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0:40

artificial intelligence and machine learning

0:42

drum beats are only getting louder

0:44

in biotech , and that trend will continue

0:46

for the foreseeable future . When

0:49

a new tech fires up the hype machine

0:51

, it's our job as industry observers

0:53

to suss out what's real . Who's

0:55

actually adopting and applying the technology

0:58

to affect outcomes in drug discovery

1:00

, development and , ultimately , patient

1:02

access ? When virtually every

1:04

biotech who's parsing a little data on

1:06

a computer calls itself AI-powered

1:09

, doing that sussing is

1:11

easier said than done . I'm

1:13

Matt Pillar . This is the business of biotech

1:15

, and on today's episode I'm sitting down with

1:17

the leader of one company that's decidedly

1:20

applying sophisticated computational

1:22

technologies to its biologic

1:24

development efforts . Flagship

1:26

Pioneerings Generate Biomedicines

1:29

led by ex Merck CMO of

1:31

Human Health , Mike Nally . He's

1:33

using machine learning to make big use of

1:35

big data and it's raising big

1:37

money along the way to support the initiative

1:39

. I sat down with Mike in San

1:42

Francisco to learn all about it . Let's

1:44

give it a listen . We're going to talk

1:46

about a lot , but I want to start with you because

1:49

when I look at your background , it's

1:52

not from an early point

1:54

, post academia

1:57

not like it wasn't written in the stars

1:59

. You're going to be a biotech CEO

2:02

. You were a financial analyst at Merck

2:04

. That came fresh off of your Harvard

2:07

MBA , which

2:09

came on the heels of an economics degree

2:11

. That's right . So that was

2:13

in 2003 . And in 2021

2:16

, you were still at Merck . You were CMO

2:18

of Human Health . So there's

2:21

many questions in there , totally One

2:23

of them being that's

2:29

a long run to be at that

2:31

company . So why

2:33

did you decide to leave the comforts

2:35

of a company like Merck , where you

2:37

surely could have rode out the entirety of your career

2:39

?

2:41

It's a really great question . I think

2:44

part of my own personal journey

2:47

. When I

2:49

graduated college , I

2:54

grew up outside of New York City and I thought the path of

2:56

salvation was to be an investment banker because that's what

2:58

all my friends and parents did . My father

3:00

had his own little business and

3:03

in many respects he was the anomaly within

3:05

the community that I grew up with . What

3:07

was his business ? He was in Sporting Goods

3:09

. Oh really , yeah , he was a Sporting Goods manufacturer

3:12

. He'd go over to China manufacturer and then sell

3:14

to the mass merchants and it was

3:16

a great business . But

3:18

he was an entrepreneur through and through

3:21

. But everyone

3:23

else in the town were vendors

3:25

and I

3:27

went and studied finance thinking that's

3:30

what you did , what you grew up

3:32

and I think , as

3:34

I stepped , back after

3:36

the first couple of years I realized I

3:38

was missing that sense of purpose . I was missing

3:41

to what end am I doing

3:43

all this work ? And at

3:45

least for me , the feeling of

3:47

slaving

3:50

away , working 100-hour

3:52

weeks to making money for

3:54

rich people was not how I wanted

3:56

to spend my time . My best friends are

3:59

investment bankers , so I don't mean any disrespect

4:02

for the field , but for me personally I

4:04

wanted a broader sense of purpose in my work and I was

4:06

fortunate in that time to touch on

4:08

the pharmaceutical industry . And when

4:10

I went back to business school I

4:13

went with a distinct purpose of transitioning

4:15

careers . Now I was really

4:17

lucky when I knew

4:20

some of the folks at Merck . One

4:23

of the ladies who was kind

4:25

of a financial Mozart , who's

4:28

Merck CFO the first female CFO

4:30

of a Fortune 500 company said Mike , if you come

4:32

to Merck I'll show you the whole company , and

4:35

this is a lady named Judy Lilliant , and Judy

4:38

basically was true to her

4:40

word . Every 18 months I

4:42

was doing a different sign . So I started

4:44

in the company as a financial analyst doing competitive

4:47

intelligence . Worked on a late-stage pipeline

4:49

. Did the pre-commercialization work

4:51

for a diabetes product called Genuvia . Was

4:54

put into investor relations during the Vioxx

4:56

trials . Went into business development . Did

4:59

the sharing fund merger . So

5:02

if you think about that

5:04

sort of broad exposure

5:06

, I'm a voracious learner

5:08

.

5:09

I was going to say like you're talking about this and I'm

5:11

thinking like you've got to be wired

5:14

a little bit differently , you've got to be a sponge , right

5:16

, I mean you've got to be a sponge .

