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Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds

Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds. Mukund Deshpande, Michihiro Kuramochi , George Karypis University of Minnesota, Department of Computer Science/Army HPC Research Center Teacher : Dr.Ynag Student : Gun-Ren Wang Minneapolis, MN 55455

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Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds

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  1. Frequent Sub-Structure-Based Approaches for ClassifyingChemical Compounds Mukund Deshpande, Michihiro Kuramochi , George Karypis University of Minnesota, Department of Computer Science/Army HPC Research Center Teacher : Dr.Ynag Student : Gun-Ren Wang Minneapolis, MN 55455 Technical Report #03-016

  2. Outline • Introduction • Frequent Subgraph Based Classification Framework • Feature Generation Feature Generation • Feature Selection • Conclusion

  3. Introduction Any new drug should not only produce the desired response to the disease, but should do so with minimal side effects. • Evaluating this large set of compounds using HTS can be prohibitively expensive. • Not all biological assays can be converted to high throughput format. • Studying what part of the chemical compound leads to desirable behavior.

  4. Frequent Subgraph Based Classification Framework

  5. Feature Generation • In our classification algorithm we find the frequently occurring subgraphs using the FSG algorithm. • Topological sub-structures capture the connectivity of atoms in the chemical compound but they ignore the 3D shape of the sub-structures.

  6. Adjacency-list representation

  7. Canonical Labeling

  8. Candidate Joining

  9. Candidate Generation(1)

  10. Candidate Generation(2)

  11. Feature Selection For example,we have two ruleitems that have the same condset: <{(A, 1), (B, 1)}, (class: 1)>. <{(A, 1), (B, 1)}, (class: 2)>. Assume the support count of the condset is 3. (assume |D| = 10): (A, 1), (B, 1)(class, 1) [supt = 20%, confd= 66.7%] we only produce one PR(possible rule)

  12. The CBA-RG algorithm

  13. Building a Classifier • Definition: Given two rules, r and r < r (also called r precedes rj or ri has a higher precedence than rj) if • 1. the confidence of ri is greater than that of rj, or • 2. their confidences are the same, but the support of ri is greater than that of rj, or • 3.both the confidences and supports of ri and rj are the same, but ri is generated earlier than rj;

  14. A naïve algorithm for CBA-CB: M1

  15. Experimental Methodology & Metrics Table 1: The characteristics of the various datasets. N is the number of compounds in the database. ¯ NA and ¯ NB are the averagenumber of atoms and bonds in each compound. ¯ L A and ¯ L B are the average number of atom- and bond-types in each dataset.max NA/min NA and max NB/min NB are the maximum/minimum number of atoms and bonds over all the compounds in eachdataset.

  16. Varying Minimum Support

  17. Conclusion In this paper we presented a highly-effective algorithm for classifying chemical compounds based on frequent substructure discovery that can scale to large datasets.

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