_contribs = Crippen._GetAtomContribs(mol)
+
+    # --- node features ---
+    atom_feat_list = []
+    for idx, atom in enumerate(mol.GetAtoms()):
+        atom_feat_list.append(atom_features(atom, crippen_contribs, idx))
+    x = torch.tensor(atom_feat_list, dtype=torch.float)
+
+    # --- edge features ---
+    edge_index = []
+    edge_attr_list = []
+    for bond in mol.GetBonds():
+        i = bond.GetBeginAtomIdx()
+        j = bond.GetEndAtomIdx()
+        bf = bond_features(bond)
+        # undirected: add both directions
+        edge_index.append([i, j])
+        edge_index.append([j, i])
+        edge_attr_list.append(bf)
+        edge_attr_list.append(bf)
+
+    if len(edge_index) == 0:
+        # single atom molecule (rare but possible)
+        edge_index = torch.empty((2, 0), dtype=torch.long)
+        edge_attr = torch.empty((0, 16), dtype=torch.float)
+    else:
+        edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()
+        edge_attr = torch.tensor(edge_attr_list, dtype=torch.float)
+
+    return Data(x=x, edge_index=edge_index, edge_attr=edge_attr,
+                smiles=smiles, num_atoms=x.size(0))
+
+
+def get_atom_feature_dim():
+    """Return the dimensionality of atom features."""
+    # Build a dummy molecule to infer feature size
+    mol = Chem.MolFromSmiles('C')
+    Chem.rdPartialCharges.ComputeGasteigerCharges(mol)
+    contribs = Crippen._GetAtomContribs(mol)
+    return len(atom_features(mol.GetAtomWithIdx(0), contribs, 0))
+
+
+def get_bond_feature_dim():
+    """Return the dimensionality of bond features."""
+    return 16
+
+
+def smiles_to_morgan(smiles: str, n_bits: int = 1024, radius: int = 2) -> torch.Tensor | None:
+    """
+    Compute Morgan fingerprint (ECFP4) from SMILES.
+
+    Morgan fingerprints encode circular substructures and are inherently
+    more generalizable than learned GNN features — they capture chemical
+    similarity based on substructure occurrence regardless of whether the
+    exact molecule has been seen during training.
+
+    Args:
+        smiles: SMILES string
+        n_bits: fingerprint length (default 1024)
+        radius: Morgan radius (2 = ECFP4)
+
+    Returns:
+        (n_bits,) float tensor, or None if parsing fails
+    """
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return None
+    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
+    arr = np.zeros(n_bits, dtype=np.float32)
+    from rdkit.DataStructs import ConvertToNumpyArray
+    ConvertToNumpyArray(fp, arr)
+    return torch.from_numpy(arr)

src/data/protein_feat.py

@@ -0,0 +1,108 @@
+"""
+protein_feat.py — Protein feature processing for BioInteract Space demo.
+
+Adapted from the training version: removed dependency on internal
+src.utils.paths module so this file runs as a standalone module.
+ESM-2 embeddings are computed on-the-fly via transformers in app.py.
+"""
+import os
+import json
+import torch
+import numpy as np
+from typing import Optional
+
+
+# ============================================================
+# Standard amino acid vocabulary
+# ============================================================
+
+AMINO_ACIDS = list('ACDEFGHIKLMNPQRSTVWY')
+AA_TO_IDX = {aa: i for i, aa in enumerate(AMINO_ACIDS)}
+UNK_AA_IDX = len(AMINO_ACIDS)
+
+
+# ============================================================
+# Residue-level physicochemical properties (domain knowledge)
+# ============================================================
+
+HYDROPHOBICITY = {
+    'A': 1.8, 'R': -4.5, 'N': -3.5, 'D': -3.5, 'C': 2.5,
+    'E': -3.5, 'Q': -3.5, 'G': -0.4, 'H': -3.2, 'I': 4.5,
+    'L': 3.8, 'K': -3.9, 'M': 1.9, 'F': 2.8, 'P': -1.6,
+    'S': -0.8, 'T': -0.7, 'W': -0.9, 'Y': -1.3, 'V': 4.2,
+}
+
+MOL_WEIGHT = {
+    'A': 89, 'R': 174, 'N': 132, 'D': 133, 'C': 121,
+    'E': 147, 'Q': 146, 'G': 75, 'H': 155, 'I': 131,
+    'L': 131, 'K': 146, 'M': 149, 'F': 165, 'P': 115,
+    'S': 105, 'T': 119, 'W': 204, 'Y': 181, 'V': 117,
+}
+
+CHARGE = {
+    'A': 0, 'R': 1, 'N': 0, 'D': -1, 'C': 0,
+    'E': -1, 'Q': 0, 'G': 0, 'H': 0.1, 'I': 0,
+    'L': 0, 'K': 1, 'M': 0, 'F': 0, 'P': 0,
+    'S': 0, 'T': 0, 'W': 0, 'Y': 0, 'V': 0,
+}
+
+POLARITY = {
+    'A': 0, 'R': 1, 'N': 1, 'D': 1, 'C': 0,
+    'E': 1, 'Q': 1, 'G': 0, 'H': 1, 'I': 0,
+    'L': 0, 'K': 1, 'M': 0, 'F': 0, 'P': 0,
+    'S': 1, 'T': 1, 'W': 0, 'Y': 1, 'V': 0,
+}
+
+
+def residue_physicochemical_features(sequence: str) -> torch.Tensor:
+    """
+    Compute per-residue physicochemical feature vectors.
