Publikation: Mining Molecular Fragments : Finding Relevant Substructures of Molecules
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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 substantially faster search by eliminating the need for frequent, computationally expensive reembeddings and by suppressing redundant search. We prove the usefulness of our algorithm by demonstrating the discovery of activity-related groups of chemical compounds in the well-known National Cancer Institute s HIV-screening dataset.
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BORGELT, Christian, Michael R. BERTHOLD, 2002. Mining Molecular Fragments : Finding Relevant Substructures of Molecules. 2002 IEEE International Conference on Data Mining. ICDM 2002. Maebashi City, Japan. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings.. IEEE Comput. Soc, 2002, pp. 51-58. ISBN 0-7695-1754-4. Available under: doi: 10.1109/ICDM.2002.1183885BibTex
@inproceedings{Borgelt2002Minin-5589, year={2002}, doi={10.1109/ICDM.2002.1183885}, title={Mining Molecular Fragments : Finding Relevant Substructures of Molecules}, isbn={0-7695-1754-4}, publisher={IEEE Comput. Soc}, booktitle={2002 IEEE International Conference on Data Mining, 2002. Proceedings.}, pages={51--58}, author={Borgelt, Christian and Berthold, Michael R.} }
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