Data science and pharma, that's our discussion
for today on CXOTalk.
Bulent Kiziltan, tell us about your work.
I have been advising companies on how to leverage
machine learning and build data science use
cases for best outcomes, as you mentioned,
multiple domains that include pharma and biotech.
Set the stage for us.
What are we actually talking about?
Pharma is big business.
Big companies that are trying to survive in
that space.
It's very competitive from a business perspective
but also the public health impact of how pharma
companies are conducting their R&D and how
they're investing their efforts has a huge
impact.
When we look at the drug discovery and production
costs, it has been skyrocketing over the last
decade or so.
It's gone up from approximately $800 million
per drug to develop from end-to-end.
That's the number back from 2001.
In 2016, the projected number was approximately
$2.9 billion.
When we look at the projections, it seems
it's going to be unsustainable.
Pharma companies are looking for ways to cut
down the costs and coming up with new, innovative
approaches to drug discovery to continue to
be relevant and sustain their impact.
AI and data science in drug discovery, basically
it's like every other industry; they're trying
to do it faster, do it better, and do it cheaper.
Right, as a quick summary?
Certainly, it's one dimension of that equation.
I think it's even more important than that.
Pharma companies, if they don't come up with
means to cut down the costs, they will not
be able to survive.
They're going to maybe turn into another Nokia
if they are not using the most innovative
approaches.
Whereas, some other companies, they can optimize
certain procedures and increase revenue.
There's certainly ROI in investing in data
science, but I think pharma is at a critical
spot and the challenge is monumental.
I'm assuming then that using data science
and AI techniques in pharma must be pretty
far along since you're describing it as existentially
important.
I think it's an existential point of investment
but, to the contrary of what you just said,
I think the data science and machine learning
investment in pharma is in its infancy.
They're just learning to crawl in that space,
mainly because pharma companies had to really
reinvent how they are going about R&D, how
they're implementing the results from their
research and development into their pipelines.
You cannot have and remain the same with the
same infrastructure and expect innovation
to happen.
I think both culturally and from an infrastructure
perspective, pharma companies are very big.
There's a lot of inertia internally.
Adapting data science and going beyond a service
provider internally to a critical stakeholder
in the decision-making process takes time.
Can you elaborate?
When you say, "Make an evolution from being
a service provider to a critical stakeholder
and decision-maker," what does that mean?
This is a generic problem in many businesses
as well as pharma and in pharma where the
risks are very high both financially and strategically.
Traditionally, pharma has been very territorial.
Back in the days when data science was not
on the map and there were statisticians or
people who do informatics, they were providing
services to other stakeholders, to the main
stakeholders that were driving the business
and drug discovery pipelines.
They are currently being replaced by data
science teams by just changing the label.
That works to a certain extent for short-term
gains, but I think pharma companies realize
that keeping them siloed and not fully integrated
into the decision making process at the very
top will not work in the long-term.
I think that transformation is taking place
as we speak in big pharma.
What are some of the applications, some of
the domain areas within pharma that data science
and AI seems particularly well suited for?
Data science has the potential to make an
impact in all operational pipelines, both
in pharma and in other businesses.
So far, in pharma, interestingly, data science
has already made an impact in optimizing the
clinical trials pipeline, which essentially
cuts down the cost.
The projections are, with just implementing
rudimentary machine learning and data science
into the clinical trials process can and has
been cutting down the costs up to 20%.
This is very significant.
The clinical trial process has been using
machine learning effectively and it's going
to be even more effective as the years go
by.
Also, pharma is trying to make financial predictions
to understand the potential market impact
of any drug they're trying to discover or
work on.
Machine learning has provided very powerful,
predictive machines that give pharma companies
powerful predictions about finances.
It has been also showing promise in that space.
Then the main areas where pharma companies
are trying to improve the drug discovery process,
I think, data science implementation is still
in its infancy.
There are new technologies coming up from
the data science domain that is currently
being discussed and implemented the drug discovery
process, but we are yet to see a drug discovery
that is being done or empowered by deep learning
or data science, in general.
Why is that?
What makes this so difficult?