+    Returns tensor of shape (L, 4).
+    """
+    features = []
+    for aa in sequence:
+        h = HYDROPHOBICITY.get(aa, 0.0) / 4.5
+        w = MOL_WEIGHT.get(aa, 130.0) / 204.0
+        c = CHARGE.get(aa, 0.0)
+        p = POLARITY.get(aa, 0.0)
+        features.append([h, w, c, p])
+    return torch.tensor(features, dtype=torch.float)
+
+
+# ============================================================
+# Domain annotation helpers
+# ============================================================
+
+DOMAIN_TYPES = [
+    'Pkinase', 'Pkinase_Tyr', '7tm_1', '7tm_2', '7tm_3',
+    'Trypsin', 'Peptidase_C1', 'Metallopep', 'zf-CCHH', 'SH2',
+    'SH3_1', 'PH', 'RRM_1', 'Ion_trans', 'Ank', 'WD40',
+    'LRR_1', 'EGF', 'Ig', 'ABC_tran', 'Hormone_recep',
+    'HATPase_c', 'Helicase_C', 'RVT_1', 'Topoisom_bac', 'NONE',
+]
+
+DOMAIN_TO_IDX = {d: i for i, d in enumerate(DOMAIN_TYPES)}
+
+
+def residue_domain_labels(
+    sequence_length: int,
+    domain_info: Optional[dict] = None
+) -> torch.LongTensor:
+    """
+    Assign each residue a domain type index.
+    Returns LongTensor of shape (L,).
+    Residues without annotation get the 'NONE' label.
+    """
+    labels = torch.full(
+        (sequence_length,), DOMAIN_TO_IDX['NONE'], dtype=torch.long
+    )
+    if domain_info is not None and 'domains' in domain_info:
+        for dom in domain_info['domains']:
+            dom_type = dom.get('type', 'NONE')
+            if dom_type not in DOMAIN_TO_IDX:
+                dom_type = 'NONE'
+            idx = DOMAIN_TO_IDX[dom_type]
+            start = dom.get('start', 0)
+            end = min(dom.get('end', sequence_length), sequence_length)
+            labels[start:end] = idx
+    return labels

src/models/biointeract.py

@@ -0,0 +1,223 @@
+"""
+biointeract.py — Full BioInteract model (v2: dual-channel + focal loss ready).
+
+Connects drug encoder (GINE + Morgan FP), target encoder (ESM-2),
+cross-attention interaction module, and prediction head.
+
+v2 changes:
+    - Dual-channel drug encoding: GNN atoms + Morgan fingerprint global vector
+    - Graph augmentation (DropNode/DropEdge) for cold-start robustness
+    - Morgan fingerprint branch fused at prediction head
+"""
+import torch
+import torch.nn as nn
+from torch_geometric.data import Batch
+
+from .drug_encoder import DrugEncoder, MorganFPEncoder, GraphAugmentation
+from .target_encoder import TargetEncoder
+from .interaction import CrossAttentionInteraction, GatedPooling
+
+
+class BioInteract(nn.Module):
+    """
+    BioInteract: Interpretable Drug-Target Interaction Prediction
+    via Residue-Level Cross-Attention with Biological Prior Knowledge.
