This page describes mining for molecules. Since molecules may be represented by molecular 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.
Contents
- Kernel methods
- Maximum Common Graph methods
- Molecular query methods
- Methods based on special architectures of neural networks
- References
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
Maximum Common Graph methods
Molecular query methods
Methods based on special architectures of neural networks
References
Molecule mining Wikipedia(Text) CC BY-SA