version 0.1.0 using davis

Davis-Final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c2c4f602839b78e253e22a312765691f3387dcbf4a478553d958c717d866c1b
+size 67932195

app.py

@@ -1,33 +1,146 @@
 import gradio as gr
-import random
+import torch
+import os
+from transformers import AutoTokenizer
+from models import AffinityPredictor
 
+# Global variables for model and tokenizers
+model = None
+molecule_tokenizer = None
+protein_tokenizer = None
+device = None
+
+def load_model():
+    """Load the trained model and tokenizers"""
+    global model, molecule_tokenizer, protein_tokenizer, device
+    
+    # Set device
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    
+    # Load tokenizers
+    molecule_tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-77M-MLM")
+    protein_tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
+    
+    # Initialize model with same configuration as training
+    model = AffinityPredictor(
+        protein_model_name="facebook/esm2_t6_8M_UR50D",
+        molecule_model_name="DeepChem/ChemBERTa-77M-MLM",
+        hidden_sizes=[1024, 768, 512, 256, 1],
+        inception_out_channels=256,
+        dropout=0.05
+    )
+    
+    # Load the trained weights
+    model_path = "Davis-Final.pth"
+    if os.path.exists(model_path):
+        checkpoint = torch.load(model_path, map_location=device)
+        # Handle different checkpoint formats
+        if 'model_state_dict' in checkpoint:
+            model.load_state_dict(checkpoint['model_state_dict'])
+        elif 'state_dict' in checkpoint:
+            model.load_state_dict(checkpoint['state_dict'])
+        else:
+            model.load_state_dict(checkpoint)
+        print("Model loaded successfully!")
+    else:
+        print(f"Warning: Model file {model_path} not found. Using randomly initialized weights.")
+    
+    model.to(device)
+    model.eval()
+    
+    return True
 
-# Placeholder prediction function
 def predict_affinity(smiles, sequence):
-    # In practice, you'd call your ML model here
-    # For now, we just return a random score between 0 and 1
-    score = round(random.uniform(0, 1), 4)
-    return f"Predicted Affinity Score: {score}"
+    """Predict drug-target affinity using the trained model"""
+    global model, molecule_tokenizer, protein_tokenizer, device
+    
+    # Load model if not already loaded
+    if model is None:
+        try:
+            load_model()
+        except Exception as e:
+            return f"Error loading model: {str(e)}"
+    
+    # Validate inputs
+    if not smiles or not smiles.strip():
+        return "Error: Please enter a valid SMILES string"
+    
+    if not sequence or not sequence.strip():
+        return "Error: Please enter a valid protein sequence"
+    
+    try:
+        model.eval()
+        
+        # Tokenize inputs
+        molecule_encoding = molecule_tokenizer(
+            [smiles.strip()],
+            padding="max_length",
+            truncation=True,
+            max_length=128,
+            return_tensors="pt"
+        )
+        
+        protein_encoding = protein_tokenizer(
+            [sequence.strip()],
+            padding="max_length",
+            truncation=True,
+            max_length=1024,
+            return_tensors="pt"
+        )
+        
+        # Create batch dictionary
+        batch = {
+            "molecule_input_ids": molecule_encoding.input_ids.to(device),
+            "molecule_attention_mask": molecule_encoding.attention_mask.to(device),
+            "protein_input_ids": protein_encoding.input_ids.to(device),
+            "protein_attention_mask": protein_encoding.attention_mask.to(device)
+        }
+        
+        # Make prediction
+        with torch.no_grad():
+            prediction = model(batch)
+            affinity_score = prediction.cpu().item()
+        
+        return f"Predicted Affinity Score: {affinity_score:.4f}"
+        
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
 
+# Load model on startup
+print("Loading model...")
+try:
+    load_model()
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Warning: Could not load model on startup: {e}")
 
 with gr.Blocks(title="Molecule-Protein Affinity Predictor") as demo:
     gr.Markdown("## Molecule–Protein Affinity Prediction")
     gr.Markdown(
         "Enter a **Molecule SMILES string** and a **Protein amino acid sequence** "
-        "then click **Predict** to get the affinity score."
+        "then click **Predict** to get the affinity score using the StructureFree-DTA model."
+    )
+    
+    gr.Markdown(
+        "### Example inputs:\n"
+        "**SMILES:** `CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N`\n"
+        "**Protein:** `MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL`"
     )
 
     with gr.Row():
         smiles_input = gr.Textbox(
             label="Molecule SMILES",
-            placeholder="e.g. CC(=O)OC1=CC=CC=C1C(=O)O"
+            placeholder="e.g. CC(=O)OC1=CC=CC=C1C(=O)O",
+            lines=2
         )
         sequence_input = gr.Textbox(
             label="Protein Sequence",
-            placeholder="e.g. MVLSPADKTNVKAA..."
+            placeholder="e.g. MVLSPADKTNVKAA...",
+            lines=5
         )
 
-    predict_button = gr.Button("Predict")
+    predict_button = gr.Button("Predict", variant="primary")
     output = gr.Textbox(label="Affinity Score", interactive=False)
 
     predict_button.click(
@@ -36,4 +149,5 @@ with gr.Blocks(title="Molecule-Protein Affinity Predictor") as demo:
         outputs=output
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1 +1,6 @@
 gradio
+torch
+transformers
+scikit-learn
+pandas
+numpy