nummer 3, april 2008

Figure 3. While ‘chemical space’, which comprises all theoretically synthesizable and stable molecules, is vast, we are able to devise new structures
which are likely to possess the molecular properties of interest by using directed search algorithms such as genetic approaches. In this example, a
recombination operator as well as mutation operators are able to modify a set of molecular structures. By iterating this variation/selection process
the set of molecules gradually is transformed into a set of molecules that fulfil the target specifications and have improved predicted properties as
compared to the initial set.
Future plans - and how you can help us!
Ever since January 2008, with the launch of the
Pharma-IT Platform, Andreas and Michael have
been discussing research interests within the Faculty.
They see as their main goal for the months to come
to identify suitable research partners within Leiden
University including the LUMC as well as in private
companies. Andreas and Michael concluding ‘unisono’:
“We see ourselves as a ‘service station’ in areas
where life science data are analyzed – and with the
combined expertise from the LACDR, LIACS and the
MI we are confident that we are able to understand
the nature of each particular dataset and to apply suitable
solutions to analyze the data in the desired way.
Please don’t hesitate to contact us if you are dealing
with life science data in this regard. Also we are offering
MSc projects for students who possess either a
life science background and who would be interested
in data analysis, or to students with a computer
science or mathematical background who would like
to apply their knowledge to life science data. We are
looking forward to hearing from you.”
Well, why not call or email them if you’re interested?
Have a look at www.pharma-it.net for the coordinates
of both Andreas and Michael.
Figure 4. By employing the Two-Entropy Analysis developed by PhD student Kai Ye, we are
able to analyze the variability (more precisely, the information entropy) of various residues
of G-protein coupled receptors. Analyzed are the information entropy within defined subfamilies
of a receptor family (here, class A GPCRs) as well as the whole dataset of sequences at
that position. We see that, for example, residues in the binding site possess a clearly different
degree of information entropy than residues from other position in the receptors. This can be
used both for functional interpretation as well as for sophisticated feature selection methods
for classification models.
Origin - Universiteit Leiden 23