Prediction of pKa values using the PM6 semiempirical method
What is it about?
Background Designing new drugs currently involves a lot of trial-and-error, so you have to pay a lot of smart scientists a lot of money for a long time to design new drugs - a cost that is ultimately passed on to you and I as consumers. There are many, many reasons why drug design is so difficult. One of them is that we often don't know fundamental properties of drug-candidates such as the charge of the molecule at a given pH. Obviously, it is hard to figure out whether or how a drug-candidate interact with the body if you don't even know whether it is postive, negative or neutral. It is not too difficult to measure the charge at a given pH, but modern day drug design involves the screening of hundreds of thousands of molecules and it is simply not feasible to measure them all. Besides, you have to make the molecules to do the measurement, which may be a waster of time if it turn out to have the wrong charge. There are several computer programs that can predict the charge at a given pH very quickly but they have been known to fail quite badly from time to time. The main problem it that these programs rely on a database of experimental data and if the molecule of interest doesn't resemble anything in the database this approach will fail. The paper that just got published is a first step towards coming up with an alternative. The New Study We present a "new" method for predicting the charge of a molecule that relies less on experimental data but it fast enough to be of practical use in drug design. The paper shows that the basic approach works reasonably well for small prototypical molecules and we even test one drug-like molecule where one of the commercial programs fail and show that our new method performs better (but not great). However, we have to test this new method for a lot more molecules and in order to do this we need to automate the prediction process, which currently requires some "manual" labor, so this is what we're working on now.
The following have contributed to this page: Jan H Jensen