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My name is Dr. Wayne Carter.
This lecture is entitled
in silico selection.
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Molecular modelling.
Molecular modelling
provides a means to screen
drug-target interactions
but without
the need for chemical synthesis,
hence the rapidity of
molecular modelling
reduces the time and cost of
new chemical synthesis and
its associated testing.
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Molecular modelling benefits.
A ligand structure can
be adapted to model
an improved fit and
potential drug potency.
Not least, the effects of
chemical modification and
generation of structural
analogues can also be predicted.
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Molecular predictions.
In silico predictions
facilitate the prediction of
chemical properties that
influence drug-target
binding affinity.
They also facilitate
theoretical adjustments
to drug physiochemical
properties, such as
the drug's permeability and
also its lipophilicity.
This will influence its
potential diffusion
across cell lipid bilayers.
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Molecular predictions.
Drug synthesis will
need to consider
the production of enantiomers.
That is, mirror images
of the same molecule.
For example, as shown here,
we have the R form of
bromochlorofluoromethane
and the S form,
and they're mirror images
down this dotted line
as shown on the slide.
Amino acids themselves
produced in nature
all have chirality
except for glycine.
They are often D-amino acids,
and there are often L-sugars
produced in nature.
So the chirality
of the drug and/or
its target will
influence binding,
but these can be
modelled in silico.
