Create app.py

app.py

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+# --- IMPORTS ---
+# Core and Data Handling
+import gradio as gr #
+import pandas as pd #
+import numpy as np #
+import os #
+import glob #
+import time #
+import warnings #
+
+# Chemistry and Cheminformatics
+from rdkit import Chem #
+from rdkit.Chem import Descriptors, Lipinski #
+from chembl_webresource_client.new_client import new_client #
+from padelpy import padeldescriptor #
+import mols2grid #
+
+# Plotting and Visualization
+import matplotlib.pyplot as plt #
+import seaborn as sns #
+from scipy import stats #
+from scipy.stats import mannwhitneyu #
+
+# Machine Learning Models and Metrics
+from sklearn.model_selection import train_test_split #
+from sklearn.feature_selection import VarianceThreshold #
+from sklearn.linear_model import ( #
+    LinearRegression, Ridge, Lasso, ElasticNet, BayesianRidge, #
+    HuberRegressor, PassiveAggressiveRegressor, OrthogonalMatchingPursuit, #
+    LassoLars #
+)
+from sklearn.tree import DecisionTreeRegressor #
+from sklearn.ensemble import ( #
+    RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, #
+    AdaBoostRegressor #
+)
+from sklearn.neighbors import KNeighborsRegressor #
+from sklearn.dummy import DummyRegressor #
+from sklearn.metrics import ( #
+    mean_absolute_error, mean_squared_error, r2_score #
+)
+
+# A placeholder class to store all results from a modeling run
+class ModelRunResult: #
+    def __init__(self, dataframe, plotter, models, selected_features): #
+        self.dataframe = dataframe #
+        self.plotter = plotter #
+        self.models = models #
+        self.selected_features = selected_features #
+
+# Optional Advanced Models
+try: #
+    import xgboost as xgb #
+    import lightgbm as lgb #
+    import catboost as cb #
+    _has_extra_libs = True #
+except ImportError: #
+    _has_extra_libs = False #
+    warnings.warn("Optional libraries (xgboost, lightgbm, catboost) not found. Some models will be unavailable.") #
+
+# --- GLOBAL CONFIGURATION & SETUP ---
+warnings.filterwarnings("ignore") #
+sns.set_theme(style='whitegrid') #
+
+# --- FINGERPRINT CONFIGURATION ---
+DESCRIPTOR_DIR = "padel_descriptors"
+
+# Check if the descriptor directory exists and contains files
+if not os.path.isdir(DESCRIPTOR_DIR):
+    warnings.warn(
+        f"The descriptor directory '{DESCRIPTOR_DIR}' was not found. "
+        "Fingerprint calculation will be disabled. Please create this directory and upload your .xml files."
+    )
+    xml_files = []
+else:
+    xml_files = sorted(glob.glob(os.path.join(DESCRIPTOR_DIR, '*.xml')))
+
+if not xml_files:
+    warnings.warn(
+        f"No descriptor .xml files found in the '{DESCRIPTOR_DIR}' directory. "
+        "Fingerprint calculation will not be possible."
+    )
+
+# The key is the filename without extension; the value is the full path to the file
+fp_config = {os.path.splitext(os.path.basename(file))[0]: file for file in xml_files}
+FP_list = sorted(list(fp_config.keys()))
+
+
+# ==============================================================================
+# === STEP 1: CORE DATA COLLECTION & EDA FUNCTIONS ===
+# ==============================================================================
+
+def get_target_chembl_id(query): #
+    try: #
+        target = new_client.target #
+        res = target.search(query) #
+        if not res: #
+            return pd.DataFrame(), gr.Dropdown(choices=[], value=None), "No targets found for your query." #
+        df = pd.DataFrame(res) #
+        return df[["target_chembl_id", "pref_name", "organism"]], gr.Dropdown(choices=df["target_chembl_id"].tolist()), f"Found {len(df)} targets." #
+    except Exception as e: #
+        raise gr.Error(f"ChEMBL search failed: {e}") #
+
+def get_bioactivity_data(target_id): #
+    try: #
+        activity = new_client.activity #
+        res = activity.filter(target_chembl_id=target_id).filter(standard_type="IC50") #
+        if not res: #
+            return pd.DataFrame(), "No IC50 bioactivity data found for this target." #
+        df = pd.DataFrame(res) #
+        return df, f"Fetched {len(df)} data points." #