+    
+    Forward pass:
+        1. Encode drug molecular graph → per-atom representations
+        2. Encode protein (ESM-2 + domain features) → per-residue representations
+        3. Cross-attention interaction → atom-residue interaction map
+        4. Gated pooling → fixed-size vector
+        5. Prediction head → binding probability / affinity
+    """
+    
+    def __init__(self, config: dict):
+        super().__init__()
+        
+        drug_cfg = config.get('drug_encoder', {})
+        target_cfg = config.get('target_encoder', {})
+        inter_cfg = config.get('interaction', {})
+        pred_cfg = config.get('predictor', {})
+        
+        hidden_dim = drug_cfg.get('hidden_dim', 256)
+        
+        # --- configurable Morgan FP ---
+        self.use_morgan_fp = drug_cfg.get('use_morgan_fp', False)
+        
+        # --- graph augmentation (training only) ---
+        self.graph_aug = GraphAugmentation(
+            p_node=drug_cfg.get('drop_node', 0.0),
+            p_edge=drug_cfg.get('drop_edge', 0.0),
+        )
+        
+        # --- encoders ---
+        self.drug_encoder = DrugEncoder(
+            num_atom_features=drug_cfg.get('num_atom_features', 52),
+            edge_dim=drug_cfg.get('edge_dim', 16),
+            hidden_dim=hidden_dim,
+            num_layers=drug_cfg.get('num_layers', 3),
+            dropout=drug_cfg.get('dropout', 0.2),
+            jk=drug_cfg.get('jk', 'last'),
+        )
+        
+        # Morgan fingerprint branch (optional)
+        if self.use_morgan_fp:
+            morgan_nbits = drug_cfg.get('morgan_nbits', 1024)
+            self.morgan_encoder = MorganFPEncoder(
+                input_dim=morgan_nbits,
+                hidden_dim=hidden_dim,
+                dropout=drug_cfg.get('dropout', 0.2),
+            )
+        
+        self.target_encoder = TargetEncoder(
+            esm2_dim=target_cfg.get('esm2_dim', 1280),
+            projection_dim=hidden_dim,
+            physchem_dim=4,
+            domain_embed_dim=target_cfg.get('domain_embed_dim', 32),
+            num_domain_types=target_cfg.get('num_domain_types', 50),
+            use_domain_features=target_cfg.get('use_domain_features', True),
+        )
+        
+        # --- interaction ---
+        self.interaction = CrossAttentionInteraction(
+            hidden_dim=hidden_dim,
+            num_heads=inter_cfg.get('num_heads', 8),
+            dropout=inter_cfg.get('dropout', 0.1),
+        )
+        
+        # --- pooling ---
+        self.drug_pooling = GatedPooling(hidden_dim)
+        self.prot_pooling = GatedPooling(hidden_dim)
+        
+        # --- prediction head ---
+        # fusion dimension depends on whether Morgan FP is used
+        if self.use_morgan_fp:
+            pred_input_dim = hidden_dim * 3  # drug_gnn + protein + morgan
+        else:
+            pred_input_dim = hidden_dim * 2  # drug_gnn + protein
+        
+        pred_hidden = pred_cfg.get('hidden_dims', [256, 128])
+        pred_dropout = pred_cfg.get('dropout', 0.3)
+        self.task = pred_cfg.get('task', 'classification')
+        
+        layers = []
+        in_dim = pred_input_dim
+        for h_dim in pred_hidden:
+            layers.extend([
+                nn.Linear(in_dim, h_dim),
+                nn.ReLU(),
+                nn.Dropout(pred_dropout),
+            ])
+            in_dim = h_dim
+        layers.append(nn.Linear(in_dim, 1))
+        self.predictor = nn.Sequential(*layers)
+    
+    def forward(self,
+                drug_batch: Batch,
+                esm2_embedding: torch.Tensor,
+                physicochemical: torch.Tensor,
+                domain_labels: torch.Tensor,
+                protein_mask: torch.Tensor,
+                morgan_fp: torch.Tensor = None,
+                return_attention: bool = False):
+        """
+        Args:
+            drug_batch: PyG Batch of molecular graphs
+            esm2_embedding: (B, L, esm2_dim)
+            physicochemical: (B, L, 4)
+            domain_labels: (B, L)
+            protein_mask: (B, L) boolean
+            morgan_fp: (B, 1024) Morgan fingerprint vectors
+            return_attention: if True, also return attention maps
+        
+        Returns:
+            prediction: (B, 1) — binding score
