Kinase Inhibitors and Polypharmacology

The Curious Wavefunction has a nice post on the issue of selective and non-selective kinase inhibitors. An interesting commentary, especially in the light of the recent paper on network polypharmcology. While there have been a number of papers on polypharmcology and the idea itself is very attractive, it has seemed to me that for this approach to succeed we need very detailed information on the targets and systems involved in these networks. Indeed, a current project of mine is currently hitting this problem. As Ashutosh notes,

… in the first place we don’t even know what specific subset of kinases to hit for treating a particular disease. First comes target validation, then modulation.

Depicting SMILES Dynamically

Sometime back I was playing around with dynamic HTML and cam across a tutorial that described how to implement the dynamic suggestion feature that is commonly found on many websites (such as Google and Amazon). This set me wondering how I could use this mechanism to dynamically depict a SMILES string as I type it.

Continue reading →

Surnames Across the Globe

I came across an interesting site called the World Names Profiler, which given a surname colors a map of the world based on frequency of occurence of the name in different countries. They have a dataset of 300 million names across 26 countries.

While it’s a nice visualization, it was very interesting for me to see the spread of Indian surnames, as the Indian diaspora is spread out all over the globe. Obviously Indian surnames have a maximum frequency in India, but it’s quite interesting to note that Guha has a high frequency in North America and Central Europe and a very low frequency in Australia. I was also surprised to see that it had a non-zero occurrence in Argentina. On the other hand, Ghosh, is has a higher frequency in Canada compared to the US and a higher frequency in Argentina than Guha. However, Patel, has a much higher frequency in Australia than either Guha or Ghosh. Singh on the other hand, appears to have similar frequencies in Canada and Australia, which are both higher than in the US

I chose these surname because they’re pretty common Indian surnames. One could correlate frequencies of occurrence to the background represented by the surnames, but that would be easily confounded by stereotypes. However, for me, it’s a nice visualization of how Indians have spread over the globe.

Do the CDK Fingerprints Work?

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.

Continue reading →

Working With Fingerprints in R (can’t beat C!)

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.

Continue reading →