+    except Exception as e: #
+        raise gr.Error(f"Failed to fetch bioactivity data: {e}") #
+
+def pIC50_calc(input_df): #
+    df_copy = input_df.copy() #
+    df_copy['standard_value'] = pd.to_numeric(df_copy['standard_value'], errors='coerce') #
+    df_copy.dropna(subset=['standard_value'], inplace=True) #
+    df_copy['standard_value_norm'] = df_copy['standard_value'].apply(lambda x: min(x, 100000000)) #
+    pIC50_values = [] #
+    for i in df_copy['standard_value_norm']: #
+        if pd.notna(i) and i > 0: #
+            molar = i * (10**-9) #
+            pIC50_values.append(-np.log10(molar)) #
+        else: #
+            pIC50_values.append(np.nan) #
+    df_copy['pIC50'] = pIC50_values #
+    df_copy['bioactivity_class'] = df_copy['standard_value_norm'].apply( #
+        lambda x: "inactive" if pd.notna(x) and x >= 10000 else ("active" if pd.notna(x) and x <= 1000 else "intermediate") #
+    )
+    return df_copy.drop(columns=['standard_value', 'standard_value_norm']) #
+
+def lipinski_descriptors(smiles_series): #
+    moldata, valid_smiles = [], [] #
+    for elem in smiles_series: #
+        if elem and isinstance(elem, str): #
+            mol = Chem.MolFromSmiles(elem) #
+            if mol: #
+                moldata.append(mol) #
+                valid_smiles.append(elem) #
+    descriptor_rows = [] #
+    for mol in moldata: #
+        row = [Descriptors.MolWt(mol), Descriptors.MolLogP(mol), Lipinski.NumHDonors(mol), Lipinski.NumHAcceptors(mol)] #
+        descriptor_rows.append(row) #
+    columnNames = ["MW", "LogP", "NumHDonors", "NumHAcceptors"] #
+    if not descriptor_rows: return pd.DataFrame(columns=columnNames), [] #
+    return pd.DataFrame(data=np.array(descriptor_rows), columns=columnNames), valid_smiles #
+
+def clean_and_process_data(df): #
+    if df is None or df.empty: raise gr.Error("No data to process. Please fetch data first.") #
+    if "canonical_smiles" not in df.columns or df["canonical_smiles"].isnull().all(): #
+        try: #
+            df["canonical_smiles"] = [c.get("molecule_structures", {}).get("canonical_smiles") for c in new_client.molecule.get(list(df["molecule_chembl_id"]))] #
+        except Exception as e: #
+            raise gr.Error(f"Could not fetch SMILES from ChEMBL: {e}") #
+    df = df[df.standard_value.notna()] #
+    df = df[df.canonical_smiles.notna()] #
+    df.drop_duplicates(['canonical_smiles'], inplace=True) #
+    df["standard_value"] = pd.to_numeric(df["standard_value"], errors='coerce') #
+    df.dropna(subset=['standard_value'], inplace=True) #
+    df_processed = pIC50_calc(df) #
+    df_processed = df_processed[df_processed.pIC50.notna()] #
+    if df_processed.empty: return pd.DataFrame(), "No compounds remaining after pIC50 calculation." #
+    df_lipinski, valid_smiles = lipinski_descriptors(df_processed['canonical_smiles']) #
+    if not valid_smiles: return pd.DataFrame(), "No valid SMILES could be processed for Lipinski descriptors." #
+    df_processed = df_processed[df_processed['canonical_smiles'].isin(valid_smiles)].reset_index(drop=True) #
+    df_lipinski = df_lipinski.reset_index(drop=True) #
+    df_final = pd.concat([df_processed, df_lipinski], axis=1) #
+    return df_final, f"Processing complete. {len(df_final)} compounds remain after cleaning." #
+
+def run_eda_analysis(df, selected_classes): #
+    if df is None or df.empty: raise gr.Error("No data available for analysis.") #
+    df_filtered = df[df.bioactivity_class.isin(selected_classes)].copy() #
+    if df_filtered.empty: return (None, None, None, pd.DataFrame(), None, pd.DataFrame(), None, pd.DataFrame(), None, pd.DataFrame(), None, pd.DataFrame(), "No data for selected classes.") #
+    plots = [create_frequency_plot(df_filtered), create_scatter_plot(df_filtered)] #
+    stats_dfs = [] #
+    for desc in ['pIC50', 'MW', 'LogP', 'NumHDonors', 'NumHAcceptors']: #
+        plots.append(create_boxplot(df_filtered, desc)) #
+        stats_dfs.append(mannwhitney_test(df_filtered, desc)) #
+    plt.close('all') #
+    return (plots[0], plots[1], plots[2], stats_dfs[0], plots[3], stats_dfs[1], plots[4], stats_dfs[2], plots[5], stats_dfs[3], plots[6], stats_dfs[4], f"EDA complete for {len(df_filtered)} compounds.") #
