bioloGPT: Evolve Code: This code analyzes BindingGYM data to train a deep learning model for predicting binding affinities. Incorporate additional features from the BindingGYM dataset to enhance model accuracy and predictive power. BindingGYM deep learning binding affinities protein complexes import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense # Load BindingGYM dataset bindinggym_data = pd.read_csv('bindinggym_data.csv') # Preprocess data X = bindinggym_data[['feature1', 'feature2', 'feature3']].values # Example features Y = bindinggym_data['binding_affinity'].values # Split data into training and testing sets X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42) # Build a simple neural network model model = Sequential() model.add(Dense(64, activation='relu', input_dim=X_train.shape[1])) model.add(Dense(32, activation='relu')) model.add(Dense(1, activation='linear')) # Output layer for regression # Compile the model model.compile(optimizer='adam', loss='mean_squared_error') # Train the model model.fit(X_train, Y_train, epochs=100, batch_size=32, validation_split=0.2) # Evaluate the model loss = model.evaluate(X_test, Y