Molecule mining

This page describes mining for molecules. Since molecules are multi-labeled graphs this is strongly related to graph mining and structured data mining. The main problem is how to represent molecules while discriminating the data instances. One way to do this is chemical similarity metrics, which has a long tradition in the field of cheminformatics.

Typical approaches to calculate chemical similarities use chemical fingerprints, but this loses the underlying information about the molecule topology. Mining the molecular graphs directly avoids this problem. So does the inverse QSAR problem which is preferable for vectorial mappings.

Kernel methods

 * Marginalized graph kernel


 * Optimal assignment kernel


 * Pharmacophore kernel

Maximum Common Graph methods

 * MCS-HSCS (Highest Scoring Common Substructure (HSCS) ranking strategy for single MCS)

Molecular query methods

 * MoFa/MoSS
 * ParMol (contains MoFa, FFSM, gSpan, and Gaston)
 * PolyFARM
 * SMIREP
 * Warmr
 * AGM
 * DMax
 * Gaston
 * optimized gSpan
 * MolFea
 * SAm/AIm/RHC
 * LAZAR