Archive for the ‘pubchem’ tag
Which XRay ligands are closest to the Fontaine et al. structure-activity relationship data for allowing structure-based drug design?
Using Blue Obelisk tools and ChemSpider and where Fontaine et al. refers to the Fontaine Factor Xa dataset. You should read his post for a nice analysis of the problem. I just wanted to consider two points he had raised.
A few days back, Hari on FriendFeed had asked how one could get a a CAS number from a PubChem compound ID (CID). The reverse, that is finding a CID for a given CAS number is generally quite easy as shown by Rich here and here. Since I was trying to get some writing done, this was a good excuse for a quick hack to solve the problem.
I met with Jean-Claude Bradley yesterday and we had a pretty useful hack session, allowing him to easily incorporate chemical and cheminformatics functionality into a GoogleDocs spreadsheet.
A common task that Jean-Claude wanted to automate was the calculation of milligrams (or milliliters) of a chemical required for a certain molarity. So what we need for this calculation is the compound name, desired molarity, molecular weight and the density. Importantly, the people who’d like to use this will provide compound names and not a directly parseable SMILES. So we’d also like to (optionally) get the SMILES. Finally, he wanted to be able to do this in a Google spreadsheet – rather than a specific web page or stand alone program.
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.
Virtual screening (VS) is a common task in the drug discovery process and is a computational method to identify promising compounds from a collection of hundreds to millions of possible compounds. What “promising” exactly means, depends on the context – it might be compounds that will likely exhibit certain pharmacological effects. Or compounds that are expected to non-toxic. Or combinations of these and other properties. Many methods are available for virtual screening including similarity, docking and predictive models.
So, given the plethora of methods which one do we use? There are many factors affecting choice of VS method including availability, price, computational cost and so on. But in the end, deciding which one is better than another depends on the use of benchmarks. There are two features of VS benchmarks: the metric employed to decide whether one method is better than another and the data used for benchmarking. This post focuses on the latter aspect.