feat: Complete Qdrant pipeline with real PCA + cleanup

- Ingestion pipeline: TDC KIBA dataset -> DeepPurpose embeddings -> Qdrant
- API server on port 8001 with /health, /api/points, /api/search
- Real sklearn PCA (not fake first-3-dims)
- Fixed shuffle bug (preserve data ordering)
- Fixed result_folder override for saved config.pkl
- Cleaned up: deleted old/, .github/, added proper .gitignore
- 23,531 drug-target pairs indexed with 3D projections

.gitignore

@@ -1 +1,27 @@
-.venv
+# Python
+.venv/
+__pycache__/
+*.pyc
+*.pyo
+*.egg-info/
+.eggs/
+dist/
+build/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+
+# Logs & cache
+*.log
+.cache/
+runs/*/events.out.tfevents.*
+
+# Model artifacts (large files)
+*.pt
+*.npy
+
+# OS
+.DS_Store
+Thumbs.db

INSTRUCTIONS.md

@@ -0,0 +1,68 @@
+# Hackathon Setup: DeepPurpose + Qdrant + UI (v2)
+
+## Architecture Overview
+```
+┌─────────────┐     ┌──────────────┐     ┌─────────────┐
+│  Next.js UI │────▶│  FastAPI     │────▶│   Qdrant    │
+│  (3000)     │     │  (8000)      │     │   (6333)    │
+└─────────────┘     └──────────────┘     └─────────────┘
+                           │
+                    ┌──────┴──────┐
+                    │ DeepPurpose │
+                    │   Model     │
+                    └─────────────┘
+```
+
+## Quick Start
+
+### 1. Start Qdrant (Vector Database)
+```bash
+docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
+```
+
+### 2. Ingest Data (Phase 1)
+```bash
+.venv\Scripts\python ingest_qdrant.py
+```
+This will:
+- Load the trained model from `runs/20260125_104915_KIBA`
+- Generate embeddings for all drug-target pairs
+- Compute **real PCA** projections (not fake first-3-dims!)
+- Upload to Qdrant with pre-computed 3D coordinates
+
+### 3. Start API Server (Phase 2)
+```bash
+.venv\Scripts\python server/api.py
+```
+API endpoints:
+- `GET /api/points?limit=500&view=combined` - Get 3D visualization data
+- `POST /api/search` - Find similar drugs/targets
+- `GET /health` - Check system status
+
+### 4. Start Frontend (Phase 3)
+```bash
+cd ui && pnpm dev
+```
+Open http://localhost:3000/explorer
+
+## What's Fixed (v2)
+
+| Issue | Before | After |
+|-------|--------|-------|
+| PCA | First 3 dims (meaningless) | Real sklearn PCA |
+| Data Order | Shuffled (broken alignment) | `shuffle=False` in DataLoader |
+| Dummy Data | `"M" * 10` (fragile) | Valid Aspirin SMILES |
+| Config | Duplicated | Shared `config.py` |
+| Error Handling | None | Validation + helpful messages |
+| Model Loading | Per-request | Cached at startup |
+
+## Best Model Results (Kept Runs)
+
+| Dataset | CI | Pearson | Has model.pt |
+|---------|-------|---------|--------------|
+| BindingDB_Kd | **0.8083** | 0.7679 | No |
+| DAVIS | 0.7914 | 0.5446 | No |
+| KIBA | 0.7003 | 0.5219 | **Yes** |
+
+*Note: KIBA run has the saved model, but BindingDB has best metrics.*
+