There are multiple dimensions to this question.
One of them is, data science is an up-and-coming
discipline.
It's still not mature enough with its methodologies.
The domain is changing and reinventing itself,
transforming every six months.
For instance, this year, graph learning and
graph theory is being used in data science
and deep learning.
One of the promises of graph learning and
deep learning is drug discovery.
On the pharma side of things, data plays an
important role in any type of data science
operations and use case.
The data has not been clean and not well integrated,
so pharma companies are working very hard
to aggregate their data, clean them up as
much as possible, and add public information
to the data that they have to increase the
predictive power of data science.
The data, not aggregated data play an important
role in moving forward.
Also, the integration internally in pharma
companies.
Data science is a fully collaborative effort.
As I mentioned, traditionally, pharma has
been very siloed and very territorial.
Unless there is an empowerment from the very
top pushing the stakeholders to fully cooperate
and approach this problem and frame it as,
"How can we achieve what we want to achieve
together?"
rather than, "Provide me with those insights
and I will do my end of things and I will
do my job," I think are critical.
The cultural transformation, the data transformation,
and also the know-how that's being improved
as years go on in data science, they all play
a role in moving slowing in that space.
But also, there is a justified skepticism
that I want to touch upon.
Traditionally, again, pharma has been very
siloed.
The leadership that is leading data science
efforts were not essentially data science
domain experts.
They were purely driven by their traditional
ways of thinking of business and that sort
of thinking has shown not to work with data
science operations.
That is also quickly transforming itself.
We have a few questions from Twitter.
Sal Rasa asks, "What kind of culture change
is required to connect the intention with
the outcomes for the organizations that you
are involved with?
Another way of saying it is, what are the
cultural disconnects that interfere with the
use of data science and AI techniques for
drug discovery in pharma?
Data science is a collaborative effort.
All the stakeholders have to be organically
integrated and work for the same cause.
One of the challenges of big companies and
especially in pharma is that the stakeholders
have been siloed for a very long time, for
many decades, and they have been very territorial.
That mindset has to change.
Also, for data science to make an impact,
they have to be critical stakeholders and
they have to sit at the decision-making table
rather than just being service providers.
That is also changing.
Once those two areas have been transformed
culturally and internally, I think then the
next step is to find the right leader and
the right talent, assuming that the data is
in place.
I think, yes, we are expecting a huge impact
in the pharma space, but I think that's going
to take a few more years.
What you're saying is, there's a kind of deep
cultural divide between the way pharma has
operated historically and the way data science
must operate.
Can you explain a little bit about the collaborative
nature of data science and why it's so important
in this context?
There's a flip side of the coin where people
are moving into data science have very strong
academic backgrounds and they don't have,
essentially, the optimized thinking of business
folks.
On the business side of things, things are
very structured and the KPIs, the key performance
metrics, don't necessarily always speak to
data scientists.
It's essential to find a leader or translators
to translate what the business side of things
is expecting from the data science teams and
also a person that can translate what can
and cannot be done to prevent overpromising
to the businesspeople to essentially prevent
over-hyping what data science can deliver.
I think speaking the same language is essential
and there are very few leaders who can successfully
do that.
We have another question from Twitter.
Arsalan Khan asks, "If pharma is using data
science for clinical trials, then it would
also be useful to regulators such as the FDA.
Has there been a push by the government to
get data science involved in this as well?"
That's a very good point.
We've seen that in the healthcare industry
for regulators.
We see the same thing in the financial world
where the regulators were really behind the
curve and the transformation, essentially,
that AI is bringing into the domain.
In FDA, I know of efforts where the teams
are being transformed to implement some of
the new technologies that are up-and-coming
and how they can be implemented in a healthy
manner into the whole clinical trials pipeline.
There was a lot of resistance to machine learning
algorithms by the regulators.
That has been changing and FDA and other government
regulators are trying to transform themselves
as well.
Zachary Jeans asks a really interesting question.
He said, "What does the average person get
wrong about what data science is and its use
in drug discovery?"
The misconceptions that non-scientists have
about this, help us understand that.
