Editing Chemical database (section)

== Similarity ==
{{Main|Chemical similarity}}

There is no single definition of molecular similarity, however the concept may be defined according to the application and is often described as an [[inverse element|inverse]] of a [[distance|measure of distance]] in descriptor space. Two molecules might be considered more similar for instance if their difference in [[molecular weight]]s is lower than when compared with others. A variety of other measures could be combined to produce a multi-variate distance measure. Distance measures are often classified into [[Euclidean distance|Euclidean measure]]s and non-Euclidean measures depending on whether the [[triangle inequality]] holds. Maximum Common Subgraph ([[Maximum common subgraph isomorphism problem|MCS]]) based substructure search <ref name="SMSD09">{{cite journal|first1=S. A. |last1=Rahman|first2= M. |last2=Bashton |first3= G. L. |last3= Holliday |first4= R. |last4=Schrader |first5=J. M. |last5=Thornton |year=2000 |title=Small Molecule Subgraph Detector (SMSD) toolkit|journal= Journal of Cheminformatics|volume=1|issue=1|page=12|doi=10.1186/1758-2946-1-12|pmid=20298518|pmc=2820491 |doi-access=free }}</ref>(similarity or distance measure) is also very common. MCS is also used for screening drug like compounds by hitting molecules, which share common subgraph (substructure).<ref>{{cite journal|first1=S. Asad |last1=Rahman|first2= M. |last2=Bashton|first3= G. L. |last3=Holliday|first4= R. |last4=Schrader |first5=J. M. |last5=Thornton|title= Small Molecule Subgraph Detector (SMSD) Toolkit|journal= Journal of Cheminformatics |year=2009|volume= 1|issue=1|page=12 |doi=10.1186/1758-2946-1-12 |pmid=20298518|pmc=2820491|url=http://www.ebi.ac.uk/thornton-srv/software/SMSD/ |doi-access=free }}</ref>

Chemicals in the databases may be [[cluster (computing)|cluster]]ed into groups of 'similar' molecules based on similarities. Both hierarchical and non-hierarchical clustering approaches can be applied to chemical entities with multiple attributes. These attributes or molecular properties may either be determined empirically or computationally derived [[Molecular descriptor|descriptors]]. One of the most popular clustering approaches is the [[Jarvis-Patrick algorithm]].<ref>{{cite journal|last=Butina|first= Darko |year=1999|title= Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets|journal=  Chem. Inf. Comput. Sci. |volume=39|issue= 4 |pages=747–750|doi=10.1021/ci9803381}}</ref>

In [[pharmacological]]ly oriented chemical repositories, similarity is usually defined in terms of the biological effects of compounds ([[ADME]]/tox) that can in turn be semiautomatically inferred from similar combinations of physico-chemical descriptors using [[QSAR]] methods.