Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers   on Subgraphs of Molecules
  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. 
Andrew Schaumberg, Angela Yu, Tatsuhiro Koshi, Xiaochan Zong,   Santoshkalyan Rayadhurgam
stat.ML cs.LG