+
+def create_frequency_plot(df): #
+    plt.figure(figsize=(5.5, 5.5)); sns.barplot(x=df['bioactivity_class'].value_counts().index, y=df['bioactivity_class'].value_counts().values, palette={'active': '#1f77b4', 'inactive': '#ff7f0e', 'intermediate': '#2ca02c'}); plt.xlabel('Bioactivity Class', fontsize=12); plt.ylabel('Frequency', fontsize=12); plt.title('Frequency of Bioactivity Classes', fontsize=14); return plt.gcf() #
+def create_scatter_plot(df): #
+    plt.figure(figsize=(5.5, 5.5)); sns.scatterplot(data=df, x='MW', y='LogP', hue='bioactivity_class', size='pIC50', palette={'active': '#1f77b4', 'inactive': '#ff7f0e', 'intermediate': '#2ca02c'}, sizes=(20, 200), alpha=0.7); plt.xlabel('Molecular Weight (MW)', fontsize=12); plt.ylabel('LogP', fontsize=12); plt.title('Chemical Space: MW vs. LogP', fontsize=14); plt.legend(title='Bioactivity Class'); return plt.gcf() #
+def create_boxplot(df, descriptor): #
+    plt.figure(figsize=(5.5, 5.5)); sns.boxplot(x='bioactivity_class', y=descriptor, data=df, palette={'active': '#1f77b4', 'inactive': '#ff7f0e', 'intermediate': '#2ca02c'}); plt.xlabel('Bioactivity Class', fontsize=12); plt.ylabel(descriptor, fontsize=12); plt.title(f'{descriptor} by Bioactivity Class', fontsize=14); return plt.gcf() #
+def mannwhitney_test(df, descriptor): #
+    results = [] #
+    for c1, c2 in [('active', 'inactive'), ('active', 'intermediate'), ('inactive', 'intermediate')]: #
+        if c1 in df['bioactivity_class'].unique() and c2 in df['bioactivity_class'].unique(): #
+            d1, d2 = df[df.bioactivity_class == c1][descriptor].dropna(), df[df.bioactivity_class == c2][descriptor].dropna() #
+            if not d1.empty and not d2.empty: #
+                stat, p = mannwhitneyu(d1, d2) #
+                results.append({'Comparison': f'{c1.title()} vs {c2.title()}', 'Statistics': stat, 'p-value': p, 'Interpretation': 'Different distribution (p < 0.05)' if p <= 0.05 else 'Same distribution (p > 0.05)'}) #
+    return pd.DataFrame(results) #
+
+# ==============================================================================
+# === STEP 2: FEATURE ENGINEERING FUNCTIONS ===
+# ==============================================================================
+
+def calculate_fingerprints(current_state, fingerprint_type, progress=gr.Progress()): #
+    input_df = current_state.get('cleaned_data') #
+    if input_df is None or input_df.empty: raise gr.Error("No cleaned data found. Please complete Step 1.") #
+    if not fingerprint_type: raise gr.Error("Please select a fingerprint type.") #
+    progress(0, desc="Starting..."); yield f"🧪 Starting fingerprint calculation...", None, gr.update(visible=False), None, current_state #
+    try: #
+        smi_file, output_csv = 'molecule.smi', 'fingerprints.csv' #
+        
+        input_df[['canonical_smiles', 'canonical_smiles']].to_csv(smi_file, sep='\t', index=False, header=False) #
+        
+        if os.path.exists(output_csv): os.remove(output_csv) #
+        descriptortypes = fp_config.get(fingerprint_type) #
+        if not descriptortypes: raise gr.Error(f"Descriptor XML for '{fingerprint_type}' not found.") #
+        
+        progress(0.3, desc="⚗️ Running PaDEL..."); yield f"⚗️ Running PaDEL...", None, gr.update(visible=False), None, current_state #
+        padeldescriptor(mol_dir=smi_file, d_file=output_csv, descriptortypes=descriptortypes, detectaromaticity=True, standardizenitro=True, standardizetautomers=True, threads=-1, removesalt=True, log=False, fingerprints=True) #
+        if not os.path.exists(output_csv) or os.path.getsize(output_csv) == 0: #
+            raise gr.Error("PaDEL failed to produce an output file. Check molecule validity.") #
+
+        progress(0.7, desc="📊 Processing results..."); yield "📊 Processing results...", None, gr.update(visible=False), None, current_state #
+        df_X = pd.read_csv(output_csv).rename(columns={'Name': 'canonical_smiles'}) #
+        
+        final_df = pd.merge(input_df[['canonical_smiles', 'pIC50']], df_X, on='canonical_smiles', how='inner') #
+        