config.py

@@ -0,0 +1,39 @@
+"""
+Shared configuration for DeepPurpose + Qdrant Pipeline.
+Used by: ingest_qdrant.py, server/api.py
+"""
+import os
+
+# --- MODEL CONFIG ---
+# Best performing run with saved model.pt
+BEST_MODEL_RUN = r"runs\20260125_104915_KIBA"
+
+# Encoding config - MUST match what was used during training!
+# Verified from config.pkl: Morgan + CNN
+MODEL_CONFIG = {
+    "drug_encoding": "Morgan",      # Morgan fingerprints
+    "target_encoding": "CNN",       # CNN for protein sequences
+    "cls_hidden_dims": [1024, 1024, 512],
+    "hidden_dim_drug": 128,
+    "hidden_dim_protein": 128,
+}
+
+# Data source - best CI run for ground truth
+PREDICTIONS_SOURCE = r"runs\20260125_080409_BindingDB_Kd\predictions_test.csv"
+
+# --- QDRANT CONFIG ---
+QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
+QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
+COLLECTION_NAME = "bio_discovery"
+
+# --- METRICS (from best runs) ---
+METRICS = {
+    "KIBA": {"CI": 0.7003, "Pearson": 0.5219, "MSE": 0.0008},
+    "BindingDB_Kd": {"CI": 0.8083, "Pearson": 0.7679, "MSE": 0.6668},
+    "DAVIS": {"CI": 0.7914, "Pearson": 0.5446, "MSE": 0.4684},
+}
+
+# --- VALID SEQUENCES FOR API ---
+# Minimal valid sequences for encoding (avoids dummy data issues)
+VALID_DUMMY_DRUG = "CC(=O)OC1=CC=CC=C1C(=O)O"  # Aspirin SMILES
+VALID_DUMMY_TARGET = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQQIAAALEHHHHHH"  # Short valid protein

runs/20260124_224109_KIBA/Morgan_CNN_logits.npy → data/kiba.tab

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:61b5181334431487cd5661bb14d6c33591a29e458e9af29d613b9a3735c64b0c
-size 94256
+oid sha256:eb77bed3ba64cc0cd07e05ff7f4b94f94a866a54cf673bb2c798cfeb0ebe2322
+size 96551234