+            attention_data: dict (only if return_attention=True)
+        """
+        batch_size = esm2_embedding.size(0)
+        
+        # 0. graph augmentation (training only)
+        drug_batch = self.graph_aug(drug_batch)
+        
+        # 1. encode drug → per-atom representations
+        atom_repr, batch_index = self.drug_encoder(drug_batch)
+        
+        # 2. encode protein → per-residue representations
+        residue_repr = self.target_encoder(
+            esm2_embedding, physicochemical, domain_labels, protein_mask
+        )
+        
+        # 3. reshape drug atoms into (B, max_atoms, D) for cross-attention
+        drug_repr_padded, drug_mask = self._pad_drug_atoms(
+            atom_repr, batch_index, batch_size
+        )
+        
+        # 4. cross-attention interaction
+        drug_updated, prot_updated, interaction_map = self.interaction(
+            drug_repr_padded, drug_mask, residue_repr, protein_mask
+        )
+        
+        # 5. gated pooling to fixed-size vectors
+        drug_pooled = self.drug_pooling(drug_updated, drug_mask)  # (B, D)
+        prot_pooled = self.prot_pooling(prot_updated, protein_mask)  # (B, D)
+        
+        # 6. Fusion — conditionally include Morgan FP
+        if self.use_morgan_fp and morgan_fp is not None:
+            morgan_repr = self.morgan_encoder(morgan_fp)  # (B, D)
+            fused = torch.cat([drug_pooled, prot_pooled, morgan_repr], dim=-1)  # (B, 3D)
+        else:
+            fused = torch.cat([drug_pooled, prot_pooled], dim=-1)  # (B, 2D)
+        
+        # 7. predict
+        prediction = self.predictor(fused)  # (B, 1)
+        
+        # NOTE: for classification, we return raw logits here.
+        # Apply sigmoid only during inference (not training with AMP).
+        # Use BCEWithLogitsLoss for training.
+        
+        if return_attention:
+            return prediction, {
+                'interaction_map': interaction_map,
+                'drug_mask': drug_mask,
+                'protein_mask': protein_mask,
+            }
+        
+        return prediction
+    
+    def predict_proba(self, *args, **kwargs):
+        """Return probabilities (sigmoid applied) for inference."""
+        logits = self.forward(*args, **kwargs)
+        if isinstance(logits, tuple):
+            return torch.sigmoid(logits[0]), logits[1]
+        return torch.sigmoid(logits)
+    
+    def _pad_drug_atoms(self, atom_repr, batch_index, batch_size):
+        """
+        Convert scattered atom representations to padded batch tensor.
+        
+        Args:
+            atom_repr: (total_atoms, D) — all atoms from all graphs
+            batch_index: (total_atoms,) — which graph each atom belongs to
+            batch_size: int
+        
+        Returns:
+            padded: (B, max_atoms, D)
+            mask: (B, max_atoms) boolean
+        """
+        device = atom_repr.device
+        D = atom_repr.size(-1)
+        
+        # count atoms per graph
+        counts = torch.bincount(batch_index, minlength=batch_size)
+        max_atoms = counts.max().item()
+        
+        padded = torch.zeros(batch_size, max_atoms, D, device=device)
+        mask = torch.zeros(batch_size, max_atoms, dtype=torch.bool, device=device)
+        
+        for i in range(batch_size):
+            atom_indices = (batch_index == i).nonzero(as_tuple=True)[0]
+            n = atom_indices.size(0)
+            padded[i, :n] = atom_repr[atom_indices]
+            mask[i, :n] = True
+        
+        return padded, mask

src/models/drug_encoder.py

@@ -0,0 +1,209 @@
+"""
+drug_encoder.py — GINE-based molecular graph encoder + Morgan fingerprint fusion.
+
+Uses Graph Isomorphism Network with Edge features (GINE) for learned
+molecular representations, combined with Morgan fingerprints (ECFP4)
+for knowledge-driven chemical similarity encoding.
+
+v2: adds DropNode/DropEdge graph augmentation for cold-start robustness,
+    and a MorganFPEncoder for dual-channel drug representation.
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_geometric.nn import GINEConv, BatchNorm
+from torch_geometric.data import Batch
+
+
+class GINELayer(nn.Module):
+    """Single GINE layer with batch norm and residual connection."""
