A casual reader my not know the background of the title of my blog. A bit over\nfive years ago, when I started this blog, I defined chemblaics :
\n\n\n\n\nChemblaics (pronounced chem-bla-ics) is the science that uses computers to address and possibly\nsolve problems in the area of chemistry, biochemistry and related fields. The general denomiter\nseems to be molecules, but I might be wrong there. The big difference between chemblaics and\nareas as cheminformatics, chemoinformatics, chemometrics, proteochemometrics, etc, is that\nchemblaic only uses open source software, making experimental results reproducable and validatable.\nAnd this is a big difference with how research in these areas is now often done.
\n
Later, I also identified molecular chemometrics (doi:10.1080/10408340600969601)\nwhen I reviewed important innovation in the field, which has, IMHO, a strong\noverlap with chemblaics. Any reader of my blog will understand that I see semantic\ntechnologies play a very important role here, as Open Standard for communication\nOpen Data between the bench chemist and the data analyst using Open Source allowing\nothers to reproduce, validate, and extend that work. Some have identified the\npossibilities the internet brings over 10 years ago, while the use of semantic\ncomputing goes back even further.
\n\nAnd what have the big publishers done? Nothing much yet. Not the old, not the\nnew. There are project ongoing, and there is a tendency (BioMed Central starts\ndelivering Open Data, Beilstein Institute spits RDF for a few years now, Royal\nSociety of Chemistry has Project Prospect), but most publishers are too late,\nand not investing enough with respect to their yearly turn over. This is particularly\nclear if you realize that citizen and hobby scientists can innovate publishing\nmore effectively than those projects (really!). Anyway, I do not want to talk\nabout publishing now, but it is just so relevant: I am not a publisher, but\npublications are the primary source of knowledge (not implying at all that I\nthink that is the best way; it is not).
\n\nInstead, I am a data analyst, a chemometrician, a statistician, a cheminformatics\ndude, or a (pharmaceutical) bioinformatician, depending on your field of expertise.\nReally, I am a chemblaics guy: I apply and develop informatics and statistics\nmethods to understand chemistry (and biology) better.
\n\nDuring my PhD it became painfully clear that current science is horribly failing,\nin many ways:
\n\nThere are likely several more, but these three are the most important to me\nright now.
\n\nSo, in the past years after finishing my PhD research which was on data mining\nand modeling molecular data, I have spend much of my time on improving methods\nin chem- and bioinformatics to handle. Hardly anyone else was doing it (several\nBlue Obelisk community un-members as prominent exceptions), but someone has.
\n\nWhy I have been doing this? Well, without good, curated data sources it is impossible\nto decide why my (or others) predictive models are not working (as good as we\nwant them to be). Is this relevant? I would say so, yes! The field is riddled\nwith irreproducible studies of which one has no clue how useful they are. Trust\nthe authors who wrote the paper? No, thank you, but I rather verify: I am a\nscientist and not a cleric. Weirdly, one would have expected this to be the\ndefault in cheminformatics, where most stuff is electronic and reproducing results\nshould be cheap. Well, another fail for science, I guess.
\n\nSo, that explains why I have not recently done so much in chemometrics. Will\nI return? Surely! Right about now. There is already a paper in press where\nwe link the semantic web to (proteo)chemometrics, and more is to follow soon.
\n\nOne example, interestingly, is pKa prediction, which has seen quite a few publications\nrecently, yet experimental pKa data is not available as Open Data. Why?? Let\nme know if you have any clue. Yet, pKa prediction seems to be important to drug\ndiscovery, as it gets an awful lot of attention (400+ papers in the past 10\nyear, of which 50+ in 2010!). But this is about to change. Samuel and I are\nfinishing a project that greatly simplifies knowledge aggregation and curation,\nas input to statistical modeling. We now have the tools ready to do this fast\nand efficiently. Right now, I am entering curated data at a speed of about 3\nchemical structures a minute. That means, given I need a break now and then,\nthat I enter create a data set of reasonable size in a few days. Crowd-sourcing\nthis, the a small community can liberate data from literature in a few days.
\n\nThis will have a huge impact on the cheminformatics and QSAR communities. They\nwill no longer have any excuse for making their data not available. There is\nno argument anymore that curation is expensive. This will also have a huge impact\non cheminformatics and chemical data vendors. Where Open Source only had moderate\nimpact so far (several software vendors have already joined the Open Source\ncheminformatics community), this will force them to rethink their business model.\nWhere they could hide behind curation where it came to text mining initiatives\n(like Oscar, on which I am currently working), with cheap, expert curation knowledge\nbuilding at hand, they will be forced to rethink their added value.
\n\nThe impact on the CDK should be clear too. We no longer depend on published\nmodels for ALogP, XLogP, pKa, etc, predictions. Within a year, you can expect\nthe CDK project to release the tools to train your own models, and make choices\nsuitable for your user base. For example, you can make more precise models around\nthe structures your lab works on, or more generic models with large screening\nprojects. Importantly, the community will provide an Open Data knowledge base\nto start from. Using our Open Standards, you can plug in your own confidential\ndata and make mixed, targeted models.
\n\nIs this possible with the cheminformatics of the past 30 years? No, and that’s\nthe reason why I have been away from chemometrics for a while.
\n\n