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Mining Statistically Significant Molecular Substructures for Efficient Molecular Classification
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DOI：10.1021/ci900035z.png)
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Abstract
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The increased availability of large repositories of chemical compounds has created new challenges in designing efficient molecular querying and mining systems. Molecular classification is an important problem in drug development where libraries of chemical compounds are screened and molecules with the highest probability of success against a given target are selected. We have developed a technique called GraphSig to mine significantly over-represented molecular substructures in a given class of molecules. GraphSig successfully overcomes the scalability bottleneck of mining patterns at a low frequency. Patterns mined by GraphSig display correlation with biological activities and serve as an excellent platform on which to build molecular analysis tools. The potential of GraphSig as a chemical descriptor is explored, and support vector machines are used to classify molecules described by patterns mined using GraphSig. Furthermore, the over-represented patterns are more informative than features generated exhaustively by traditional fingerprints; this has potential in providing scaffolds and lead generation. Extensive experiments are carried out to evaluate the proposed techniques, and empirical results show promising performance in terms of classification quality. An implementation of the algorithm is available free for academic use at http://www.uweb.ucsb.edu/similar to sayan/software/GraphSig.tar.
Keywords:
SIMILARITY
PREDICTION
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