+    
+    def __init__(self, hidden_dim: int, edge_dim: int, dropout: float = 0.2):
+        super().__init__()
+        # GINE uses an MLP as the update function
+        mlp = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim * 2),
+            nn.ReLU(),
+            nn.Linear(hidden_dim * 2, hidden_dim),
+        )
+        self.conv = GINEConv(nn=mlp, edge_dim=edge_dim)
+        self.bn = BatchNorm(hidden_dim)
+        self.dropout = nn.Dropout(dropout)
+    
+    def forward(self, x, edge_index, edge_attr):
+        h = self.conv(x, edge_index, edge_attr)
+        h = self.bn(h)
+        h = F.relu(h)
+        h = self.dropout(h)
+        # residual connection
+        return h + x
+
+
+class DrugEncoder(nn.Module):
+    """
+    GINE-based drug molecular graph encoder.
+    
+    Produces per-atom representations suitable for cross-attention
+    with protein residue representations.
+    
+    Architecture:
+        Input projection → N × GINE layers → atom-level output
+    
+    We intentionally do NOT apply global readout (mean/sum pooling)
+    here — the cross-attention module needs individual atom vectors
+    to compute residue-level interaction maps.
+    """
+    
+    def __init__(self,
+                 num_atom_features: int = 52,
+                 edge_dim: int = 16,
+                 hidden_dim: int = 256,
+                 num_layers: int = 3,
+                 dropout: float = 0.2,
+                 jk: str = 'last'):
+        """
+        Args:
+            num_atom_features: input atom feature dimension
+            edge_dim: bond feature dimension
+            hidden_dim: GINE hidden dimension
+            num_layers: number of GINE layers
+            dropout: dropout rate
+            jk: jumping knowledge mode ('last' or 'cat')
+        """
+        super().__init__()
+        self.num_layers = num_layers
+        self.jk = jk
+        
+        # input projection
+        self.input_proj = nn.Sequential(
+            nn.Linear(num_atom_features, hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+        )
+        
+        # edge feature projection (match edge_attr dim to hidden_dim for GINE)
+        self.edge_proj = nn.Linear(edge_dim, hidden_dim)
+        
+        # GINE layers
+        self.layers = nn.ModuleList([
+            GINELayer(hidden_dim, hidden_dim, dropout)
+            for _ in range(num_layers)
+        ])
+        
+        # jumping knowledge output
+        if jk == 'cat':
+            self.jk_proj = nn.Linear(hidden_dim * num_layers, hidden_dim)
+        
+        self.output_dim = hidden_dim
+    
+    def forward(self, drug_batch: Batch) -> tuple:
+        """
+        Args:
+            drug_batch: PyG Batch of molecular graphs
+        
+        Returns:
+            atom_repr: (total_atoms, hidden_dim) — per-atom representations
+            batch_index: (total_atoms,) — which graph each atom belongs to
+        """
+        x = drug_batch.x
+        edge_index = drug_batch.edge_index
+        edge_attr = drug_batch.edge_attr
+        batch_index = drug_batch.batch
+        
+        # project inputs
+        h = self.input_proj(x)
+        edge_features = self.edge_proj(edge_attr) if edge_attr.size(0) > 0 \
+            else edge_attr
+        
+        # message passing
+        layer_outputs = []
+        for layer in self.layers:
+            h = layer(h, edge_index, edge_features)
+            layer_outputs.append(h)
+        
+        # jumping knowledge
+        if self.jk == 'cat':
+            h = torch.cat(layer_outputs, dim=-1)
+            h = self.jk_proj(h)
+        else:
+            h = layer_outputs[-1]
+        
+        return h, batch_index
+
+
+class GraphAugmentation(nn.Module):
+    """
+    Stochastic graph augmentation for regularisation during training.
+
+    DropNode: randomly masks node features (zero-out) with probability p_node.
+    DropEdge: randomly removes edges with probability p_edge.
+
+    These augmentations prevent the GNN from memorising specific molecular
+    graphs, which is critical for cold-start generalisation where test
+    molecules are unseen during training.