I think the perception is, you give data to
a machine learning algorithm and it spits
out the final product.
That's not how machine learning works.
Machine learning is incrementally implemented
into the whole process, including clinical
trials.
But the drug discovery itself and the holy
grail of drug discovery using machine learning
is to make predictions on the therapeutics
side of things with machine learning just
by going into the compound libraries that
pharmaceutical companies have and pipe in
the clinical trials data, both the successful
ones and the failures in the past, and ask
the machine learning algorithm to spit out
something that's predictive and useful in
nature.
That has not happened yet.
Maybe the layman understanding of machine
learning is this and it might happen in the
future, but we're not there yet.
You mentioned that one of the obstacles is
the lack of data and the lack of normalized
and prepared data.
Can you give us some insight into what kind
of data you're describing?
There are clinical trials data that are all
over the place.
They are not standardized across even within
a single pharma company because different
stakeholders create their own data.
They used to create their own data and they
were not normalized.
Also, because data scientists or machine learning
experts have not been a critical part of the
decision-making process in the past, the type
of data that is available, even if they were
normalized and clean, might not be sufficient
to extract the type of information that we're
trying extract.
Right now, with the proliferation of different
techniques, sensors that we can put on the
people who are a part of the clinical trials
and continuously monitor them, not doing the
trials only but after when they go home, I
think that's the sort of data that we get
from sensors that we can attach to individuals
will be very powerful in the future.
That sort of information is currently not
available.
They are being implemented currently but,
in order for machine learning algorithms to
make predictions, we need data going back
a few years.
We don't have that yet.
Is data the primary or lack of data the primary
obstacle to broader adoption of AI and data
science techniques in drug discovery, is it
the set of cultural issues you were describing
or is it a glomeration of both together?
I will give you a third option.
Both play an important role, but also our
understanding of human physiology and disease
is very primitive.
Because I'm a physicist, I can tell you, in
physics, we have a fundamental understanding
of the laws that produce the universe that
we see.
But in biology, the interactions of proteins
and how disease happens to be at the physiology,
the fundamental understanding is not there
yet.
This is both a pro and a con, how data science
can make an impact.
It's a limitation that we have because we
don't have the fundamentals, but this is again
one of the primary reasons why data science
can make and will make an impact because we
can do data-driven predictions even though
we don't know how the fundamentals work.
I think the disciplinary ignorance, if you
will, the lack of deep information, I think,
is one of the primary bottlenecks that prevent
us from producing drugs that are going to
be impactful.
Can you give us an example of a situation
where it's worked, where data science has
supported machine learning, has supported
drug discovery?
For pharma companies, I think the primary
objective right now is to build their infrastructure
and be ready when the technology is ready
and it can be scaled.
Startups, because they are very focused, they
are agile, they're dynamic, they don't have
the cultural divide, everybody is working
to achieve a single goal, there have been
startups that have shown to produce predictions
about molecular structure in three dimensions
purely driven by data and purely produced
in the computer and make those predictions
about molecules.
Once those molecules are produced and tested
in the lab, they show efficacy that is being
predicted.
I don't want to name names, but there are
companies that have shown and published their
work.
Recently, Google DeepMind also published one
of their tools that are called AlphaFold,
which uses deep learning approaches to make
predictions about the three-dimensional structure
of proteins.
It will be a very active domain within drug
discovery and pharma.
I am expecting that there will be a breakthrough
in the coming year.
What you're saying is—correct me if I'm
wrong—it seems like a good idea but not
yet.
It depends on who is saying it's not a good
idea.
I think, for pharma companies, I think it's
essential, critical to invest into that domain
even though I predict, for the coming year
or two, pharma companies will end up buying
the solutions that startups have produced,
mainly because it's much cheaper that way.
Startups can move much quicker in their drug
development efforts, currently.
I think it's essential for pharma companies
to be ready and have a scalable infrastructure
and talent pool when the time comes.
There is a big opportunity cost if they don't
do so.
Also for startup companies, I think moving
quickly and attracting top talent, being culturally
viable for data science is one of their advantages.
But they have a big disadvantage.
They don't have the data they require.
