  In this study, we intend to solve a mutual information problem in interacting
molecules of any type, such as proteins, nucleic acids, and small molecules.
Using machine learning techniques, we accurately predict pairwise interactions,
which can be of medical and biological importance. Graphs are are useful in
this problem for their generality to all types of molecules, due to the
inherent association of atoms through atomic bonds. Subgraphs can represent
different molecular domains. These domains can be biologically significant as
most molecules only have portions that are of functional significance and can
interact with other domains. Thus, we use subgraphs as features in different
machine learning algorithms to predict if two drugs interact and predict
potential single molecule effects.
