Update app.py

app.py

@@ -172,99 +172,99 @@ def run_inference(mode: str, model_name: str, num_molecules: int, seed_num: str,
 
 with gr.Blocks(theme=gr.themes.Ocean()) as demo:
     
-    with gr.Column(scale=1):
-        # Add custom CSS for styling
-        gr.HTML("""
-        <style>
-        #metrics-container {
-            border: 1px solid rgba(128, 128, 128, 0.3);
-            border-radius: 8px;
-            padding: 15px;
-            margin-top: 15px;
-            margin-bottom: 15px;
-            background-color: rgba(255, 255, 255, 0.05);
-        }
-        </style>
-        """)
-    
-        gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
-        
-        gr.HTML("""
-        <div style="display: flex; gap: 10px; margin-bottom: 15px;">
-            <!-- arXiv badge -->
-            <a href="https://arxiv.org/abs/2302.07868" target="_blank" style="text-decoration: none;">
-                <div style="
-                    display: inline-block; 
-                    background-color: #b31b1b; 
-                    color: #ffffff !important; 
-                    padding: 5px 10px; 
-                    border-radius: 5px; 
-                    font-size: 14px;">
-                    <span style="font-weight: bold;">arXiv</span> 2302.07868
-                </div>
-            </a>
-            
-            <!-- GitHub badge -->
-            <a href="https://github.com/HUBioDataLab/DrugGEN" target="_blank" style="text-decoration: none;">
-                <div style="
-                    display: inline-block; 
-                    background-color: #24292e; 
-                    color: #ffffff !important; 
-                    padding: 5px 10px; 
-                    border-radius: 5px; 
-                    font-size: 14px;">
-                    <span style="font-weight: bold;">GitHub</span> Repository
-                </div>
-            </a>
-        </div>
-        """)
-    
-        with gr.Accordion("About DrugGEN Models", open=False):
-            gr.Markdown("""
-                ## Model Variations
-                
-                ### DrugGEN-AKT1
-                This model is designed to generate molecules targeting the human AKT1 protein (UniProt ID: P31749).
-                
-                ### DrugGEN-CDK2
-                This model is designed to generate molecules targeting the human CDK2 protein (UniProt ID: P24941).
-                
-                ### DrugGEN-NoTarget
-                This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein.
-                - Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
-                
-                For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
-            """)
+    # Add custom CSS for styling
+    gr.HTML("""
+    <style>
+    #metrics-container {
+        border: 1px solid rgba(128, 128, 128, 0.3);
+        border-radius: 8px;
+        padding: 15px;
+        margin-top: 15px;
+        margin-bottom: 15px;
+        background-color: rgba(255, 255, 255, 0.05);
+    }
+    </style>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
         
-        with gr.Accordion("Understanding the Metrics", open=False):
-            gr.Markdown("""
-                ## Evaluation Metrics
+            gr.HTML("""
+            <div style="display: flex; gap: 10px; margin-bottom: 15px;">
+                <!-- arXiv badge -->
+                <a href="https://arxiv.org/abs/2302.07868" target="_blank" style="text-decoration: none;">
+                    <div style="
+                        display: inline-block; 
+                        background-color: #b31b1b; 
+                        color: #ffffff !important; 
+                        padding: 5px 10px; 
+                        border-radius: 5px; 
+                        font-size: 14px;">
+                        <span style="font-weight: bold;">arXiv</span> 2302.07868
+                    </div>
+                </a>
                 
