Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules
This work addresses a domain-specific problem in computational biology and drug discovery, with incremental improvements in methodology.
The study tackled the problem of predicting drug interactions and mutagenicity by using ensemble classifiers on molecular subgraphs, achieving accurate predictions with potential medical and biological importance.
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.