thank you for having me here my name is
is Harv Aleph and I'm the CTO and
co-founder of atomize and I'm here to
talk to you about the world largest
perspective machine learning validation
in at least in small molecule drug
discovery so I want to start with with
Rosario this is Rosario and he is the
first child diagnosed with opiates in
one syndrome in 2009 when he was just 11
years old
OPH in one syndrome is a rare disorder
characterized by having intellectual
disability and changes to the part of
the brain that control movement and
balance
it's x-linked mutation so it means that
it mainly affect males and there are
less than 30 patients world wild
diagnosed with that syndrome quickly
just how this syndrome is in place a
mutation in the open one gene makes it
lose its function and become inactive
and the OPH and one gene regulates the
cycling of a protein family called Rory
between its active and inactive state
you can see it here and then that
protein family actually regulates
another protein family called rocks and
the level of activity of rock proteins
affect many important cellular pathways
which are involved in hypertension
diabetes stroke just to name few
unfortunately long term overstimulation
of rock proteins also result in
cognitive impairment so it means that
inhibiting the rock protein activity may
alleviate the severity of the opiate and
one syndrome symptoms now because
because occasion one is probably as rare
disease as it possibly gets it means
that the pharmaceutical companies have
very little incentive to work on that
indication
however because rock proteins are
involved in diabetes for example there's
actually
quite a lot of research around those
proteins in the recent years and they
are potent inhibitors for rock one and
rock two proteins in different stages of
development I'm showing two of those
here on the screen so these are great
news for OPH and one patients but
there's obviously still a long way to go
to make these compounds into drugs
even if successful so the question is
what can we do in the meantime and what
we can do is is try to repurpose or off
label an existing approved drug to have
new indication and what do I mean by
that
so all drugs have side effects but
science side effects are beneficial like
think about aspirin we take it for
headaches we take it for heart
conditions two different mechanism two
different systems two different
timescales
I want my my headache to go away in 30
minutes I want to treat my heart
condition for the next 30 years so
whichever we discover first the second
one becomes the side effect or
beneficial side effect and the question
is whether we can identify those in a
principled way rather than just by
chance and why stopping at OPH and one
like there are more than 7,000 rare
neglected diseases in in the world that
we can serve here so frankly there are
two main shot at least two challenges
that comes to mind one as I mentioned is
the the economical challenge like we
need to be able to do discovery cheaply
and the second one is that we need
different expertise in different
therapeutic areas so being an expert in
OPH and one does not make me an expert
in malaria so how do we approach those
challenges well we try to in some sense
democratize the discovery process and to
enable professors richer sir researchers
to do the kind of discovery that they
have not been able to do
before and create new opportunities to
find new treatments so we built a
program we call it aims it stands for
artificial intelligence molecular screen
and it's an open and free program that
lets us collaborate with academics and
nonprofit organizations not just on the
occasion one case obviously and we give
collaborators access to our technology
we predict molecules that are likely to
modulate proteins that involved in in
clinical indications and we evaluate
millions of commercially available
compounds and ship the top ones to our
collaborator plate at QC it ready to be
tested in the lab on all for free this
program is actually in flight already
we've we've done three rounds of
applications this is how it looks like
as you can see like right right from the
start there was huge enthusiasm and and
and you can see that by round three we
actually saw a massive uptick in terms
of number of application number of
researchers and number of institutions
we also opened it in in round three two
applications worldwide so we see more
applicants from outside the US also so
I've been talking about rare diseases
and the incredible need in in that
community but actually this is
resonating with researchers working on
all kind of diseases researchers in
Neurology or oncology or cardiology also
want to have access to early discoveries
and actually the diversity of the
therapeutic areas permit us to scale
because our technology is agnostic to
deter a particular so we can actually
collaborate with with all those
researchers so how how scaling our scan
look like here we see increased number
of applicants from round to round and we
expect that number to grow even further
in incoming rounds were seen returning
researcher returning institution
as well as new applicants from all
around the world in terms of projects
we're accepting we see a similar trend
and we expect it to continue in in the
coming rounds frankly the main limiting
factor in terms of how many projects we
can accept is how efficient we can get
in in running every single project the
more efficient we are the more scalable
we are the more molecules we can
evaluate for the same cost so we need to
build infrastructure to support that