ingest_qdrant.py

@@ -0,0 +1,309 @@
+"""
+Phase 1: DeepPurpose -> Qdrant Ingestion Pipeline (v2 - Fixed)
+
+Fixes applied:
+- Proper PCA dimensionality reduction (not just first 3 dims)
+- No shuffle to preserve data ordering
+- Proper error handling and validation
+- Shared config import
+"""
+import os
+os.environ["DGL_DISABLE_GRAPHBOLT"] = "1"
+
+import sys
+import numpy as np
+import pandas as pd
+import torch
+import pickle
+from sklearn.decomposition import PCA
+from qdrant_client import QdrantClient
+from qdrant_client.models import VectorParams, Distance, PointStruct
+from DeepPurpose import utils, DTI as dp_models
+import warnings
+warnings.filterwarnings("ignore")
+
+# Import shared config
+from config import (
+    BEST_MODEL_RUN, MODEL_CONFIG,
+    QDRANT_HOST, QDRANT_PORT, COLLECTION_NAME, METRICS
+)
+
+def validate_model_path(run_dir: str) -> bool:
+    """Check if run directory contains valid model."""
+    model_path = os.path.join(run_dir, "model.pt")
+    if not os.path.exists(model_path):
+        print(f"[ERROR] No model.pt found in {run_dir}")
+        print("        Available runs with models:")
+        for d in os.listdir("runs"):
+            if os.path.exists(os.path.join("runs", d, "model.pt")):
+                print(f"          - runs/{d}")
+        return False
+    return True
+
+def load_model(run_dir: str):
+    """Load DeepPurpose model with validation."""
+    if not validate_model_path(run_dir):
+        return None
+        
+    print(f"[1/6] Loading Model from {run_dir}...")
+    
+    # Load the config.pkl if it exists for exact config match
+    config_path = os.path.join(run_dir, "config.pkl")
+    if os.path.exists(config_path):
+        with open(config_path, "rb") as f:
+            config = pickle.load(f)
+        print("      Using saved config.pkl")
+        # Override result_folder to current directory (old path may be stale)
+        config["result_folder"] = run_dir
+    else:
+        # Fallback to hardcoded config
+        config = utils.generate_config(
+            drug_encoding=MODEL_CONFIG["drug_encoding"], 
+            target_encoding=MODEL_CONFIG["target_encoding"], 
+            cls_hidden_dims=MODEL_CONFIG["cls_hidden_dims"], 
+            train_epoch=1, LR=1e-4, batch_size=256,
+            result_folder=run_dir
+        )
+        print("      Using hardcoded config (no config.pkl found)")
+    
+    model = dp_models.model_initialize(**config)
+    model.load_pretrained(os.path.join(run_dir, "model.pt"))
+    model.model.eval()
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.model.to(device)
+    print(f"      Model loaded on {device}")
+    
+    return model
+
+def load_data(dataset_name: str = "KIBA") -> pd.DataFrame:
+    """
+    Load DTI dataset from TDC (Therapeutics Data Commons).
+    We need the original Drug/Target sequences to generate embeddings.
+    """
+    print(f"[2/6] Loading {dataset_name} Dataset from TDC...")
+    
+    from tdc.multi_pred import DTI
+    data = DTI(name=dataset_name)
+    
+    # Get just the test split (smaller, faster for demo)
+    split = data.get_split()
+    df = split['test']  # Use test set
+    
+    # TDC uses different column names
+    df = df.rename(columns={'Drug': 'Drug', 'Target': 'Target', 'Y': 'Label'})
+    
+    print(f"      Loaded {len(df)} test samples")
+    print(f"      Columns: {list(df.columns)}")
+    print(f"      Sample Drug (SMILES): {df['Drug'].iloc[0][:50]}...")
+    print(f"      Sample Target (first 50 aa): {df['Target'].iloc[0][:50]}...")
+    
+    return df
+
+def generate_embeddings(model, df: pd.DataFrame):
+    """
+    Generate embeddings WITHOUT shuffling to preserve order alignment.
+    Returns: (drug_embeddings, target_embeddings, labels)
+    """
+    print("[3/6] Generating Embeddings (no shuffle)...")
+    
+    drugs = df['Drug'].values
+    targets = df['Target'].values
+    labels = df['Label'].values
+    
+    drug_encoding = model.config['drug_encoding']
+    target_encoding = model.config['target_encoding']
+    
+    print(f"      Encoding {len(drugs)} drugs with {drug_encoding}...")
+    print(f"      Encoding {len(targets)} targets with {target_encoding}...")
+    
+    # data_process returns DataFrames with encoded columns
+    train_df, val_df, test_df = utils.data_process(
+        drugs, targets, labels, 
+        drug_encoding, target_encoding, 
+        split_method='random', 
+        frac=[0.01, 0.01, 0.98],  # Mostly test
+        random_seed=42
+    )
+    
+    # Combine all DataFrames
+    all_df = pd.concat([train_df, val_df, test_df], ignore_index=True)
+    print(f"      Combined {len(all_df)} samples")
+    
+    # Create proper Dataset using DeepPurpose's loader
+    from DeepPurpose.utils import data_process_loader
+    
+    config = {
+        'drug_encoding': drug_encoding,
+        'target_encoding': target_encoding
+    }
+    
+    # Create loader - pass indices, labels, and the dataframe with config
+    indices = list(range(len(all_df)))
+    label_values = all_df['Label'].values
+    dataset = data_process_loader(indices, label_values, all_df, **config)
+    
+    # Create DataLoader WITHOUT shuffling - CRITICAL!
+    loader = torch.utils.data.DataLoader(
+        dataset, 
+        batch_size=64, 
+        shuffle=False,
+        drop_last=False
+    )
+    
+    device = next(model.model.parameters()).device