5:19

Well , I love science

5:21

. I love

5:23

kind of the

5:26

learning process . There's

5:28

so much complexity in

5:31

the end-to-end business model for these

5:33

large pharmaceutical companies . I mean , if you think about

5:35

it , it's a miracle that

5:37

we're able to come up with medicines . Yeah

5:40

, and these medicines are harder

5:42

than putting a man on the moon . Sure

5:45

, in the scientific challenge

5:47

, the manufacturing

5:50

challenges , the commercial challenges , to make

5:52

sure that the world has access to these

5:54

medicines require really

5:57

dedicated , committed

5:59

professionals and as

6:02

I went on those different rotations through Merck

6:04

, I was just really blessed to see the end-to-end

6:06

process , working with

6:09

some of the best people in the industry and within

6:12

any organization , why join Merck I ? Thought it

6:14

was two to three years .

6:15

Right , and when you were

6:17

thinking like at the time , you talked about seeking

6:20

not to get shocked here , but you

6:23

talked about seeking a more meaningful

6:26

end right , rather than getting rich

6:28

and making rich people richer . What was

6:30

the pharmaceutical life

6:34

sciences At that time ? Was that like , well , yeah , that's

6:36

the place . Or why not some other

6:39

philanthropic endeavor ?

6:41

Well , I think combining

6:43

a background in business with

6:46

a endeavor

6:48

that , basically , was

6:50

set up to improve

6:52

the health and well-being of the population

6:54

was something that was really attractive to me . I also

6:57

love the global nature of

6:59

the industry . I loved the

7:02

challenges that the industry were facing

7:05

. The industry's been under kind

7:08

of seminal challenges for

7:10

the last 25 years R&D

7:13

productivity going down . You think about

7:15

pricing challenges , access

7:17

challenges these are things

7:19

that unfortunately have undermined

7:21

the reputation of what was once seen as

7:24

a very novel industry

7:26

and has made it on

7:29

par reputationally

7:31

with tobacco in the

7:33

late public and the

7:36

questions we have to answer and

7:39

solve for as an industry

7:41

are really , really profound . And as

7:43

someone who loves complex challenges

7:45

, as someone who loves these sort of puzzles

7:48

, trying to figure

7:50

out how you

7:52

write the ship so that you can solve

7:54

some of the world's greatest amount of medical

7:56

needs . And when we talk about

7:58

sense of purpose , I

8:00

had the privilege of working on some of

8:03

the most impactful programs

8:05

, products that the industry's

8:07

ever seen Ketrula , indicating

8:11

our 30 different tumor types . You

8:13

think about the number of lives you can

8:15

impact , you think about . Gardasil

8:18

for the HPV vaccine

8:20

, the prospect that we can now eliminate cervical

8:22

cancer from this planet , some of the HIV

8:25

medicines where you're

8:27

offering the prospect ultimately of stopping

8:30

the transmission dynamics in HIV . But

8:33

these are things that have global impact

8:36

and millions of

8:38

people are going to live longer

8:41

, healthier , happier lives as

8:43

a result of that work . And that's what's

8:45

fulfilling what we do and that's

8:47

why a year , two

8:50

years , three years at Merck became

8:52

18 . I was constantly learning . I was constantly

8:55

challenged . I had mentors who

8:57

believed in me in ways that I didn't

8:59

believe in myself . There were jumps that

9:01

I took in my career that I

9:03

probably wouldn't have taken without

9:05

steady encouragement . You know

9:07

, junie was one mentor . Ken Frazier

9:09

, merck's former CEO

9:12

, was another who constantly

9:14

said Mike , we want you to go to

9:16

Sweden and run our Swedish business

9:18

. I had basically been to Sweden for one

9:20

day in my life and knew nothing about the

9:23

Swedish healthcare system . It was asked to

9:25

run that organization and you

9:27

know those were opportunities for me

9:29

to actually become much

9:31

more self-aware as a

9:33

professional , to learn to grow , to

9:36

grow personally , with my family going

9:38

to Sweden , but also professionally

9:40

but also contribute to a much

9:42

greater cause , and that's part of what made

9:44

, you know , the 18 years at Merck so special .

9:47

Yeah , it's interesting the impact you talked

9:49

about , the impact that you were able to play a part

9:51

of in Merck

9:53

. Often when I in specific

9:55

examples , ketrude Gardasil I'm an

9:57

amazing often when I talk

10:00

with big bio turn biotech

10:02

CEOs , they tell me that , maybe

10:06

, ironically , the

10:09

tipping point for them wanting to jump into

10:11

biotech you know , small , nimble , agile

10:13

, scrappy is because they want to feel

10:16

like they have more of an impact than they did in big

10:18

bio .

