SVMs operate by mapping input data into a higher-dimensional space where a linear decision boundary can be constructed. This process is particularly beneficial in toxicology, where the relationships between chemical structure and toxicity are often non-linear. By using kernel functions, such as the radial basis function (RBF) or polynomial kernel, SVMs can transform input data to better separate toxic from non-toxic substances.