+    print(f"      Running inference on {device}... ({len(dataset)} samples)")
+    
+    all_drug_emb = []
+    all_target_emb = []
+    all_labels = []
+    
+    with torch.no_grad():
+        for v_d, v_p, label in loader:
+            v_d = v_d.to(device)
+            v_p = v_p.to(device)
+            
+            emb_drug = model.model.model_drug(v_d).cpu().numpy()
+            emb_target = model.model.model_protein(v_p).cpu().numpy()
+            
+            all_drug_emb.append(emb_drug)
+            all_target_emb.append(emb_target)
+            all_labels.extend(label.numpy().tolist())
+    
+    drug_embeddings = np.vstack(all_drug_emb)
+    target_embeddings = np.vstack(all_target_emb)
+    
+    print(f"      Drug embeddings: {drug_embeddings.shape}")
+    print(f"      Target embeddings: {target_embeddings.shape}")
+    
+    return drug_embeddings, target_embeddings, np.array(all_labels)
+
+def compute_3d_projections(drug_emb: np.ndarray, target_emb: np.ndarray):
+    """
+    Compute REAL PCA projections for 3D visualization.
+    """
+    print("[4/6] Computing PCA projections...")
+    
+    combined = np.hstack([drug_emb, target_emb])
+    
+    pca = PCA(n_components=3)
+    combined_3d = pca.fit_transform(combined)
+    
+    pca_drug = PCA(n_components=3)
+    drug_3d = pca_drug.fit_transform(drug_emb)
+    
+    pca_target = PCA(n_components=3)
+    target_3d = pca_target.fit_transform(target_emb)
+    
+    print(f"      Explained variance (combined): {pca.explained_variance_ratio_.sum():.2%}")
+    print(f"      Explained variance (drug): {pca_drug.explained_variance_ratio_.sum():.2%}")
+    print(f"      Explained variance (target): {pca_target.explained_variance_ratio_.sum():.2%}")
+    
+    return drug_3d, target_3d, combined_3d
+
+def upload_to_qdrant(
+    df: pd.DataFrame,
+    drug_emb: np.ndarray,
+    target_emb: np.ndarray,
+    drug_3d: np.ndarray,
+    target_3d: np.ndarray,
+    combined_3d: np.ndarray,
+    labels: np.ndarray
+):
+    """Upload vectors and metadata to Qdrant."""
+    print(f"[5/6] Connecting to Qdrant ({QDRANT_HOST}:{QDRANT_PORT})...")
+    
+    try:
+        client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT, timeout=10)
+        client.get_collections()
+    except Exception as e:
+        print(f"[ERROR] Cannot connect to Qdrant: {e}")
+        print("        Make sure Qdrant is running:")
+        print("        docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant")
+        return False
+    
+    drug_dim = drug_emb.shape[1]
+    target_dim = target_emb.shape[1]
+    
+    existing = [c.name for c in client.get_collections().collections]
+    if COLLECTION_NAME in existing:
+        print(f"      Collection '{COLLECTION_NAME}' exists. Recreating...")
+    
+    client.recreate_collection(
+        collection_name=COLLECTION_NAME,
+        vectors_config={
+            "drug": VectorParams(size=drug_dim, distance=Distance.COSINE),
+            "target": VectorParams(size=target_dim, distance=Distance.COSINE),
+        }
+    )
+    print(f"      Collection created: drug={drug_dim}d, target={target_dim}d")
+    
+    print("[6/6] Uploading points...")
+    
+    drugs = df['Drug'].values
+    targets = df['Target'].values
+    
+    points = []
+    for i in range(len(df)):
+        payload = {
+            "smiles": str(drugs[i]),
+            "target_seq": str(targets[i])[:500],
+            "label_true": float(labels[i]),
+            "pca_drug": drug_3d[i].tolist(),
+            "pca_target": target_3d[i].tolist(),
+            "pca_combined": combined_3d[i].tolist(),
+            "affinity_class": "high" if labels[i] > 7 else "medium" if labels[i] > 5 else "low",
+        }
+        
+        point = PointStruct(
+            id=i,
+            vector={
+                "drug": drug_emb[i].tolist(),
+                "target": target_emb[i].tolist()
+            },
+            payload=payload
+        )
+        points.append(point)
+        
+        if len(points) >= 100:
+            client.upsert(collection_name=COLLECTION_NAME, points=points)
+            points = []
+            print(f"      Uploaded {i+1}/{len(df)}", end='\r')
+    
+    if points:
+        client.upsert(collection_name=COLLECTION_NAME, points=points)
+    
+    print(f"\n      ✓ Successfully indexed {len(df)} points!")
+    return True
+
+def main():
+    print("=" * 60)
+    print("   DEEPPURPOSE -> QDRANT INGESTION PIPELINE (v2)")
+    print("=" * 60)
+    
+    model = load_model(BEST_MODEL_RUN)
+    if not model:
+        sys.exit(1)
+    
+    # Load KIBA dataset from TDC (the model was trained on KIBA)
+    df = load_data("KIBA")
+    if df is None:
+        sys.exit(1)
+    
+    drug_emb, target_emb, labels = generate_embeddings(model, df)
+    drug_3d, target_3d, combined_3d = compute_3d_projections(drug_emb, target_emb)
+    success = upload_to_qdrant(df, drug_emb, target_emb, drug_3d, target_3d, combined_3d, labels)
+    
+    if success:
+        print("\n" + "=" * 60)
+        print("   INGESTION COMPLETE")
+        print("=" * 60)
+        print(f"   Collection: {COLLECTION_NAME}")
+        print(f"   Points: {len(df)}")
+        print(f"   Next: python -m uvicorn server.api:app --reload")
+    else:
+        print("\n[FAILED] Check errors above.")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