+        current_state['fingerprint_data'] = final_df; current_state['fingerprint_type'] = fingerprint_type #
+        progress(0.9, desc="🖼️ Generating molecule grid...") #
+        mols_html = mols2grid.display(final_df, smiles_col='canonical_smiles', subset=['img', 'pIC50'], rename={"pIC50": "pIC50"}, transform={"pIC50": lambda x: f"{x:.2f}"})._repr_html_() #
+        success_msg = f"✅ Success! Generated {len(df_X.columns) -1} descriptors for {len(final_df)} molecules." #
+        progress(1, desc="Completed!"); yield success_msg, final_df, gr.update(visible=True), gr.update(value=mols_html, visible=True), current_state #
+    except Exception as e: raise gr.Error(f"Calculation failed: {e}") #
+    finally: #
+        if os.path.exists('molecule.smi'): os.remove('molecule.smi') #
+        if os.path.exists('fingerprints.csv'): os.remove('fingerprints.csv') #
+
+# ==============================================================================
+# === STEP 3: MODEL TRAINING & PREDICTION FUNCTIONS ===
+# ==============================================================================
+class ModelPlotter: #
+    def __init__(self, models: dict, X_test: pd.DataFrame, y_test: pd.Series): #
+        self._models, self._X_test, self._y_test = models, X_test, y_test #
+    def plot_validation(self, model_name: str): #
+        if model_name not in self._models: raise ValueError(f"Model '{model_name}' not found.") #
+        model, y_pred = self._models[model_name], self._models[model_name].predict(self._X_test) #
+        residuals = self._y_test - y_pred #
+        fig, axes = plt.subplots(2, 2, figsize=(12, 10)); fig.suptitle(f'Model Validation Plots for {model_name}', fontsize=16, y=1.02) #
+        sns.scatterplot(x=self._y_test, y=y_pred, ax=axes[0, 0], alpha=0.6); axes[0, 0].set_title('Actual vs. Predicted'); axes[0, 0].set_xlabel('Actual pIC50'); axes[0, 0].set_ylabel('Predicted pIC50'); lims = [min(self._y_test.min(), y_pred.min()), max(self._y_test.max(), y_pred.max())]; axes[0, 0].plot(lims, lims, 'r--', alpha=0.75, zorder=0) #
+        sns.scatterplot(x=y_pred, y=residuals, ax=axes[0, 1], alpha=0.6); axes[0, 1].axhline(y=0, color='r', linestyle='--'); axes[0, 1].set_title('Residuals vs. Predicted'); axes[0, 1].set_xlabel('Predicted pIC50'); axes[0, 1].set_ylabel('Residuals') #
+        sns.histplot(residuals, kde=True, ax=axes[1, 0]); axes[1, 0].set_title('Distribution of Residuals') #
+        stats.probplot(residuals, dist="norm", plot=axes[1, 1]); axes[1, 1].set_title('Normal Q-Q Plot') #
+        plt.tight_layout(); return fig #
+    def plot_feature_importance(self, model_name: str, top_n: int = 7): #
+        if model_name not in self._models: raise ValueError(f"Model '{model_name}' not found.") #
+        model = self._models[model_name] #
+        if hasattr(model, 'feature_importances_'): importances = model.feature_importances_ #
+        elif hasattr(model, 'coef_'): importances = np.abs(model.coef_) #
+        else: return None #
+        top_features = pd.DataFrame({'Feature': self._X_test.columns, 'Importance': importances}).sort_values(by='Importance', ascending=False).head(top_n) #
+        plt.figure(figsize=(10, top_n * 0.5)); sns.barplot(x='Importance', y='Feature', data=top_features, palette='viridis', orient='h'); plt.title(f'Top {top_n} Features for {model_name}'); plt.tight_layout(); return plt.gcf() #
+
+def run_regression_suite(df: pd.DataFrame, progress=gr.Progress()): #
+    progress(0, desc="Splitting data..."); yield "Splitting data (80/20 train/test split)...", None, None #
+    X = df.drop(columns=['pIC50', 'canonical_smiles'], errors='ignore') #
+    y = df['pIC50'] #
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #
+
+    progress(0.1, desc="Selecting features..."); yield "Performing feature selection (removing low variance)...", None, None #
+    selector = VarianceThreshold(threshold=0.1) #
+    X_train = pd.DataFrame(selector.fit_transform(X_train), columns=X_train.columns[selector.get_support()], index=X_train.index) #
+    X_test = pd.DataFrame(selector.transform(X_test), columns=X_test.columns[selector.get_support()], index=X_test.index) #
+    selected_features = X_train.columns.tolist() #
