Please wait while the transcript is being prepared...
0:00
Hi, my name is Dr. Douglass R. Houston.
I'm a senior lecturer at the University of Edinburgh.
My field is biochemistry and
specifically, drug discovery and
especially with a strong emphasis on computational drug discovery.
I'm going to be talking today about
the utility of consensus approaches in virtual drug discovery.
0:26
Before I get to the computational side of drug discovery and in particular,
the methodology of virtual screening,
I thought I'd just give a quick introduction into
the field of high-throughput screening as used
by both academia and
the pharmaceutical industry in the drug discovery field at the moment.
So, you've probably seen a diagram like this before.
It's essentially a pipeline detailing the different steps that you usually have to
go through when performing a drug discovery project and this is state of the art;
it's fairly standard now,
especially in the pharmaceutical industry.
So, you start on the left with target discovery.
So, that essentially is the stage zero where you're actually trying to find
a drug target that ought to be suitable for treating a particular disease.
So, for example, if your disease of interest is a type of cancer,
then an oncogene that encodes an oncoprotein might be a worthwhile target.
There are various computational approaches that can complement target discovery,
but that's not what I'm going to be talking about today.
I'm going to be talking about the computational or virtual versions
of the next step in the pipeline which is lead discovery.
But, this is the step that comes after you've
determined what you hope is a good drug target,
then you have to embark on a series of
experiments to try and find small molecules, hopefully,
drug-like compounds that modulate the activity of the target in
order to give you the effect that you're hoping
for in terms of treating a particular disease.
So, there are a few approaches to this lead discovery step.
A few experimental so-called wet-lab approaches,
we've got listed here:
fragment-based screening, focused libraries,
but the main one, the big one that's usually used,
especially in big pharma is the first one on the list-
high-throughput screening or HTS.