+
+    Reference: Rong et al., "DropEdge: Towards Deep Graph ConvNets", ICLR 2020
+    """
+
+    def __init__(self, p_node: float = 0.1, p_edge: float = 0.15):
+        super().__init__()
+        self.p_node = p_node
+        self.p_edge = p_edge
+
+    def forward(self, drug_batch: Batch) -> Batch:
+        if not self.training:
+            return drug_batch
+
+        # DropNode: zero-out whole node feature vectors
+        if self.p_node > 0:
+            mask = torch.rand(drug_batch.x.size(0), 1,
+                              device=drug_batch.x.device) > self.p_node
+            drug_batch.x = drug_batch.x * mask.float()
+
+        # DropEdge: remove random edges
+        if self.p_edge > 0 and drug_batch.edge_index.size(1) > 0:
+            n_edges = drug_batch.edge_index.size(1)
+            keep = torch.rand(n_edges, device=drug_batch.edge_index.device) > self.p_edge
+            drug_batch.edge_index = drug_batch.edge_index[:, keep]
+            if drug_batch.edge_attr is not None and drug_batch.edge_attr.size(0) == n_edges:
+                drug_batch.edge_attr = drug_batch.edge_attr[keep]
+
+        return drug_batch
+
+
+class MorganFPEncoder(nn.Module):
+    """
+    Encoder for Morgan (ECFP4) molecular fingerprints.
+
+    Morgan fingerprints capture circular substructure patterns and provide
+    a chemistry-prior-based global drug representation that inherently
+    generalises to unseen molecules — unlike GNN features that depend on
+    message passing over specific graph topologies.
+
+    This encoder projects the binary fingerprint to a dense vector and
+    is fused with the GNN representation for dual-channel drug encoding.
+    """
+
+    def __init__(self, input_dim: int = 1024, hidden_dim: int = 256,
+                 dropout: float = 0.2):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim * 2),
+            nn.LayerNorm(hidden_dim * 2),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim * 2, hidden_dim),
+            nn.LayerNorm(hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+        )
+        self.output_dim = hidden_dim
+
+    def forward(self, fp: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            fp: (B, input_dim) binary Morgan fingerprint
+        Returns:
+            (B, hidden_dim) dense drug representation
+        """
+        return self.encoder(fp)

src/models/interaction.py

@@ -0,0 +1,187 @@
+"""
+interaction.py — Cross-Attention interaction module.
+
+This is the core of BioInteract: computing fine-grained atom-residue
+interaction maps that are both predictive and interpretable.
+
+Design philosophy:
+    Traditional DTI models concatenate drug and protein vectors and
+    lose spatial information. Our cross-attention computes an explicit
+    interaction matrix M ∈ R^(n_atoms × n_residues), where M[i,j]
+    represents the "interaction strength" between drug atom i and
+    protein residue j.
+    
+    This matrix directly corresponds to the biological concept of
+    molecular contacts — and can be validated against crystal structures.
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+class CrossAttentionInteraction(nn.Module):
+    """
+    Multi-head cross-attention between drug atoms and protein residues.
+    
+    Returns:
+        - Fused representation for prediction
+        - Attention weights for interpretability (atom × residue interaction map)
+    """
+    
+    def __init__(self,
+                 hidden_dim: int = 256,
+                 num_heads: int = 8,
+                 dropout: float = 0.1):
+        super().__init__()
+        assert hidden_dim % num_heads == 0
+        
+        self.hidden_dim = hidden_dim
+        self.num_heads = num_heads
+        self.head_dim = hidden_dim // num_heads
+        self.scale = math.sqrt(self.head_dim)
+        
+        # drug atoms attend to protein residues
+        self.W_q_drug = nn.Linear(hidden_dim, hidden_dim)
+        self.W_k_prot = nn.Linear(hidden_dim, hidden_dim)
+        self.W_v_prot = nn.Linear(hidden_dim, hidden_dim)
+        
+        # protein residues attend to drug atoms (bidirectional)
+        self.W_q_prot = nn.Linear(hidden_dim, hidden_dim)
+        self.W_k_drug = nn.Linear(hidden_dim, hidden_dim)
+        self.W_v_drug = nn.Linear(hidden_dim, hidden_dim)
+        
+        # output projections
+        self.out_proj_drug = nn.Linear(hidden_dim, hidden_dim)
+        self.out_proj_prot = nn.Linear(hidden_dim, hidden_dim)
+        
+        # layer norms
+        self.ln_drug = nn.LayerNorm(hidden_dim)
+        self.ln_prot = nn.LayerNorm(hidden_dim)
+        
+        self.dropout = nn.Dropout(dropout)
+    
+    def _attention(self, Q, K, V, mask=None):
+        """
+        Standard scaled dot-product attention.