+
+    model_defs = [('Linear Regression', LinearRegression()), ('Ridge', Ridge(random_state=42)), ('Lasso', Lasso(random_state=42)), ('Random Forest', RandomForestRegressor(random_state=42, n_jobs=-1)), ('Gradient Boosting', GradientBoostingRegressor(random_state=42))] #
+    if _has_extra_libs: model_defs.extend([('XGBoost', xgb.XGBRegressor(random_state=42, n_jobs=-1, verbosity=0)), ('LightGBM', lgb.LGBMRegressor(random_state=42, n_jobs=-1, verbosity=-1)), ('CatBoost', cb.CatBoostRegressor(random_state=42, verbose=0))]) #
+    
+    results_list, trained_models = [], {} #
+    for i, (name, model) in enumerate(model_defs): #
+        progress(0.2 + (i / len(model_defs)) * 0.8, desc=f"Training {name}...") #
+        yield f"Training {i+1}/{len(model_defs)}: {name}...", None, None #
+        start_time = time.time(); model.fit(X_train, y_train); y_pred = model.predict(X_test) #
+        results_list.append({'Model': name, 'R²': r2_score(y_test, y_pred), 'MAE': mean_absolute_error(y_test, y_pred), 'RMSE': np.sqrt(mean_squared_error(y_test, y_pred)), 'Time (s)': f"{time.time() - start_time:.2f}"}) #
+        trained_models[name] = model #
+
+    results_df = pd.DataFrame(results_list).sort_values(by='R²', ascending=False).reset_index(drop=True) #
+    plotter = ModelPlotter(trained_models, X_test, y_test) #
+    model_run_results = ModelRunResult(results_df, plotter, trained_models, selected_features) #
+    
+    model_choices = results_df['Model'].tolist() #
+    yield "✅ Model training & evaluation complete.", model_run_results, gr.Dropdown(choices=model_choices, interactive=True) #
+
+def predict_on_upload(uploaded_file, model_name, current_state, progress=gr.Progress()): #
+    if not uploaded_file: raise gr.Error("Please upload a file.") #
+    if not model_name: raise gr.Error("Please select a trained model.") #
+    model_run_results = current_state.get('model_results') #
+    fingerprint_type = current_state.get('fingerprint_type') #
+    if not model_run_results or not fingerprint_type: raise gr.Error("Please run Steps 2 and 3 first.") #
+    
+    model = model_run_results.models.get(model_name) #
+    selected_features = model_run_results.selected_features #
+    if model is None: raise gr.Error(f"Model '{model_name}' not found.") #
+    
+    smi_file, output_csv = 'predict.smi', 'predict_fp.csv' #
+    try: #
+        progress(0, desc="Reading & processing new molecules..."); yield "Reading uploaded file...", None, None #
+        df_new = pd.read_csv(uploaded_file.name) #
+        if 'canonical_smiles' not in df_new.columns: raise gr.Error("CSV must contain a 'canonical_smiles' column.") #
+        df_new = df_new.reset_index().rename(columns={'index': 'mol_id'}) #
+        
+        padel_input = pd.DataFrame({'smiles': df_new['canonical_smiles'], 'name': df_new['mol_id']}) #
+        padel_input.to_csv(smi_file, sep='\t', index=False, header=False) #
+        if os.path.exists(output_csv): os.remove(output_csv) #
+        
+        progress(0.3, desc="Calculating fingerprints..."); yield "Calculating fingerprints for new molecules...", None, None #
+        padeldescriptor(mol_dir=smi_file, d_file=output_csv, descriptortypes=fp_config.get(fingerprint_type), detectaromaticity=True, standardizenitro=True, threads=-1, removesalt=True, log=False, fingerprints=True) #
+        if not os.path.exists(output_csv) or os.path.getsize(output_csv) == 0: raise gr.Error("PaDEL calculation failed for the uploaded molecules.") #
+        
+        progress(0.7, desc="Aligning features and predicting..."); yield "Aligning features and predicting...", None, None #
+        df_fp = pd.read_csv(output_csv).rename(columns={'Name': 'mol_id'}) #
+        
+        X_new = df_fp.set_index('mol_id') #
+        X_new_aligned = X_new.reindex(columns=selected_features, fill_value=0)[selected_features] #
+        
+        predictions = model.predict(X_new_aligned) #
+        
+        results_subset = pd.DataFrame({'mol_id': X_new_aligned.index, 'predicted_pIC50': predictions}) #
+        df_results = pd.merge(df_new, results_subset, on='mol_id', how='left') #
+
