ML models in toxicology typically follow a specific workflow. First, data collection is performed, where information on chemical structures, biological activities, and toxicity outcomes is gathered. Next, this data is preprocessed to remove noise and handle missing values. Feature extraction is then conducted to identify key variables that influence toxicity. The data is then split into training and testing sets.
During the training phase, the ML model learns from the data by identifying patterns and correlations. Various algorithms, such as random forests, support vector machines, and neural networks, can be used depending on the complexity and nature of the data. Finally, the model is validated using the testing set to evaluate its performance.
