Mining molecular fragments: Finding relevant substructures of molecules, in

We present an algorithm to find fragments in a set of molecules that help to discriminate between different classes of, for instance, activity in a drug discovery context. Instead of carrying out a brute-force search, our method generates fragments by embedding them in all appropriate molecules in parallel and prunes the search tree based on a local order of the atoms and bonds, which results in sub-stantially faster search by eliminating the need for frequent, computationally expensive reembeddings and by suppress-ing redundant search. We prove the usefulness of our al-gorithm by demonstrating the discovery of activity-related groups of chemical compounds in the well-known National Cancer Institute’s HIV-screening dataset. 1