Archive for the ‘chemistry’ Category
In a previous post, I described a simple web form to query and visualize the solubility data being generated as part of the ONS Challenge. The previous approach required me to manually download the data and load it into a Postgres database. While trivial from a coding point of view, it’s a pain since I have to keep my local DB in sync with the Google Docs spreadsheet.
This is very nice since I now no longer have to maintain a local DB and ensure that it’s in sync with Jean-Claudes results. Of course, there are some drawbacks to this method. First, the query page will assume that the data in the spreadsheet is clean. So if there are two entries called “Ethanol” and “ethanol”, they will be considered seperate solvents. Secondly, this approach cannot be used to include cheminformatics in the queries, since Google doesn’t support that functionality. Finally, it’s not going to be very good for large spreadsheets.
However, this is a very nice API, that allows one to elegantly integrate web applications with live data. I heart Google!
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.
Houghten, R. et al, “Strategies for the Use of Mixture-Based Synthetic Combinatorial Libraries: Scaffold Ranking, Direct Testing In Vivo, and Enhanced Deconvolution by Computational Methods”, J. Comb. Chem., 2008, 10, 3-19
Recently a collaborator pointed me to the above article by Houghten and co-workers where they describe the use of mixture-based combinatorial libraries for high-throughput screening (HTS) experiments.
Traditionally an HTS experiment will screen thousands to millions of individual molecules. Obviously, it’s all done by robots so though you have to be careful during setup it’s not like you have to do it all by hand. But the fact is, if it’s possible to reduce the actual number of individual screens, life becomes easier and cheaper. Houghten et al describe an elegant approach that does just this.