Archive for the ‘Molecular Graphs’ Category

Using molecular networks to assess molecular similarity

Friday, February 15th, 2013

Systems chemistry: Using molecular networks to assess molecular similarity by Bailey Fallon.

From the post:

In new research published in Journal of Systems Chemistry, Sijbren Otto and colleagues have provided the first experimental approach towards molecular networks that can predict bioactivity based on an assessment of molecular similarity.

Molecular similarity is an important concept in drug discovery. Molecules that share certain features such as shape, structure or hydrogen bond donor/acceptor groups may have similar properties that make them common to a particular target. Assessment of molecular similarity has so far relied almost exclusively on computational approaches, but Dr Otto reasoned that a measure of similarity might be obtained by interrogating the molecules in solution experimentally.

Important work for drug discovery but there are semantic lessons here as well:

Tests for similarity/sameness are domain specific.

Which means there are no universal tests for similarity/sameness.

Lacking universal tests for similarity/sameness, we should focus on developing documented and domain specific tests for similarity/sameness.

Domain specific tests provide quicker ROI than less useful and doomed universal solutions.

Documented domain specific tests may, no guarantees, enable us to find commonalities between domain measures of similarity/sameness.

But our conclusions will be based on domain experience and not projection from our domain onto others, less well known domains.

Systematic benchmark of substructure search in molecular graphs – From Ullmann to VF2

Sunday, August 12th, 2012

Systematic benchmark of substructure search in molecular graphs – From Ullmann to VF2 by Hans-Christian Ehrlich and Matthias Rarey. (Journal of Cheminformatics 2012, 4:13 doi:10.1186/1758-2946-4-13)

Abstract:

Background

Searching for substructures in molecules belongs to the most elementary tasks in cheminformatics and is nowadays part of virtually every cheminformatics software. The underlying algorithms, used over several decades, are designed for the application to general graphs. Applied on molecular graphs, little effort has been spend on characterizing their performance. Therefore, it is not clear how current substructure search algorithms behave on such special graphs. One of the main reasons why such an evaluation was not performed in the past was the absence of appropriate data sets.

Results

In this paper, we present a systematic evaluation of Ullmann’s and the VF2 subgraph isomorphism algorithms on molecular data. The benchmark set consists of a collection of 1236 SMARTS substructure expressions and selected molecules from the ZINC database. The benchmark evaluates substructures search times for complete database scans as well as individual substructure-molecule-pairs. In detail, we focus on the influence of substructure formulation and size, the impact of molecule size, and the ability of both algorithms to be used on multiple cores.

Conclusions

The results show a clear superiority of the VF2 algorithm in all test scenarios. In general, both algorithms solve most instances in less than one millisecond, which we consider to be acceptable. Still, in direct comparison, the VF2 is most often several folds faster than Ullmann’s algorithm. Additionally, Ullmann’s algorithm shows a surprising number of run time outliers.

Questions:

How do your graphs compare to molecular graphs? Similarities? Differences?

For searching molecular graphs, what algorithm does your software use for substructure searches?