Abstract
Increased availability of large repositories of chemical compounds is creating new
challenges and opportunities for the application of machine learning methods to
problems in computational chemistry and chemical informatics. Because chemical
compounds are often represented by the graph of their covalent bonds, machine
learning methods in this domain must be capable of processing graphical structures
with variable size. Here we first briefly review the literature on graph kernels and
then introduce three new kernels (Tanimoto, MinMax, Hybrid) based on the idea
of molecular fingerprints and counting labeled paths of depth up to d using depthfirst
search from each possible vertex. The kernels are applied to three classification
problems to predict mutagenicity, toxicity, and anti-cancer activity on three publicly
available data sets. The kernels achieve performances at least comparable, and most
often superior, to those previously reported in the literature reaching accuracies of
91.5 on the Mutag dataset, 65-67 on the PTC (Predictive Toxicology Challenge)
dataset, and 72 on the NCI (National Cancer Institute) dataset. Properties and
tradeoffs of these kernels, as well as other proposed kernels that leverage 1D or 3D
representations of molecules, are briefly discussed.