old/predictions_test.csv

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-+--------+---------------------+--------------+-------------------+
-|  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.4734 |        0.5386       |    0.0000    |       0.7885      |
-+--------+---------------------+--------------+-------------------+

runs/20260124_212915_DAVIS/valid_markdowntable.txt

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-+---------+--------+---------------------+--------------+-------------------+
-| # epoch |  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.5978 |        0.4531       |    0.0000    |       0.7311      |
-| epoch 1 | 0.5074 |        0.5377       |    0.0000    |       0.7823      |
-| epoch 2 | 0.4985 |        0.5556       |    0.0000    |       0.7877      |
-| epoch 3 | 0.4908 |        0.5612       |    0.0000    |       0.7901      |
-| epoch 4 | 0.4868 |        0.5670       |    0.0000    |       0.7932      |
-| epoch 5 | 0.4889 |        0.5699       |    0.0000    |       0.7948      |
-| epoch 6 | 0.4791 |        0.5761       |    0.0000    |       0.7981      |
-| epoch 7 | 0.4769 |        0.5801       |    0.0000    |       0.8019      |
-| epoch 8 | 0.4714 |        0.5875       |    0.0000    |       0.8053      |
-| epoch 9 | 0.4764 |        0.5889       |    0.0000    |       0.8081      |
-+---------+--------+---------------------+--------------+-------------------+

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-{
-  "run_id": "20260124_224109_KIBA",
-  "dataset": "KIBA",
-  "drug_encoding": "Morgan",
-  "target_encoding": "CNN",
-  "epochs": 10,
-  "batch": 256,
-  "lr": 0.0001,
-  "seed": 1,
-  "label_transform": "paffinity_nm",
-  "harmonize_bindingdb": "mean",
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-  "files": {
-    "predictions_test_csv": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\predictions_test.csv",
-    "scatter_png": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\scatter.png",
-    "curves_sorted_png": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\curves_sorted.png",
-    "residuals_png": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\residuals.png",
-    "hist_true_pred_png": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\hist_true_pred.png",
-    "ecdf_true_pred_png": "c:\\Users\\hamza\\Downloads\\Free Spitfire 3D printed RC plane - 2118624\\files\\runs\\20260124_224109_KIBA\\ecdf_true_pred.png"
-  },
-  "timing": {
-    "total_seconds": 33780.70901584625,
-    "train_seconds": 33649.42254161835
-  }
-}

runs/20260124_224109_KIBA/test_markdowntable.txt

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-+--------+---------------------+--------------+-------------------+
-|  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.0008 |        0.4817       |    0.0000    |       0.6847      |
-+--------+---------------------+--------------+-------------------+

runs/20260124_224109_KIBA/valid_markdowntable.txt

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-+---------+--------+---------------------+--------------+-------------------+
-| # epoch |  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.0046 |       -0.0476       |    0.0000    |       0.4710      |
-| epoch 1 | 0.0025 |        0.1240       |    0.0000    |       0.5352      |
-| epoch 2 | 0.0052 |        0.2611       |    0.0000    |       0.5879      |
-| epoch 3 | 0.0030 |        0.3712       |    0.0000    |       0.6374      |
-| epoch 4 | 0.0009 |        0.4274       |    0.0000    |       0.6673      |
-| epoch 5 | 0.0038 |        0.4554       |    0.0000    |       0.6727      |
-| epoch 6 | 0.0008 |        0.4896       |    0.0000    |       0.6897      |
-| epoch 7 | 0.0010 |        0.5113       |    0.0000    |       0.7005      |
-| epoch 8 | 0.0112 |        0.5019       |    0.0000    |       0.6889      |
-| epoch 9 | 0.0012 |        0.5242       |    0.0000    |       0.7023      |
-+---------+--------+---------------------+--------------+-------------------+