+        progress(0.9, desc="Generating visualization..."); yield "Generating visualization...", None, None #
+        
+        df_grid_view = df_results.dropna(subset=['predicted_pIC50']).copy() #
+        mols_html = "<h3>No molecules with successful predictions to display.</h3>" #
+        if not df_grid_view.empty: #
+            df_grid_view.rename(columns={"predicted_pIC50": "Predicted pIC50"}, inplace=True) #
+            mols_html = mols2grid.display( #
+                df_grid_view, #
+                smiles_col='canonical_smiles', #
+                subset=['img', 'Predicted pIC50'], #
+                transform={"Predicted pIC50": lambda x: f"{x:.2f}"} #
+            )._repr_html_() #
+        
+        progress(1, desc="Complete!"); yield "✅ Prediction complete.", df_results[['canonical_smiles', 'predicted_pIC50']], mols_html #
+    finally: #
+        if os.path.exists(smi_file): os.remove(smi_file) #
+        if os.path.exists(output_csv): os.remove(output_csv) #
+
+# ==============================================================================
+# === GRADIO INTERFACE ===
+# ==============================================================================
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="sky"), title="Comprehensive Drug Discovery Workflow") as demo: #
+    gr.Markdown("# 🧪 Comprehensive Drug Discovery Workflow") #
+    gr.Markdown("A 3-step application to fetch, analyze, and model chemical bioactivity data.") #
+    app_state = gr.State({}) #
+    with gr.Tabs(): #
+        with gr.Tab("Step 1: Data Collection & EDA"): #
+            gr.Markdown("## Fetch Bioactivity Data from ChEMBL and Perform Exploratory Analysis") #
+            with gr.Row(): #
+                query_input = gr.Textbox(label="Target Query", placeholder="e.g., acetylcholinesterase, BRAF kinase", scale=3) #
+                fetch_btn = gr.Button("Fetch Targets", variant="primary", scale=1) #
+            status_step1_fetch = gr.Textbox(label="Status", interactive=False) #
+            target_id_table = gr.Dataframe(label="Available Targets", interactive=False, headers=["target_chembl_id", "pref_name", "organism"]) #
+            with gr.Row(): #
+                selected_target_dropdown = gr.Dropdown(label="Select Target ChEMBL ID", interactive=True, scale=3) #
+                process_btn = gr.Button("Process Data & Run EDA", variant="primary", scale=1, interactive=False) #
+            status_step1_process = gr.Textbox(label="Status", interactive=False) #
+            gr.Markdown("### Filtered Data & Analysis") #
+            bioactivity_class_selector = gr.CheckboxGroup(["active", "inactive", "intermediate"], label="Filter by Bioactivity Class", value=["active", "inactive", "intermediate"]) #
+            df_output_s1 = gr.Dataframe(label="Cleaned Bioactivity Data") #
+            with gr.Tabs(): #
+                with gr.Tab("Chemical Space Overview"): #
+                    with gr.Row(): #
+                        freq_plot_output = gr.Plot(label="Frequency of Bioactivity Classes") #
+                        scatter_plot_output = gr.Plot(label="Scatter Plot: MW vs LogP") #
+                with gr.Tab("pIC50 Analysis"): #
+                    with gr.Row(): #
+                        pic50_plot_output = gr.Plot(label="pIC50 Box Plot") #
+                        pic50_stats_output = gr.Dataframe(label="Mann-Whitney U Test Results for pIC50") #
+                with gr.Tab("Molecular Weight Analysis"): #
+                    with gr.Row(): #
+                        mw_plot_output = gr.Plot(label="MW Box Plot") #
+                        mw_stats_output = gr.Dataframe(label="Mann-Whitney U Test Results for MW") #
+                with gr.Tab("LogP Analysis"): #
+                    with gr.Row(): #
+                        logp_plot_output = gr.Plot(label="LogP Box Plot") #
+                        logp_stats_output = gr.Dataframe(label="Mann-Whitney U Test Results for LogP") #
+                with gr.Tab("H-Bond Donor/Acceptor Analysis"): #
+                    with gr.Row(): #
+                        hdonors_plot_output = gr.Plot(label="H-Donors Box Plot") #
+                        hacceptors_plot_output = gr.Plot(label="H-Acceptors Box Plot") #