So, I think it's also essential to build collaborative
relationships between big pharma and startups
to alleviate that problem.
Are there any specific examples of that that
you can point to that you're comfortable talking
about?
For instance, one of the startups that have
proven that their predictions show promise,
they have partnered with big pharma to use
their compound libraries to inform their deep
learning and machine learning algorithms to
make further predictions.
Obviously, the predictions that they're making
are not public yet, but I would assume that
they're making progress in that sense.
Give us examples of where the use of data
science and machine learning has not met expectations
or failed.
Give us some insight, at the same time, as
to what went wrong.
One reason data science has produced some
justified skepticism in many domains, including
pharma, is the leaders of that domain, as
I said, they were essentially not domain experts.
They had maybe unrealistic expectations of
what data science can do today and were projecting
for the future.
Data science has yet to produce therapeutics
and medicines that can be used in cell therapy,
in gene therapy.
Those are areas where pharma is growing mainly
because data science has to offer new insights
in those domains.
Until now, we haven't seen data science-driven
insights into drug discovery except a few
startups that have shown some promise in that
area.
But we don't have a single drug yet that has
been predicted and has gone through the pipeline
to produce a drug that's viable and applicable.
You know we have to consider also that data
science is not the only part to blame here.
In regular drug discovery or drug development,
1 out of 20 drugs that go through the pipeline
is successful.
There is a very high attrition rate anyway
in the standard approach, so data science
is promising to increase those odds.
Given the challenges, why do you say that
data science is an existential necessity for
pharma?
As I just pointed to the cost of drug discovery.
Right now, I think it's past $3 billion per
drug to be produced and go to the market.
The projections show it's going to hit $5
billion in the coming years.
That projection, that cost is just not sustainable.
Either the price will go up to a level where
it will not make sense for pharma companies
to invest into them, which essentially will
make their portfolio even smaller and the
revenues will go down, or they have to come
up with new ways.
Data science and AI is potentially the only
way that we know of right now to cut down
the cost to optimize the whole process and
even come up with new insights just given
that pharma companies have huge compound libraries
that scientists have not been able to effectively
tap into to produce insights in the past.
I think it won't be farfetched to predict
that AI and data science machine learning
will produce new insights.
We just don't know the level of impact it's
going to have in the future, but I'm optimistic.
What's the timeframe, do you think, that we
will actually see some type of material result
as opposed to the, shall we say, theory today
that it seems like a good idea?
Data science is already playing a role in
cutting down the costs.
Also, producing new insights, making the clinical
trials process more effective, especially
doing patient enrollment that produce data
that will produce information that's more
useful for the data science process.
I would expect, since the data is already
coming in over the past year or two, I would
expect a drug or therapeutics to come out
that has been largely influenced and empowered
by data science processes, but that doesn't
mean that data science and machine learning
is yet powerful enough and implemented well
enough to make predictions for a three-dimensional
structure and to produce therapeutics from
end-to-end.
I think that is the holy grail to be able
to simulate things in the computer and produce
information and empower the people who are
developing the drugs from end-to-end, which
will primarily drive down the costs significantly,
which will make therapeutics accessible to
people who have rare diseases.
It was not financially viable for pharma companies
to invest into what's called orphaned diseases,
diseases that not more than 200,000 people
in the U.S. are suffering, but they are significant.
Now, data science processes will make those
drug development efforts viable and possible
for people with genetic disorders and mutations.
It's one of the areas in which pharma companies
are currently investing in cell therapeutics
and gene therapeutics.
We have another question from Twitter.
Arsalan Khan asks a great question having
to do with the data.
"Given the fact that the data is so important,
is this different from any other area, type
of domain, where we have to gather and aggregate
large amounts of data?
In addition, specifically in drug discovery,
are there perception and bias issues that
are obstacles to progress and getting the
results that we want?"
Yes, aggregate data is very important in all
domains.
The problem in pharma is that the risks are
high.
You cannot just probabilistically make a prediction,
see, and test it on the ground to get results
and then basically produce that drug.
The stakes are very high.