kind of scaling and the problem that
we're working on here are not exactly
the typical problem that you'd see in a
typical SAS company you know I'm right
now the 12th or 11th person who talked
about that but let me quickly compare
the challenges that or the work that
we're doing here to a well-known
well-respected Silicon Valley giant so I
was afraid that some would not know that
but the but anyway think about think
about latency for example no biologist
ever got frustrated if the East grew one
millisecond slower human however may get
impatient right the same applies to
uptime if we run a simulation and it got
terminated or preempted for some reason
no harm no harm is done we can rerun it
again if resources became temporarily
unavailable or expensive we can usually
wait this is for example why we can
heavily utilize spot instances and
capitalize on underutilized computing
capacity it's also interesting to point
out that most of the problems that were
working on are embarrassingly parallel
every simulation between a small
molecule and a protein that we run is
independent from any
small molecule protein simulation that
we're on so the order of the simulations
really matter
scaling however is a different issue so
think about the entire social network of
a company like hooli there are only
seven billion people on the planet and
even if you know we count all of them
and their pets and their secret identity
we'll still end up with several billions
of compound of of people but chemists
can synthesize trillion trillion more or
less ballpark trillion trillion compound
today so we're actually talking about a
scale which is very different than what
tech company have had to handle in the
past and that requires creative new
creative system designs so quickly
I guess that by now nothing is is very
new here like you've seen that kind of
technology stock through out the day
as I said were we're using we're heavily
using spot instances GPU CPUs we're
using services such as sqs lambda and
batch for our distributed queuing
systems and ECS to handle our docker
images and actually we're because we
have so many parallel projects running
in running concurrently then we're using
terraform to programmatically manage on
those those projects and to spin them up
and turn them off on this on the fly so
I've been talking about how we scale our
effort to give you know as many
molecules for free to researcher around
the world and one may argue that you
know losing money on every deal and
making it up in in volume
is not a great business model for a
start-up so what's in it for us
obviously you may ask yourself well
frankly this is the only way we can
convince yourself that the technology
actually works
traditionally the ML approach the
machine learning approach is to
retrospectively test your technology and
benchmarks but apparently this is
something that is very hard to do well
in small molecule drug discovery we
looked at many benchmarks pretty much I
would say all of them and every
benchmark out there has been highly
biased and it's very hard to
retrospectively test the algorithm and
then reliably estimate how well it would
work prospectively in the hands of your
clients so if we want to know whether
the technology works and if we want to
know how well it works we need to run a
large-scale diverse statistically
powered prospective program anecdotes
are just not enough it's just it's the
only reliable way I believe to do it in
this domain fortunately enough this is a
win-win situation to both sides we have
hundreds of those experiments in flight
and we'll have the empirical evidence of
how well the technology works
so I started D I started this talk with
the occasion bond syndrome project so I
want to close with that this is a
project we've done in collaboration with
the O page in one foundation in Merck we
compiled a library of about 7,000 drugs
and drug candidates so that was our
repurposing project and we ran
predictions against rock 1 and rock two
proteins we shipped our collaborator 27
compounds out of which 7 showed
activities and I'm showing here on the
screen the most important ones a net nib
whose only threefold difference in
potency from the control compound as
some of you may know
selenium is and oncology drug so it the
bad news here is that those kind of
drugs usually have unfavorable toxicity
profile and this is something that you
would not want to to take for you know
for a chronic condition so but there's a
ray of hope here because OPH and one is
mainly targeted to children like and
maybe there's going to be a way to find
a dose at which the drugs still going to
be effective but the side effects are
going to be mild and this is what our
collaborators are now looking into not
just for soon with me but for the other
six compounds so this is just an
anecdote but you know the aims program
itself is working in practice and we
have dozens of these experiment in
flight and we have some results already
so with that I would like to thank to
the people who actually did the work
here so Adrienne in khong or the
modelers who ran the predictions from
our side actually this project was
Adrienne's first project that autumn
wise it pretty cool Sarah who's in
charge of all that massive
collaborations to Ames Greg Matthew and
Pierre who built the infrastructure to
support that kind of scaling and David
Rosen from Merck and the O page and one
foundation who ran the experiment and
the whole autumn Weiss team that made it
possible
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you
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