+                    with gr.Row(): #
+                        hdonors_stats_output = gr.Dataframe(label="Stats for H-Donors") #
+                        hacceptors_stats_output = gr.Dataframe(label="Stats for H-Acceptors") #
+        with gr.Tab("Step 2: Feature Engineering"): #
+            gr.Markdown("## Calculate Molecular Fingerprints using PaDEL") #
+            with gr.Row(): #
+                fingerprint_dropdown = gr.Dropdown(choices=FP_list, value='PubChem' if 'PubChem' in FP_list else None, label="Select Fingerprint Method", scale=3) #
+                calculate_fp_btn = gr.Button("Calculate Fingerprints", variant="primary", scale=1) #
+            status_step2 = gr.Textbox(label="Status", interactive=False) #
+            output_df_s2 = gr.Dataframe(label="Final Processed Data", wrap=True) #
+            download_s2 = gr.DownloadButton("Download Feature Data (CSV)", variant="secondary", visible=False) #
+            mols_grid_s2 = gr.HTML(label="Interactive Molecule Viewer") #
+        with gr.Tab("Step 3: Model Training & Prediction"): #
+            gr.Markdown("## Train Regression Models and Predict pIC50") #
+            with gr.Tabs(): #
+                with gr.Tab("Model Training & Evaluation"): #
+                    train_models_btn = gr.Button("Train All Models", variant="primary") #
+                    status_step3_train = gr.Textbox(label="Status", interactive=False) #
+                    model_results_df = gr.DataFrame(label="Ranked Model Results", interactive=False) #
+                    with gr.Row(): #
+                        model_selector_s3 = gr.Dropdown(label="Select Model to Analyze", interactive=False) #
+                        feature_count_s3 = gr.Number(label="Top Features to Show", value=7, minimum=3, maximum=20, step=1) #
+                    with gr.Tabs(): #
+                        with gr.Tab("Validation Plots"): validation_plot_s3 = gr.Plot(label="Model Validation Plots") #
+                        with gr.Tab("Feature Importance"): feature_plot_s3 = gr.Plot(label="Top Feature Importances") #
+                with gr.Tab("Predict on New Data"): #
+                    gr.Markdown("Upload a CSV with a `canonical_smiles` column to predict pIC50.") #
+                    with gr.Row(): #
+                        upload_predict_file = gr.File(label="Upload CSV for Prediction", file_types=[".csv"]) #
+                        predict_btn_s3 = gr.Button("Run Prediction", variant="primary") #
+                    status_step3_predict = gr.Textbox(label="Status", interactive=False) #
+                    prediction_results_df = gr.DataFrame(label="Prediction Results") #
+                    prediction_mols_grid = gr.HTML(label="Interactive Molecular Grid of Predictions") #
+
+    # --- EVENT HANDLERS ---
+    def enable_process_button(target_id): return gr.update(interactive=bool(target_id)) #
+    def process_and_analyze_wrapper(target_id, selected_classes, current_state, progress=gr.Progress()): #
+        if not target_id: raise gr.Error("Please select a target ChEMBL ID first.") #
+        progress(0, desc="Fetching data..."); raw_data, msg1 = get_bioactivity_data(target_id); yield {status_step1_process: gr.update(value=msg1)} #
+        progress(0.3, desc="Cleaning data..."); processed_data, msg2 = clean_and_process_data(raw_data); yield {df_output_s1: processed_data, status_step1_process: gr.update(value=msg2)} #
+        current_state['cleaned_data'] = processed_data #
+        progress(0.6, desc="Running EDA..."); plots_and_stats = run_eda_analysis(processed_data, selected_classes); msg3 = plots_and_stats[-1] #
+        progress(1, desc="Done!") #
+        filtered_data = processed_data[processed_data.bioactivity_class.isin(selected_classes)] if not processed_data.empty else pd.DataFrame() #
+        outputs = [filtered_data] + list(plots_and_stats[:-1]) + [msg3, current_state] #
+        output_components = [df_output_s1, freq_plot_output, scatter_plot_output, pic50_plot_output, pic50_stats_output, mw_plot_output, mw_stats_output, logp_plot_output, logp_stats_output, hdonors_plot_output, hdonors_stats_output, hacceptors_plot_output, hacceptors_stats_output, status_step1_process, app_state] #
