Archive for the ‘similarity’ tag
A few days back I posted on improving query times in Pub3D by going from a monolithic database (17M rows), to a partitioned version (~ 3M rows in 6 separate databases) and then performing queries in parallel. I also noted that we were improving query times by making use of an R-tree spatial index.
I’ve wondered about this quote from the ANN page at http://www.cs.umd.edu/~mount/ANN/ .
“Computing exact nearest neighbors in dimensions much higher than 8 seems to be a very difficult task. Few methods seem to be significantly better than a brute-force computation of all distances.”
Since you’re in 12-D space, this suggests that a linear search would be faster. The times I’ve done searches for near neighbors in higher dimensional property space have been with a few thousand molecules at most, so I’ve never worried about more complicated data structures.
Has there been work on creating visualizations of “conformer envelopes”, graphical representations of the conformational space occupied (or available) to molecules. Particularly when such visualizations are used to (quickly/visually) compare whether 2 molecules can adopt the same shape – or if there are shapes of one that can’t be adopted by another.
A while back when I was investigating the use of the Ballester & Graham-Richards shape descriptors for 3D similarity searching. It turns out they perform quite poorly in enrichment benchmarks (which I’ll describe in a future post). At that time I was thinking of how Pub3D could scale to a multi-conformer version and I realized that the shape descriptors would allow me to easily visualize the “shape space” of a set of compounds. When these compounds are conformers for a molecule, one effectively gets a conformational envelope.
In a previous post, I dicussed virtual screening benchmarks and some new public datasets for this purpose. I recently improved the performance of the CDK hashed fingerprints and the next question that arose is whether the CDK fingerprints are any good. With these new datasets, I decided to quantitatively measure how the CDK fingerprints compare to some other well known fingerprints.
Update – there was a small bug in the calculations used to generate the enrichment curves in this post. The bug is now fixed. The conclusions don’t change in a significant way. To get the latest (and more) results you should take a look here.
Since I do a lot of cheminformatics work in R, I’ve created various functions and packages that make life easier for me as do my modeling and analysis. Most of them are for private consumption. However, I’ve released a few of them to CRAN since they seem to be generally useful.
One of them is the fingerprint package (version 2.9 was just uploaded to CRAN) , that is designed to read and manipulate fingerprint data generated from various cheminformatics toolkits or packages. Right now it supports output from the CDK, BCI and MOE. Fingerprints are represented using S4 classes. This allows me to override the R logical operators, so that one can do things like compute the logical OR of two fingerprints.
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).