6. Reaction Prediction

dataset: USPTO subset (https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00064)

6.1 RNNs and LSTM

• Recurrent Neural Networks (RNNs)
  • RNNs are designed to process sequential data, making them ideal for tasks that depend on time-series or ordered inputs.
  • Common applications include:
    • Time series forecasting (e.g., predicting flood levels).
    • Natural language processing (e.g., language translation, sentiment analysis).
    • Speech recognition.
    • Image captioning.
• Key Characteristics of RNNs
  • “Memory” function that uses past inputs to influence current output.
  • Different from feedforward networks due to their ability to handle sequences.
  • Uses shared parameters across network layers, unlike feedforward networks which have unique weights for each node.
• RNN Operations
  • Processes input step-by-step while maintaining a hidden state that holds information from prior steps.
  • Utilizes backpropagation through time (BPTT) to calculate gradients and update weights.
• Limitations of Traditional RNNs
  • Struggles with long-term dependencies due to vanishing gradient problems.
• LSTM Networks
  • A specialized type of RNN designed to capture long-term dependencies.
  • Uses gates (input, forget, and output) to regulate the flow of information.
• Use Cases for LSTM
  • Time series analysis for experimental data.
  • Prediction of chemical properties.
  • Simulation of reactions over time.

    Python Code Snippet for Reaction Prediction Using RNNs and LSTMs

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense

# Load and preprocess your dataset (e.g., BACE Scaffold dataset from https://paperswithcode.com/dataset/bace-scaffold)
# Example: Assume input_data and target_data are preprocessed and ready for training

# Define the model
model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=128))  # Example embedding layer
model.add(LSTM(128))  # LSTM layer with 128 units
model.add(Dense(1, activation='sigmoid'))  # Output layer for binary classification

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Example input data (replace with preprocessed chemical sequences)
input_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]  # Replace with actual sequence data
target_data = [0, 1, 0]  # Replace with actual target labels

# Train the model
model.fit(input_data, target_data, epochs=10)

# Make predictions
predictions = model.predict(input_data)

How the Code Works

• Importing Required Libraries:
  • tensorflow for building neural networks.
  • Sequential, Embedding, LSTM, and Dense layers from tensorflow.keras for constructing the model.
• Defining the Model:
  • Create a Sequential model, which means layers are added one after another.
  • Add an Embedding layer that helps the model understand the input features.
  • Include an LSTM layer with 128 units to capture dependencies in the input data.
  • Add a Dense layer with a sigmoid activation function for binary classification.
• Compiling the Model:
  • Use the Adam optimizer to adjust the learning rate during training.
  • Use binary_crossentropy as the loss function since this example involves binary classification.
  • Track accuracy as a metric.
• Preparing Data:
  • Replace input_data and target_data with data from the BACE Scaffold dataset after preprocessing.
  • Ensure the dataset is formatted as sequences of numbers (e.g., molecular features) for the model.
• Training the Model:
  • Train using model.fit() by providing input data and corresponding target outputs.
  • Use epochs=10 to iterate the training process multiple times to improve learning.
• Making Predictions:
  • Use model.predict() to make predictions based on new input data.
  • This output can help indicate the likelihood of a specific chemical reaction occurring.
• Timesteps & Features: Ensure your input shape matches the data format, where timesteps refer to the number of time points and features refer to properties recorded at each time point.
• Data Example: Sequences should represent input variables like time-dependent chemical properties (e.g., temperature, concentration).