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runs/20260126_153020_DAVIS/test_markdowntable.txt

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-+---------------+---------------------+--------------+-------------------+
-|      MSE      | Pearson Correlation | with p-value | Concordance Index |
-+---------------+---------------------+--------------+-------------------+
-| 73974003.5008 |       -0.0242       |    0.2194    |       0.4867      |
-+---------------+---------------------+--------------+-------------------+

runs/20260126_153020_DAVIS/valid_markdowntable.txt

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-+---------+---------------+---------------------+--------------+-------------------+
-| # epoch |      MSE      | Pearson Correlation | with p-value | Concordance Index |
-+---------+---------------+---------------------+--------------+-------------------+
-| epoch 0 | 11222237.5076 |        0.5493       |    0.0000    |       0.7836      |
-| epoch 1 | 11268550.4673 |        0.5596       |    0.0000    |       0.7869      |
-| epoch 2 | 10935933.6634 |        0.5698       |    0.0000    |       0.7924      |
-| epoch 3 | 10767720.9830 |        0.5822       |    0.0000    |       0.7985      |
-| epoch 4 | 10760275.3041 |        0.5891       |    0.0000    |       0.8025      |
-| epoch 5 | 10299218.9160 |        0.6010       |    0.0000    |       0.8093      |
-| epoch 6 | 10213825.8110 |        0.6086       |    0.0000    |       0.8132      |
-| epoch 7 | 10005538.3511 |        0.6172       |    0.0000    |       0.8199      |
-| epoch 8 |  9869431.4575 |        0.6207       |    0.0000    |       0.8219      |
-| epoch 9 |  9812799.9596 |        0.6245       |    0.0000    |       0.8244      |
-+---------+---------------+---------------------+--------------+-------------------+

runs/20260126_154351_DAVIS/Morgan_CNN_logits.npy

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runs/20260126_154351_DAVIS/loss_curve_iter.pkl

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runs/20260126_154351_DAVIS/run_summary.json

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-{
-  "run_id": "20260126_154351_DAVIS",
-  "dataset": "DAVIS",
-  "drug_encoding": "Morgan",
-  "target_encoding": "CNN",
-  "epochs": 10,
-  "batch": 256,
-  "lr": 0.0001,
-  "seed": 1,
-  "split_method": "random",
-  "frac": [
-    0.8,
-    0.1,
-    0.1
-  ],
-  "label_transform": "none",
-  "harmonize": "none",
-  "n_rows_after_clean": 25772,
-  "cuda_id": 0,
-  "gpu_used": true,
-  "gpu_name": "NVIDIA GeForce RTX 3070 Laptop GPU",
-  "metrics_test": {
-    "mse": 73974003.50081368,
-    "rmse": 8600.814118489812,
-    "mae": 7643.865212462178,
-    "pearson": -0.024202168836860823,
-    "spearman": -0.02411776290508354,
-    "ci_approx": 0.4922038187527615
-  },
-  "files": {
-    "predictions_test_csv": "runs\\20260126_154351_DAVIS\\predictions_test.csv"
-  },
-  "timing": {
-    "load_seconds": 545.3709628582001,
-    "train_seconds": 537.8021245002747
-  }
-}

runs/20260126_154351_DAVIS/test_markdowntable.txt

@@ -1,5 +0,0 @@
-+---------------+---------------------+--------------+-------------------+
-|      MSE      | Pearson Correlation | with p-value | Concordance Index |
-+---------------+---------------------+--------------+-------------------+
-| 73974003.5008 |       -0.0242       |    0.2194    |       0.4867      |
-+---------------+---------------------+--------------+-------------------+