It's not like a marketing effort where you
can produce a model that's based on probabilities
that you're producing and then you can iterate
in the market in real-time to inform your
algorithms to perfect them.
That's very difficult even in clinical trials
because there are certain strict regulations
that regulate what you can and cannot do.
You have to be very transparent.
You have to have very finely sampled data
rather than just granular data.
You cannot aggregate data and average out
certain aspects of that data, which you can
really do in marketing data and some other
domains.
The risks are very high in pharma.
To the second part of the question about biases,
yes, bias exists everywhere because we humans
are biased.
Our biases are reflected into the data.
There has been an ongoing discussion of whether
machine learning algorithms are biased.
I would argue that the algorithms reflect
the bias that's in the data.
Certainly, that bias also exists in the pharma
space.
We have certain methodologies to overcome
those biases, but it's an ongoing effort by
all parties, both in academia and in the industry.
James McGovern asks, "Any thoughts on how
enterprise architecture adds value, in general,
to data science, but if there are specifics
that you can talk about in pharma, that would
be great."
It's one of the most critical components of
data science operations, I think.
One of the reasons why data science has been
slow in delivering and why the data science
efforts have been hampered is mainly because
IT and the enterprise architecture has not
been incentivized to keep up with technology
in the past.
Going into an infrastructure that is not up
to date, that doesn't use the latest technology,
and does not speak organically with the data
science efforts was one of the problems and
has been one of the problems in larger companies.
In order for companies to move forward and
produce the value that data science is promising,
quickly, I think an effort that is company-wide
where all stakeholders are on board is essential.
The specifics of what type of enterprise architecture
is to be used is very domain-specific.
It's very specific to the business objectives
of the company, the timelines, the resources,
and the talent pool.
I think that there are a lot of solutions
in the market which can be customized to the
company to make sure that data science can
deliver today.
We have another question from Twitter.
This is from the @CXOTalk account.
"What part of the drug discovery process do
you see data science making the most significant
contributions in drug discovery?"
I think the frontier today is to make predictions
on the three-dimensional structures of molecules
and compounds with the computer using machine
learning algorithms.
Currently, those are all done manually with
robots, but they are basically manually mixing
certain components and trying to see the effect
on target molecules.
This is, again, done manually.
I think the frontier is where machine learning
can go into the compound library and know
what the target is and make predictions based
on previous trials.
I think this is where machine learning can
and will make an impact, but it will take
time.
Talk about the collaborative nature of data
science and drill down and compare that to
the siloed nature of historically the way
pharma companies work.
Coming from academia, the culture is, unfortunately,
not something that we have always talked about.
But as we move into the business and how things
can operate and produce value, culture comes
up at the top of our list all the time in
many of the use cases that we are working
on.
Culturally, the business mindset expects certain
outcomes and they are very rigid in their
thinking.
This is the traditional way of approaching
things.
Also, in pharma, different silos and stakeholders,
they want certain results and they are used
to getting certain results.
Data science, by nature, has a significant
exploratory component to it.
I think this is not being appreciated by many
business stakeholders.
If you keep a rigid project management pipeline
and expect certain outcomes without redefining
how you operate, it will be very difficult
for data scientists to use their creative
capability to contribute to the operations
and to the value that data science can produce.
There are two outcomes to this.
One outcome is in cases where businesspeople
who have been at the company are strong stakeholders.
They will push the data science teams to keep
producing the rigid outcomes that they're
used to and it will limit the production and
the creative contribution a data science team
can do.
Whereas, companies who are on the way of transforming
and redefining their culture, sitting down
with the data science leadership, having a
dynamic interaction, producing the key performance
metrics together, and to keep it dynamic,
I think, is essential because data science,
as I said, is very exploratory in nature.
This is why some of the project management
styles that people have been trying to implement
into data science simply does not work because
the outcomes are not well defined.
Whereas, you can borrow some of the ideas
of project management such as Agile and some
other project management styles and implement
it but not as is.
If companies think of data science as a software
engineering project, it will limit, significantly,
their capability.
For stakeholders that are a part of the discussion,
I would advise them to not be so rigid and
try to have a two-way dialog rather than asking
data science teams to produce certain outcomes
that they're used to having.