-                ### Basic Metrics
-                - **Validity**: Percentage of generated molecules that are chemically valid
-                - **Uniqueness**: Percentage of unique molecules among valid ones
-                - **Runtime**: Time taken to generate or evaluate the molecules
-                
-                ### Novelty Metrics
-                - **Novelty (Train)**: Percentage of molecules not found in the training set
-                - **Novelty (Inference)**: Percentage of molecules not found in the test set
-                - **Novelty (Real Inhibitors)**: Percentage of molecules not found in known inhibitors of the target protein
-                
-                ### Structural Metrics
-                - **Average Length**: Average component length in the generated molecules
-                - **Mean Atom Type**: Average distribution of atom types
-                - **Internal Diversity**: Diversity within the generated set (higher is more diverse)
-                
-                ### Drug-likeness Metrics
-                - **QED (Quantitative Estimate of Drug-likeness)**: Score from 0-1 measuring how drug-like a molecule is (higher is better)
-                - **SA Score (Synthetic Accessibility)**: Score from 1-10 indicating ease of synthesis (lower is better)
-                
-                ### Similarity Metrics
-                - **SNN ChEMBL**: Similarity to ChEMBL molecules (higher means more similar to known drug-like compounds)
-                - **SNN Real Inhibitors**: Similarity to known drugs (higher means more similar to approved drugs)
+                <!-- GitHub badge -->
+                <a href="https://github.com/HUBioDataLab/DrugGEN" target="_blank" style="text-decoration: none;">
+                    <div style="
+                        display: inline-block; 
+                        background-color: #24292e; 
+                        color: #ffffff !important; 
+                        padding: 5px 10px; 
+                        border-radius: 5px; 
+                        font-size: 14px;">
+                        <span style="font-weight: bold;">GitHub</span> Repository
+                    </div>
+                </a>
+            </div>
             """)
         
-        with gr.Row():
+            with gr.Accordion("About DrugGEN Models", open=False):
+                gr.Markdown("""
+                    ## Model Variations
+                    
+                    ### DrugGEN-AKT1
+                    This model is designed to generate molecules targeting the human AKT1 protein (UniProt ID: P31749).
+                    
+                    ### DrugGEN-CDK2
+                    This model is designed to generate molecules targeting the human CDK2 protein (UniProt ID: P24941).
+                    
+                    ### DrugGEN-NoTarget
+                    This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein.
+                    - Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
+                    
+                    For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
+                """)
+            
+            with gr.Accordion("Understanding the Metrics", open=False):
+                gr.Markdown("""
+                    ## Evaluation Metrics
+                    
+                    ### Basic Metrics
+                    - **Validity**: Percentage of generated molecules that are chemically valid
+                    - **Uniqueness**: Percentage of unique molecules among valid ones
+                    - **Runtime**: Time taken to generate or evaluate the molecules
+                    
+                    ### Novelty Metrics
+                    - **Novelty (Train)**: Percentage of molecules not found in the training set
+                    - **Novelty (Inference)**: Percentage of molecules not found in the test set
+                    - **Novelty (Real Inhibitors)**: Percentage of molecules not found in known inhibitors of the target protein
+                    
+                    ### Structural Metrics
+                    - **Average Length**: Average component length in the generated molecules
+                    - **Mean Atom Type**: Average distribution of atom types
+                    - **Internal Diversity**: Diversity within the generated set (higher is more diverse)
+                    
+                    ### Drug-likeness Metrics
+                    - **QED (Quantitative Estimate of Drug-likeness)**: Score from 0-1 measuring how drug-like a molecule is (higher is better)
+                    - **SA Score (Synthetic Accessibility)**: Score from 1-10 indicating ease of synthesis (lower is better)
+                    
+                    ### Similarity Metrics
+                    - **SNN ChEMBL**: Similarity to ChEMBL molecules (higher means more similar to known drug-like compounds)
+                    - **SNN Real Inhibitors**: Similarity to known drugs (higher means more similar to approved drugs)
+                """)
+            
             model_name = gr.Radio(
                 choices=("DrugGEN-AKT1", "DrugGEN-CDK2", "DrugGEN-NoTarget"),
                 value="DrugGEN-AKT1",
@@ -272,62 +272,60 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
                 info="Choose which protein target or general model to use for molecule generation"
             )
                 
