Predictive models in computational toxicology are developed using various machine learning and statistical techniques. These models are trained on large datasets containing information about known toxic and non-toxic chemicals. Features such as molecular structure, physicochemical properties, and biological interactions are used to train these models. Techniques like Quantitative Structure-Activity Relationship (QSAR) models, neural networks, and support vector machines are commonly employed.