You mentioned earlier the importance of large
pharma companies working with startups.
The larger companies have access to data and
processes.
The smaller companies have access to more
advanced technical approaches.
Once you start mixing those two, don't you
layer onto it another whole set of very difficult
cultural and economic challenges?
That's correct.
One of the solutions some of the companies
have applied was to build an internal group
that is somewhat isolated from the rest of
the stakeholders internally, essentially acting
like an internal startup.
This is one way to solve some of the problems,
potential problems, that you just mentioned.
I think the traditional approach nowadays
is to collaborate and build relationships
with startups.
It really depends on how that relationship
is being structured.
If you have an agreement with a startup but
have that startup act like an internal stakeholder,
I think it won't solve some of the problems.
Whereas, you can really build a relationship
with a startup that isolates the startup as
a separate entity but reports only to the
top and then, organically, builds a very flat
structure with some of the teams that can
complement to the efforts, I think, is the
way to move forward.
I think companies are eager to build that
sort of relationship.
I've seen similar relationships being built
in the industry.
From this standpoint, there's really not much
of a difference from looking at innovation
approaches across different kinds of industries.
The pharma space is not unique from that innovation
standpoint of partnering with startups and
trying various things to make it work out
successfully for both sides.
There are unique aspects to it because pharma
is also very siloed, as I mentioned.
That brings a certain level of secrecy.
Unless that trust has been built, which takes
some time and sometimes years to build, the
lack of transparency hampers those efforts.
Once that relationship has been built, I think
the companies and the stakeholders can move
beyond that secrecy and share information
transparency.
It's one of the problems building those long-lasting
relationships that are based on trust.
Is the secrecy aspect in drug discovery really
one of the most important obstacles to that
innovation partnership?
I wouldn't say it's one of the most important
aspects, but it definitely contributes or
hampers the effort of building those relationships.
When people and companies sit down at the
table and discuss how they can contribute
to their efforts and how mutually they can
benefit from that relationship, it typically
takes several months to a year to move through
bureaucracy and make sure that the data, the
challenges, and the fiscal priorities are
being shared transparently.
To what extent do large pharma companies have
an interest in deploying innovative processes
that disrupt the established organization
and disrupt established executive positions
and, potentially, compensation?
This is a generic problem.
It's not just drug discovery, it seems like.
How big an issue is it?
I think it's a very big issue.
There is a high risk that comes with cultural
transformation.
There comes a high risk when it comes to changing
the status quo internally.
What we see is, when you come as a stakeholder
or leader in one of the branches within the
company, to have that vision but you are not
empowered or you don't share that vision with
the board, the CEO, or the CTO, what we see
is it never happens.
It never matures to a level where it can be
implemented and executed on.
I think companies that really have decided
at the board level to drive that cultural
transformation, which has to go in parallel
with the digital transformation, change their
leadership from the very top.
They change their culture and they make sure
that it precipitates down to the stakeholders,
which will take time, yes.
But I think the first and foremost transformational
point is at the very top.
Unless you have a board or leadership that
drives that transformation, it will never
happen from the bottom up.
Kanupriya Agarwal asks, "How much funding
do you anticipate being set aside for AI and
data science in drug discovery in the near
future?
What do you see the dynamics at play here?"
It's a difficult one to answer because each
company is making its own decisions.
I think the trend moves in a direction where
a significant amount of money will go into
internal investment.
But also, I think, at least in the short-term,
because pharma companies won't be able to
complete that digital transformation and cultural
transformation in the short-term, they will
continue to buy, partner, or invest in startups.
I think there will be a significant budget.
Obviously, many billions of dollars that go
into external partnerships and purchases to
expand their portfolio in the target areas.
But also, internally, I think it will be more
incremental.
But I think, in order to build capabilities
that are scalable, it takes a long time and
companies know that they cannot just wait,
not invest internally, and expect to buy out
startups.
Two years from now, when the technology is
mature enough, they cannot just hire people
and then do it internally, which will obviously
be the choice to make.
I think incrementally building that internal
know-how and capability while they diversify
their risk is the way to move forward.