-        # Use Gradio Tabs to separate the two modes.
-        with gr.Tabs():
-            with gr.TabItem("Classical Generation"):
-                    num_molecules = gr.Slider(
-                        minimum=10,
-                        maximum=200,
-                        value=100,
-                        step=10,
-                        label="Number of Molecules to Generate",
-                        info="This space runs on a CPU, which may result in slower performance. Generating 100 molecules takes approximately 6 minutes. Therefore, we set a 200-molecule cap."
-                    )
-    
-                    seed_num = gr.Textbox(
-                        label="Random Seed (Optional)",
-                        value="",
-                        info="Set a specific seed for reproducible results, or leave empty for random generation"
-                    )
-    
-                    classical_submit = gr.Button(
-                        value="Generate Molecules",
-                        variant="primary",
-                        size="lg"
-                    )
+            with gr.Tabs():
+                with gr.TabItem("Classical Generation"):
+                        num_molecules = gr.Slider(
+                            minimum=10,
+                            maximum=200,
+                            value=100,
+                            step=10,
+                            label="Number of Molecules to Generate",
+                            info="This space runs on a CPU, which may result in slower performance. Generating 100 molecules takes approximately 6 minutes. Therefore, we set a 200-molecule cap."
+                        )
+        
+                        seed_num = gr.Textbox(
+                            label="Random Seed (Optional)",
+                            value="",
+                            info="Set a specific seed for reproducible results, or leave empty for random generation"
+                        )
+        
+                        classical_submit = gr.Button(
+                            value="Generate Molecules",
+                            variant="primary",
+                            size="lg"
+                        )
+        
+                with gr.TabItem("Custom Input SMILES"):
+                        custom_smiles = gr.Textbox(
+                            label="Input SMILES (one per line, maximum 100 molecules)",
+                            placeholder="C(C(=O)O)N\nCCO\n...",
+                            lines=10
+                        )
+                        
+                        custom_submit = gr.Button(
+                            value="Generate Molecules using Custom SMILES",
+                            variant="primary",
+                            size="lg"
+                        )
+
+        with gr.Column(scale=2):
+            basic_metrics_df = gr.Dataframe(
+                headers=["Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)", "Novelty (Real Inhibitors)", "Runtime (s)"],
+                elem_id="basic-metrics"
+            )
+                            
+            advanced_metrics_df = gr.Dataframe(
+                headers=["QED", "SA Score", "Internal Diversity", "SNN (ChEMBL)", "SNN (Real Inhibitors)", "Average Length"],
+                elem_id="advanced-metrics"
+            )
     
-            with gr.TabItem("Custom Input SMILES"):
-                    custom_smiles = gr.Textbox(
-                        label="Input SMILES (one per line, maximum 100 molecules)",
-                        placeholder="C(C(=O)O)N\nCCO\n...",
-                        lines=10
-                    )
-                    
-                    custom_submit = gr.Button(
-                        value="Generate Molecules using Custom SMILES",
-                        variant="primary",
-                        size="lg"
-                    )
-
-        gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
-
-    with gr.Column(scale=2):
-        basic_metrics_df = gr.Dataframe(
-            headers=["Validity", "Uniqueness", "Novelty (Train)", "Novelty (Inference)", "Novelty (Real Inhibitors)", "Runtime (s)"],
-            elem_id="basic-metrics"
-        )
+            file_download = gr.File(label="Download All Generated Molecules (SMILES format)")
                         
-        advanced_metrics_df = gr.Dataframe(
-            headers=["QED", "SA Score", "Internal Diversity", "SNN (ChEMBL)", "SNN (Real Inhibitors)", "Average Length"],
-            elem_id="advanced-metrics"
-        )
-
-        file_download = gr.File(label="Download All Generated Molecules (SMILES format)")
-                    
-        image_output = gr.Image(label="Structures of Randomly Selected Generated Molecules",
-                        elem_id="molecule_display")
+            image_output = gr.Image(label="Structures of Randomly Selected Generated Molecules",
+                            elem_id="molecule_display")
 
 
+    gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
 
     # Set up the click actions for each tab.
     classical_submit.click(