+        
+        Returns:
+            output: attended values
+            attn_weights: softmax attention weights (for interpretability)
+        """
+        # Q: (B, H, Lq, d), K: (B, H, Lk, d), V: (B, H, Lk, d)
+        scores = torch.matmul(Q, K.transpose(-2, -1)) / self.scale  # (B,H,Lq,Lk)
+        
+        if mask is not None:
+            # mask shape: (B, 1, 1, Lk) — broadcast over heads and queries
+            scores = scores.masked_fill(~mask, float('-inf'))
+        
+        attn_weights = F.softmax(scores, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        output = torch.matmul(attn_weights, V)  # (B, H, Lq, d)
+        return output, attn_weights
+    
+    def _reshape_to_heads(self, x, batch_size):
+        """(B, L, D) → (B, H, L, d)"""
+        return x.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+    
+    def forward(self,
+                drug_repr: torch.Tensor,
+                drug_mask: torch.Tensor,
+                prot_repr: torch.Tensor,
+                prot_mask: torch.Tensor):
+        """
+        Bidirectional cross-attention between drug atoms and protein residues.
+        
+        Args:
+            drug_repr: (B, N_atoms, D) — per-atom drug representations
+            drug_mask: (B, N_atoms) — boolean mask for drug atoms
+            prot_repr: (B, L_residues, D) — per-residue protein representations
+            prot_mask: (B, L_residues) — boolean mask for protein residues
+        
+        Returns:
+            drug_updated: (B, N_atoms, D) — drug repr enriched by protein context
+            prot_updated: (B, L_residues, D) — protein repr enriched by drug context
+            interaction_map: (B, N_atoms, L_residues) — attention-based interaction matrix
+                this is the key output for interpretability analysis
+        """
+        B = drug_repr.size(0)
+        
+        # --- Drug → Protein attention ---
+        Q_d = self._reshape_to_heads(self.W_q_drug(drug_repr), B)
+        K_p = self._reshape_to_heads(self.W_k_prot(prot_repr), B)
+        V_p = self._reshape_to_heads(self.W_v_prot(prot_repr), B)
+        
+        # mask: (B, L_residues) → (B, 1, 1, L_residues)
+        prot_attn_mask = prot_mask.unsqueeze(1).unsqueeze(2) if prot_mask is not None else None
+        
+        drug_attended, drug_to_prot_attn = self._attention(Q_d, K_p, V_p, prot_attn_mask)
+        # drug_to_prot_attn: (B, H, N_atoms, L_residues)
+        
+        drug_attended = drug_attended.transpose(1, 2).contiguous().view(B, -1, self.hidden_dim)
+        drug_attended = self.out_proj_drug(drug_attended)
+        drug_updated = self.ln_drug(drug_repr + drug_attended)
+        
+        # --- Protein → Drug attention ---
+        Q_p = self._reshape_to_heads(self.W_q_prot(prot_repr), B)
+        K_d = self._reshape_to_heads(self.W_k_drug(drug_repr), B)
+        V_d = self._reshape_to_heads(self.W_v_drug(drug_repr), B)
+        
+        drug_attn_mask = drug_mask.unsqueeze(1).unsqueeze(2) if drug_mask is not None else None
+        
+        prot_attended, _ = self._attention(Q_p, K_d, V_d, drug_attn_mask)
+        
+        prot_attended = prot_attended.transpose(1, 2).contiguous().view(B, -1, self.hidden_dim)
+        prot_attended = self.out_proj_prot(prot_attended)
+        prot_updated = self.ln_prot(prot_repr + prot_attended)
+        
+        # --- Interaction map (averaged over heads) ---
+        # This is what we visualise and validate against binding sites
+        interaction_map = drug_to_prot_attn.mean(dim=1)  # (B, N_atoms, L_residues)
+        
+        return drug_updated, prot_updated, interaction_map
+
+
+class GatedPooling(nn.Module):
+    """
+    Gated pooling for aggregating atom/residue-level features into
+    a fixed-size vector for prediction.
+    
+    Instead of mean/max pooling, we learn which atoms and residues
+    are most important for the final prediction. The gate weights
+    are themselves interpretable signals.
