Hanjo Kim of the Bioinformatics & Molecular Design Research Center (BMDRC) in Seoul, South Korea writes in to tell about his Korean-language cheminformatics blog Agile2Robust. Some of his articles, such as this one on PubChem have been translated from Depth-First; others are based on Depth-First articles.

This completes a circle. It was Kim's post to the Ruby mailing list in late 2005 and the responses to it that helped me in the early days of developing cheminformatics software in Ruby (and writing articles about it here), although Kim probably didn't know it at the time.

During this process, none of the usual scientific protocols have been observed: no journal articles were written or published; no grants to do any of the work were prepared or funded; no presentations were made; and no central authority was involved (at least on my end). In fact, if any of these conventions had been observed, the work would have never seen the light of day. Free tools, free services, and open standards enabled one scientist to connect with another in a way that's impossible to achieve through conventional means.

A recent D-F article discussed a method for encoding machine-readable molecular structure information as image metadata. This article generated some interest among developers. For example, Noel O'Boyle posted code for reading PNG image metadata with Python. The popularity of Python in cheminformatics makes this approach especially attractive.

But how would you write PNG image metadata with Python? The obvious answer of using Image.info followed by Image.write doesn't appear to work. Given my limited knowledge of Python, the answer must come from elsewhere.

Fortunately, Nick Galbreath wrote in to offer a solution. Using Python, PIL, and an undocumented class, Nick has developed a small wrapper function that writes metadata for PNG images. In fact, Nick is fast on his way to becoming a PNG metadata expert, if reluctantly so. His blog is worth checking out and contains several useful techniques for image manipulation.

The peer-reviewed literature and patents are chock-full of valuable biological screening data. The problem is not finding it; the problem is putting it all together. A company called Aureus Pharma has stepped into this void by offering a product aimed directly at collating and mining published biological screening data.

The product, AurScope, consists of a curated database of biological screening results divided into categories including GPCRs, Kinases, Ion Channels, ADME, and hERG. Searches can be performed by structure or activity at a target. Aureus offers AurSCOPE in a few configurations - the one I saw demoed was a stand-alone client. Apparently, Aureus also offers AurSCOPE as a stripped-down database that can be integrated within a drug discovery organization's existing IT infrastructure, presenting some very interesting possibilities for medicinal chemists looking to exploit competitor data or work around selectivity problems.

One of the more interesting aspects about AurSCOPE is the way it's produced. Like ScienceHack, AurSCOPE is built with people-power. Although the company wouldn't give exact numbers, Aureus' representatives did say that it employed a number of trained scientists to cull the biological screening results making up AurSCOPE from published sources.

Competitive intelligence and target selectivity are omnipresent concerns in drug discovery. Products like AurSCOPE can fill important needs in these areas. Although it wasn't clear how AurSCOPE handles the (not uncommon) situation in which two labs report numbers based on different screening protocols at the same target, the product seems to have a lot going for it. Given the vast wealth of disjointed data contained in the published chemical literature, Aureus' approach of using trained scientists to assemble it into something greater than the sum of its parts is well worth keeping in mind.

image credit: Cranium Oxide

If a picture is worth a thousand words, a video must be worth a million. ScienceHack offers a service that helps users find free videos in a variety of scientific fields, including chemistry.

Perhaps more noteworthy than the service itself is how ScienceHack works. Rather than relying on robotic indexing agents, ScienceHack claims to use real scientists to screen its links to videos.

Given the wealth of free chemical information sources appearing in a variety of formats, from blogs to videos to databases, could the ScienceHack model of expert-selected scientific information be the start of a new trend in scientific Internet startups?

Currently, ScienceHack's links point to mostly standard general chemistry demonstrations found on YouTube and Google Video. This could get considerably more interesting if research chemists begin to experiment more with video as a medium for communicating results. Regardless, ScienceHack demonstrates the value of indexing free stuff in science.