Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering frequent topological structures from graph datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
MoSS: a program for molecular substructure mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
The Gaston Tool for Frequent Subgraph Mining
Electronic Notes in Theoretical Computer Science (ENTCS)
Latent structure pattern mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Data analysis in the life sciences — sparking ideas —
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Data analysis in the life sciences — sparking ideas —
ECML'05 Proceedings of the 16th European conference on Machine Learning
Molecular fragment mining for drug discovery
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Using and learning semantics in frequent subgraph mining
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules with the aim to build a classifier that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper we present two extensions of this approach: A special treatment of rings and a method that finds fragments with wildcards based on chemical expert knowledge.