runs/20260126_154351_DAVIS/valid_markdowntable.txt

@@ -1,14 +0,0 @@
-+---------+---------------+---------------------+--------------+-------------------+
-| # epoch |      MSE      | Pearson Correlation | with p-value | Concordance Index |
-+---------+---------------+---------------------+--------------+-------------------+
-| epoch 0 | 18396109.4621 |       -0.0600       |    0.0023    |       0.4606      |
-| epoch 1 | 12896225.6144 |        0.4845       |    0.0000    |       0.7501      |
-| epoch 2 | 11418881.9174 |        0.5436       |    0.0000    |       0.7804      |
-| epoch 3 | 11164989.3404 |        0.5549       |    0.0000    |       0.7856      |
-| epoch 4 | 11044041.3782 |        0.5587       |    0.0000    |       0.7876      |
-| epoch 5 | 11052461.8699 |        0.5608       |    0.0000    |       0.7881      |
-| epoch 6 | 10984461.8219 |        0.5624       |    0.0000    |       0.7888      |
-| epoch 7 | 10958531.1202 |        0.5636       |    0.0000    |       0.7897      |
-| epoch 8 | 10944600.7693 |        0.5651       |    0.0000    |       0.7898      |
-| epoch 9 | 10927414.8391 |        0.5653       |    0.0000    |       0.7896      |
-+---------+---------------+---------------------+--------------+-------------------+

runs/20260126_161739_DAVIS/Morgan_CNN_logits.npy

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runs/20260126_161739_DAVIS/loss_curve_iter.pkl

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runs/20260126_161739_DAVIS/predictions_test.csv

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runs/20260126_161739_DAVIS/run_summary.json

@@ -1,37 +0,0 @@
-{
-  "run_id": "20260126_161739_DAVIS",
-  "dataset": "DAVIS",
-  "drug_encoding": "Morgan",
-  "target_encoding": "CNN",
-  "epochs": 10,
-  "batch": 256,
-  "lr": 0.0001,
-  "seed": 1,
-  "split_method": "random",
-  "frac": [
-    0.8,
-    0.1,
-    0.1
-  ],
-  "label_transform": "paffinity_nm",
-  "harmonize": "none",
-  "n_rows_after_clean": 25772,
-  "cuda_id": 0,
-  "gpu_used": true,
-  "gpu_name": "NVIDIA GeForce RTX 3070 Laptop GPU",
-  "metrics_test": {
-    "mse": 0.46840563033228455,
-    "rmse": 0.6844016586276546,
-    "mae": 0.4135942073549742,
-    "pearson": 0.544594927541983,
-    "spearman": 0.5194291749084063,
-    "ci_approx": 0.7863682932361785
-  },
-  "files": {
-    "predictions_test_csv": "runs\\20260126_161739_DAVIS\\predictions_test.csv"
-  },
-  "timing": {
-    "load_seconds": 568.5627524852753,
-    "train_seconds": 560.3927385807037
-  }
-}

runs/20260126_161739_DAVIS/test_markdowntable.txt

@@ -1,5 +0,0 @@
-+--------+---------------------+--------------+-------------------+
-|  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+--------+---------------------+--------------+-------------------+
-| 0.4684 |        0.5446       |    0.0000    |       0.7914      |
-+--------+---------------------+--------------+-------------------+

runs/20260126_161739_DAVIS/valid_markdowntable.txt

@@ -1,14 +0,0 @@
-+---------+--------+---------------------+--------------+-------------------+
-| # epoch |  MSE   | Pearson Correlation | with p-value | Concordance Index |
-+---------+--------+---------------------+--------------+-------------------+
-| epoch 0 | 0.5955 |        0.4549       |    0.0000    |       0.7321      |
-| epoch 1 | 0.5085 |        0.5353       |    0.0000    |       0.7816      |
-| epoch 2 | 0.4964 |        0.5558       |    0.0000    |       0.7882      |
-| epoch 3 | 0.4866 |        0.5639       |    0.0000    |       0.7920      |
-| epoch 4 | 0.4834 |        0.5651       |    0.0000    |       0.7921      |
-| epoch 5 | 0.4785 |        0.5731       |    0.0000    |       0.7970      |
-| epoch 6 | 0.4875 |        0.5734       |    0.0000    |       0.7975      |
-| epoch 7 | 0.4730 |        0.5797       |    0.0000    |       0.8017      |
-| epoch 8 | 0.4690 |        0.5853       |    0.0000    |       0.8044      |
-| epoch 9 | 0.4651 |        0.5885       |    0.0000    |       0.8074      |
-+---------+--------+---------------------+--------------+-------------------+