Archive for October, 2008
The recent paper by Wang and Bajorath is an interesting approach to identifying the important bits in a fingerprint, with respect to a dataset.
Their discussion focuses on the structural key type fingerprints (such as MACCS and the BCI fingerprints) and the problem they are trying to address is the fact that certain structural features may be more important for similarity searching than others. This is also related to the fact that molecular complexity (i.e., the number of structural features) can lead to bias in similarity calculations . Given a dataset, an easy way to identify the important bits is the so called consensus approach [2, 3]- basically find out which bit positions are set to 1 for all (or a specified fraction) of the dataset. While useful, this can be misled if the target dataset has many molecules with a large number of structural features (so that many bits in the fingerprint will be set to 1).
Pub3D is a 3D version of PubChem, in which we have generated a single conformer for 99% of PubChem using the smi23d suite of programs. The structures are then stored in a PostgreSQL database along with their distance moment shape descriptors described by Ballester and Graham-Richards. This allows us to perform shape similarity queries against a user supplied 3D structure. By partitioning the database (thanks to the CGL folks at IU) and using a spatial index, performance is quite snappy. (I had briefly mentioned this in a presentation at the ACS meeting, last spring).
The database had been down for some time, so today I got it back up and running and AJAX’ified the interface, to make it look a little nicer. jQuery rocks! (OK, the color scheme sucks)
There are obvious drawbacks to the current database – single conformer shape search is not very rigorous, especially since the stored structures are not necessarily the minimum energy conformer. However, we have started generating multiple conformers, so hopefully we’ll address this issue in time. The bigger issue is how this approach to shape similarity compares to other well known approaches such as ROCS. Clearly, a shape descriptor approach is lower resolution to a volumetric approach such as ROCS, so in that sense the results are ‘rougher’. However visual inspection of some searches seems to indicate that it isn’t too bad. The paper describing these shape descriptors didn’t do a rigorous comparison – that’s on our TODO list.
OK, the fun part (a.k.a, coding) is done for now – got to get back to the paper.