A support vector machine (SVM) is a supervised machine learning model for binary classification. To train an SVM using a labeled training data set {(G n , y n )} N n=1 , with G n ∈ G and y n ∈ {-1, 1}, we rely on a feature map φ : G → R F to represent graphs in a vector space. Such feature maps can be constructed explicitly (the case with the MACCS FP), or implicitly via the use of a kernel function k(G, G ) = φ(G) • φ(G ) between pairs of data (the case with the random walk feature vector). We briefly explain the SVM here. For more details, consult Refs. .