+    """
+    
+    def __init__(self, hidden_dim: int):
+        super().__init__()
+        self.gate = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.Tanh(),
+            nn.Linear(hidden_dim, 1),
+        )
+        self.transform = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.Tanh(),
+        )
+    
+    def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
+        """
+        Args:
+            x: (B, L, D) — sequence of feature vectors
+            mask: (B, L) — boolean mask
+        
+        Returns:
+            pooled: (B, D) — single feature vector per sample
+        """
+        gate_scores = self.gate(x).squeeze(-1)  # (B, L)
+        
+        if mask is not None:
+            gate_scores = gate_scores.masked_fill(~mask, float('-inf'))
+        
+        gate_weights = F.softmax(gate_scores, dim=-1)  # (B, L)
+        
+        transformed = self.transform(x)  # (B, L, D)
+        pooled = torch.bmm(gate_weights.unsqueeze(1), transformed).squeeze(1)  # (B, D)
+        
+        return pooled

src/models/target_encoder.py

@@ -0,0 +1,97 @@
+"""
+target_encoder.py — Protein target encoder combining ESM-2 with domain knowledge.
+
+Takes pre-extracted ESM-2 embeddings and enriches them with:
+  1. Physicochemical amino acid properties (hydrophobicity, charge, etc.)
+  2. Learnable functional domain embeddings (Pfam/InterPro)
+
+This design lets us inject biological knowledge about protein structure
+and function without any additional computational cost at training time.
+"""
+import torch
+import torch.nn as nn
+
+
+class TargetEncoder(nn.Module):
+    """
+    Protein residue-level encoder.
+    
+    Input:
+        - ESM-2 embeddings: (B, L, 1280) — pretrained, frozen
+        - Physicochemical features: (B, L, 4) — amino acid properties
+        - Domain labels: (B, L) — functional domain type indices
+    
+    Output:
+        - Residue representations: (B, L, projection_dim)
+    
+    The domain embedding adds awareness of which functional region each
+    residue belongs to (e.g., kinase domain, SH2 domain). This helps the
+    cross-attention module learn family-specific interaction patterns:
+    a kinase inhibitor should attend differently to residues in the
+    catalytic domain vs. a regulatory domain.
+    """
+    
+    def __init__(self,
+                 esm2_dim: int = 1280,
+                 projection_dim: int = 256,
+                 physchem_dim: int = 4,
+                 domain_embed_dim: int = 32,
+                 num_domain_types: int = 50,
+                 use_domain_features: bool = True,
+                 dropout: float = 0.1):
+        super().__init__()
+        self.use_domain = use_domain_features
+        self.projection_dim = projection_dim
+        
+        # total input dimension
+        input_dim = esm2_dim + physchem_dim
+        if use_domain_features:
+            input_dim += domain_embed_dim
+            self.domain_embedding = nn.Embedding(
+                num_domain_types + 1,  # +1 for padding
+                domain_embed_dim,
+                padding_idx=num_domain_types,
+            )
+        
+        # projection: compress concatenated features to target dim
+        self.projection = nn.Sequential(
+            nn.Linear(input_dim, projection_dim * 2),
+            nn.LayerNorm(projection_dim * 2),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(projection_dim * 2, projection_dim),
+            nn.LayerNorm(projection_dim),
+        )
+    
+    def forward(self,
+                esm2_embedding: torch.Tensor,
+                physicochemical: torch.Tensor,
+                domain_labels: torch.Tensor = None,
+                protein_mask: torch.Tensor = None) -> torch.Tensor:
+        """
+        Args:
+            esm2_embedding: (B, L, 1280) pretrained embeddings
+            physicochemical: (B, L, 4) amino acid properties
+            domain_labels: (B, L) LongTensor of domain type indices
+            protein_mask: (B, L) boolean mask (True = valid residue)
+        
+        Returns:
+            residue_repr: (B, L, projection_dim) residue representations
+        """
+        parts = [esm2_embedding, physicochemical]
+        
+        if self.use_domain and domain_labels is not None:
+            domain_emb = self.domain_embedding(domain_labels)
+            parts.append(domain_emb)
+        
+        # concatenate all feature sources
+        combined = torch.cat(parts, dim=-1)
+        
+        # project
+        residue_repr = self.projection(combined)
+        
+        # zero out padded positions
+        if protein_mask is not None:
+            residue_repr = residue_repr * protein_mask.unsqueeze(-1).float()
+        
+        return residue_repr