+        yield dict(zip(output_components, outputs)) #
+    def update_analysis_on_filter_change(selected_classes, current_state): #
+        cleaned_data = current_state.get('cleaned_data') #
+        if cleaned_data is None or cleaned_data.empty: return (pd.DataFrame(),) + (None,) * 11 + ("No data available.",) #
+        plots_and_stats = run_eda_analysis(cleaned_data, selected_classes); msg = plots_and_stats[-1] #
+        filtered_data = cleaned_data[cleaned_data.bioactivity_class.isin(selected_classes)] #
+        return (filtered_data,) + plots_and_stats[:-1] + (msg,) #
+    def handle_model_training(current_state, progress=gr.Progress(track_tqdm=True)): #
+        fingerprint_data = current_state.get('fingerprint_data') #
+        if fingerprint_data is None or fingerprint_data.empty: raise gr.Error("No feature data. Please complete Step 2.") #
+        for status_msg, model_results, model_choices_update in run_regression_suite(fingerprint_data, progress=progress): #
+            if model_results: current_state['model_results'] = model_results #
+            yield status_msg, model_results.dataframe if model_results else None, model_choices_update, current_state #
+    def save_dataframe_as_csv(df): #
+        if df is None or df.empty: return None #
+        filename = "feature_engineered_data.csv"; df.to_csv(filename, index=False); return gr.File(value=filename, visible=True) #
+    def update_analysis_plots(model_name, feature_count, current_state): #
+        model_results = current_state.get('model_results') #
+        if not model_results or not model_name: return None, None #
+        plotter = model_results.plotter; validation_fig = plotter.plot_validation(model_name); feature_fig = plotter.plot_feature_importance(model_name, int(feature_count)); plt.close('all'); return validation_fig, feature_fig #
+
+    fetch_btn.click(fn=get_target_chembl_id, inputs=query_input, outputs=[target_id_table, selected_target_dropdown, status_step1_fetch], show_progress="minimal") #
+    selected_target_dropdown.change(fn=enable_process_button, inputs=selected_target_dropdown, outputs=process_btn, show_progress="hidden") #
+    process_btn.click(fn=process_and_analyze_wrapper, inputs=[selected_target_dropdown, bioactivity_class_selector, app_state], outputs=[df_output_s1, freq_plot_output, scatter_plot_output, pic50_plot_output, pic50_stats_output, mw_plot_output, mw_stats_output, logp_plot_output, logp_stats_output, hdonors_plot_output, hdonors_stats_output, hacceptors_plot_output, hacceptors_stats_output, status_step1_process, app_state]) #
+    bioactivity_class_selector.change(fn=update_analysis_on_filter_change, inputs=[bioactivity_class_selector, app_state], outputs=[df_output_s1, freq_plot_output, scatter_plot_output, pic50_plot_output, pic50_stats_output, mw_plot_output, mw_stats_output, logp_plot_output, logp_stats_output, hdonors_plot_output, hdonors_stats_output, hacceptors_plot_output, hacceptors_stats_output, status_step1_process], show_progress="minimal") #
+    calculate_fp_btn.click(fn=calculate_fingerprints, inputs=[app_state, fingerprint_dropdown], outputs=[status_step2, output_df_s2, download_s2, mols_grid_s2, app_state]) #
+    
+    @download_s2.click(inputs=app_state, outputs=download_s2, show_progress="hidden") #
+    def download_handler(current_state): #
+        df_to_download = current_state.get('fingerprint_data') #
+        return save_dataframe_as_csv(df_to_download) #
+    
+    train_models_btn.click(fn=handle_model_training, inputs=[app_state], outputs=[status_step3_train, model_results_df, model_selector_s3, app_state]) #
+    for listener in [model_selector_s3.change, feature_count_s3.change]: listener(fn=update_analysis_plots, inputs=[model_selector_s3, feature_count_s3, app_state], outputs=[validation_plot_s3, feature_plot_s3], show_progress="minimal") #
+    predict_btn_s3.click(fn=predict_on_upload, inputs=[upload_predict_file, model_selector_s3, app_state], outputs=[status_step3_predict, prediction_results_df, prediction_mols_grid]) #
+
+if __name__ == "__main__": #
+    demo.launch(debug=True) #