AI-Driven Molecular De-Extinction: A New Frontier in Combating Drug-Resistant Pathogens

Researchers
are
leveraging
artificial
intelligence
(AI)
to
mine
the
DNA
of
long-extinct
species,
such
as
woolly
mammoths
and
giant
sloths,
to
uncover
genomic
secrets
that
could
help
combat
today’s
most
infectious
pathogens,
according
to

NVIDIA
Technical
Blog.

Addressing
a
Growing
Crisis

Every
year,
more
than
1.25
million
people
worldwide
die
from
infections
that
are
resistant
to
current
drugs
like
antibiotics,
as
reported
by
the
World
Health
Organization
(WHO).
This
number
is
projected
to
rise
to
10
million
by
2050.
Additionally,
within
six
years,
around
24
million
people
could
be
pushed
into
extreme
poverty
due
to
the
costs
associated
with
treating
infectious
diseases.

AI
and
Molecular
De-Extinction

Dr.
Cesar
de
la
Fuente,
a
professor
at
the
University
of
Pennsylvania,
is
leading
a
team
of
researchers
to
use
AI
in
a
process
they
call
“molecular
de-extinction.”
This
technique,
detailed
in
a

paper
published
in

Nature
Biomedical
Engineering
in
June
2024,
aims
to
identify
novel
solutions
to
dangerous
drug-resistant
microbes
by
analyzing
DNA
from
extinct
species.

“Exploring
and
comparing
molecules
throughout
evolution
can
unlock
new
biological
insights,”
Dr.
de
la
Fuente
explained.
“Our
AI-driven
molecular
de-extinction
work
allows
us
to
bring
back
molecules
from
the
past
to
address
contemporary
challenges.”

Advanced
Computational
Techniques

Using
a
cluster
of

NVIDIA
A100
GPUs,
Dr.
de
la
Fuente
and
his
team
trained
deep
learning
models
to
mine
the
proteomes
of
both
living
and
extinct
species.
The
scientists
hypothesized
that
pathogens,
which
have
adapted
to
modern-day
drugs,
might
be
vulnerable
to
antimicrobial
defenses
found
in
ancient
genomes.

The
team
trained
40
variants
of
deep
learning
models,
named
APEX,
on
DNA
extracted
from
fossils
of
extinct
animals
and
plants.
These
included
species
such
as
extinct
bears,
penguins,
and
woolly
mammoths.
The
training
utilized
a
combination
of
988
in-house
created
peptides
and
thousands
of
publicly
available
antimicrobial
peptides
(AMPs)
and
non-AMPs.

The
models,
trained
using
the
cuDNN-accelerated
PyTorch
framework
with
a
single
NVIDIA
A100
GPU,
predicted
encrypted
peptide
sequences—protein
fragments
that
immune
systems
use
to
fight
infections.
APEX
predicted
over
37,000
peptide
sequences
with
antimicrobial
potentials,
11,000
of
which
were
not
found
in
living
organisms.

Laboratory
Successes

From
the
APEX-generated
peptides,
the
researchers
synthesized
69
potential
antibiotics.
In
lab
tests,
mice
infected
with
a
bacterial
pathogen
commonly
found
in
human
burn
victims
were
treated
with
these
ancient
peptides.
The
results
were
promising;
the
experimental
antibiotic
derived
from
giant
sloths,
named
mylodonin-2,
showed
significant
improvement
in
the
health
of
the
mice
within
two
days,
comparable
to
those
treated
with
the
common
antibiotic
Polymyxin.

“Exploring
extinct
organisms
allows
us
to
access
a
vast
array
of
molecules
that
contemporary
pathogens
have
never
encountered,”
Dr.
de
la
Fuente
said.
“Molecular
de-extinction
can
provide
a
new
arsenal
of
compounds
to
combat
antimicrobial
resistance,
one
of
humanity’s
greatest
threats.”

Future
Prospects

The
researchers
noted
that
the
de-extincted
antimicrobial
molecules
attack
microbes
by
depolarizing
the
inner
membrane
of
a
pathogen’s
cells,
a
mechanism
different
from
most
known
antimicrobial
peptides.
This
innovative
approach,
made
possible
by
advancements
in
AI
and
GPU
technology,
seems
almost
like
a
plot
from
a
Michael
Crichton
novel.

Dr.
de
la
Fuente
believes
that
generative
AI
holds
the
potential
to
revolutionize
drug
discovery
methods,
reducing
both
the
cost
and
time
required
for
developing
new
antibacterial
drugs.
Traditional
methods
can
take
up
to
15
years
and
cost
over
$1
billion,
but
AI-driven
approaches
can
significantly
shorten
these
timelines.

“GPUs
are
transforming
how
we
do
our
work
in
our
lab,”
Dr.
de
la
Fuente
said.
“We
can
accomplish
in
a
few
hours
what
used
to
take
six
years
of
research.
This
has
enabled
us
to
dramatically
accelerate
antibiotic
discovery.
It’s
like
bringing
science
fiction
into
reality.”

Dr.
de
la
Fuente
is
in
the
early
stages
of
setting
up
a
company
to
commercialize
the
most
promising
antimicrobial
drugs
discovered
by
his
research
team.
The
Machine
Biology
Group
continues
to
explore
promising
antimicrobial
peptides
using
their
APEX
models.
Their
work
is
open
source
and
available
on

GitHub.

For
more
detailed
information,
readers
can
review
the

Nature
paper
and
other

publications
from
Dr.
de
la
Fuente’s
lab.

Image
source:
Shutterstock