Intellegens,
a spin-out
from the
University of
Cambridge, has
developed a
unique
Artificial
Intelligence
(AI) method
for training
deep neural
networks from
incomplete
data. The technique, developed at the
Department of
Physics, was
originally
established to
design new
superalloys for
use in jet
engines,
however, the
technique is
generic and has
now been applied
in drug
discovery,
specifically
looking at large
compound /
protein activity
matrices.
Intellegens’
novel machine
learning tool,
AlcemiteTM, adds
value to even
the sparsest
data, increasing
the
understanding
from rare
experimental
results and
reducing the
number of future
investigations
required. Data
sparseness has
long been a
stumbling block
for applications
of AI in
computational
drug discovery
(the ChEMBL
dataset, for
example, is only
0.07% complete),
but Intellegens’
tools can cope
with the data
sparseness and
learn
correlations
between all the
available data.
Intellegens’
AI models,
trained on
sparse data, can
then be used to
impute missing
values in sparse
chemical
databases,
correct errors
and predict the
activities of
new molecules.
AlchemiteTM can
also be used to
guide
experiments to
collect data
that will
maximize the
accuracy of the
model in a
particular
space.
This
approach has
been
commercially
proven with
initial license
agreements in
place.
Intellegens are
now developing a
suite of
software tools
to support the
underlying
algorithms and
address a number
of data analysis
bottlenecks.
Intellegens
are based in
Cambridge and
are interested
in talking to
partners who are
facing sparse
data / deep
learning related
problems.
For
more
information,
visit the
Intellegens
website at here
» or
email Ben
Pellegrini at ben@intellegens.ai
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