It's the trend that we see.
In your view, there's an opportunity for startups
who are able to gain success with this to
be acquired by larger pharma companies going
forward.
It's the right time to invest in startups
that build therapeutics in different domains
in gene therapy, in proteins, in oncology,
and in different kinds of therapeutics and
drug discovery.
I think it's a very good time to invest in
those companies.
Data, however, is going to have to come from
the large companies, or is that not correct?
Do startups have a chance of developing their
own data?
It'll depend on the company and what exactly
they're doing.
Some companies are just building algorithms.
Some companies want to be a part of or drive
the whole drug discovery process and, typically,
those companies are already backed by big
investors.
There are companies that just tackle certain
aspects of the clinical trials process and
optimize that.
It will really depend on the company, their
funding, whether they're being backed by big
companies and VCs.
What about the role of VCs and investors in
pushing this forward?
There are several kinds of VCs.
One type of VC wants a short-term gain.
But I think, in data science and machine learning,
particularly in biotech, the investors are
more interested in midterm to long-term gains
because it will be transformational.
But VCs are interacting, obviously, across
the industry.
They know where things are going.
If they're smart investors, they will diversify
their risk.
There are some companies that potentially
will have results in the short-term, but there
are companies that are in for the long-term.
I think data science machine learning in the
context of drug discovery is somewhat of a
midterm investment to a long-term investment
whereas pharma companies internally will continue
to invest in the short-term as well to be
ready when the technology is mature enough.
Bulent, as we finish up, what have I not asked
you?
Are there any fundamentally key issues that
we have not covered that we need to talk about
for the sake of completeness?
One of the aspects that we have touched upon
in our previous conversations is also very
valid in this context, and that's leadership.
A part of the cultural transformation, I think
that the most important aspect of driving
the transformation internally is the profile
of the leader.
Pharma, as I mentioned, is traditionally very
territorial.
When you look at the leadership that is driving
those efforts, they have been in the industry
for more than 10, 15 years.
The leaders that they're typically hiring
have been in the pharma industry, out of academy,
for many decades.
When you have data science efforts primarily
led by business leadership, it certainly adds
to the data science efficiency.
But I think the business mindset does not
work really well with long-term data science
strategies.
I think it is essential to find leadership
that has domain expertise, has been in multiple
domains, and can adapt to the pharma domain
fairly quickly.
Repeating the same thing, keeping the same
structure, having the same mindset and then
expecting out-of-the-box transformational
results just doesn't work.
You have to have leaders that can think out-of-the-box,
can bring the creative aspect of data science
into the operations of your company, especially
in pharma, so they have to be open to the
hiring of leadership that is out-of-the-box.
If I can push back slightly on what you just
said, of course, it makes sense to hire leadership
with domain expertise, both in data science
and in drug discovery.
However, if you don't bring a businessperson
to that party, then you may not be able to
construct an economically viable model and
set of processes for enabling this to be durable
for the longer-term.
Absolutely.
I didn't want to recommend hiring a person
that does not consider business priorities.
This is where collaboration is critically
important and the approach to data science
operations is to build a collaborative relationship
with all stakeholders, especially with the
business arm of the companies, to make sure
that everybody has an open mind and can go
through a process in which they educate each
other rather than having a rigid mindset.
That goes both to the business side of things
and also to the data science of things.
I notice that, on your bio, it says Stealth
Startup.
How would you like to totally spill the beans
and no longer be stealth?
[Laughter]
For companies that move out of stealth mode,
there are different models.
If you have a product that you are ready to
talk about and share with the public, I think
it's a good time.
But typically, nowadays, because the R&D loop
is very fast, much quicker than before, companies
prefer to come out of stealth way before the
product is mature enough, but right after,
when they have a proof of concept.
With the companies that I'm involved in, both
on the advising side of things and also building
technology, some of them are not there yet.
We have been speaking with Bulent Kiziltan.
He is a senior data scientist and C-level
executive.
Bulent, thank you for taking the time to talk
with us today.
Thanks for having me, Michael.
It's been a very interesting and fascinating
discussion.
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