10:18

So that's sort of an interesting dichotomy . Well

10:21

, I think you

10:23

have more control in

10:26

biotech . You

10:28

know , the litmus test that I had when I thought

10:30

about , you know , leaving Merck was

10:32

wherever I was going to go . I had a belief , fundamentally

10:35

, I could have a greater impact

10:37

on humanity , and

10:40

there aren't many organizations in the world that

10:43

can have a greater impact on humanity than the company

10:45

. If you think about the history

10:47

of the company , yeah , from the mass production

10:49

of penicillin in the 50s

10:51

, all the way through

10:53

a series of antivirals , vaccines

10:55

you know 70 , 11 , cdc

10:58

recommended vaccines were invented by Merck

11:01

the cardiovascular drugs , the

11:04

HIV drugs , all the way

11:06

now through an array of

11:08

cancer products . It's

11:10

a company that's had , you know

11:13

, a huge impact on society . And you

11:15

know , in the 90s Merck

11:19

was the IT company . It

11:22

was Fortune's most admired company seven

11:24

years in a row . And

11:26

that's because of an unparalleled

11:29

track record of not only

11:31

scientific innovation

11:33

but doing good for humanity

11:35

. Programs like the Mectizan donation program

11:37

, which you know had basically a limited

11:40

river blindness , were a huge

11:42

part of what made the company

11:44

a special place . And you know that

11:46

is , you know , this industry at

11:49

its best , where you can do good

11:51

for shareholders while doing

11:53

good for humanity . And that was a core

11:56

part of , I think , what I learned within

11:58

Merck . And you know , as I thought about

12:01

going to biotech , certainly

12:04

in a smaller organization , the

12:07

impact of your actions

12:09

and decisions . You know , at Merck

12:11

you had to influence a 72,000 person

12:14

organization In a biotech

12:16

organization . When I joined Generate , it was 30

12:18

people . Obviously

12:20

, you know each of those 30 employees had

12:23

a huge impact on

12:25

the success of the organization . Sometimes it was harder to

12:27

see that impact in a big company

12:29

. But I think the impact of the

12:31

collective work of the 72,000 was

12:34

enormous and you know

12:36

, depending on how you're measuring , you

12:39

come up with different viewpoints .

12:41

Yeah . So I mean

12:43

, if listeners didn't know that your CEO partner

12:46

, flagship CEO at Generate

12:48

, they might think you're still an executive

12:51

worker . They might think you're passionate

12:53

about the work you did there . Why did you leave ? You

12:56

could have stayed there until .

12:58

I've believed . Merck

13:00

to you . It's a company that is near

13:02

and dear to my heart

13:05

. It's a company with some

13:07

of my best friends , some of my

13:09

proudest professional moments . I

13:12

was really just fortunate to

13:14

uncover an amazing

13:17

opportunity , what I think is a generational

13:19

technology . And

13:22

you know , when I was at Merck one of the

13:24

things that I was fortunate enough to do

13:26

was lead Merck's vaccine business . Well

13:29

, I was leading Merck's vaccine business . We had

13:31

an early partnership with Moderna and vaccines

13:33

. During that time I got

13:35

to know Stefon

13:38

Bunsell and Newbar Fan . So

13:40

Stefon is the CEO of Moderna and

13:42

Newbar is the founder and

13:45

CEO of flagship pioneer and

13:47

we

13:50

had had conversations over the years around

13:53

. You know , if I were to leave Merck , what would I

13:55

want to do ? And you

13:57

know , over time I became

14:00

increasingly convinced that

14:02

the future of the industry

14:04

was going to lie at the intersection

14:07

of science and technology . And

14:09

you know I became , you know , increasingly

14:12

frustrated in many respects

14:14

that the

14:17

industry wasn't having enough success

14:19

at changing underlying productivity curve . We've

14:23

talked about it forever that in four

14:25

decades in a row , industry-wide

14:27

R&D birth have been going down . I

14:31

think somewhat interrelated not entirely

14:33

that has led

14:35

to bad pricing practices

14:38

within the industry . Those

14:41

pricing practices have undermined patient

14:43

access to medicines . That's what creates

14:46

some of the reputation conundrum

14:49

that the industry finds itself with them

14:52

, I became the belief

14:54

on why science and technology

14:56

would be this potential

15:00

answer . It's simply

15:02

the fact that I think it's in our

15:04

view . We'll just look at the

15:07

fact that , despite staring

15:09

at biology in the eye for all of our existence

15:11

as humanity , we only

15:13

know a small fraction of

15:17

the majesty of biology . With

15:20

the advances in technology that

15:22

we were seeing all around us

15:24

, we were entering an age

15:26

where the combination

15:29

of brilliant human ingenuity

15:31

and computation

15:33

and technology was

15:36

going to start to help us understand the language

15:38

of biology in ways that the human mind alone

15:40

couldn't understand it . Fortunately

15:45

, the team at Flagship

15:48

was working on a company called

15:51

Generate Biomedicines . Generate

15:53

was founded five years ago . When

15:56

I first received this call roughly

15:58

three years , a little over three years ago Generative

16:02

artificial intelligence was not part of my lexicon

16:05

. I

16:07

mean , it's still a black box to most

16:09

. This

16:12

team recognized that

16:15

by applying a top-down approach

16:17

to the language of proteins and

16:19

using these sort of generative technologies , we

16:22

would be able to understand that language

16:24

in a way that would allow

16:26

us to ultimately learn how to program

16:28

novel protein-based therapeutics

16:30

with desired attributes . In

16:33

the 20 years I've been part of the industry

16:38

, I had never seen anything like the

16:40

early data . If you think

16:42

about how we discover protein therapeutics

16:44

today , we immunize a human and

16:46

a mouse , a llama we call

16:48

them plasma . We find things that

16:51

stick to interesting targets and

16:53

then we manipulate them in a

16:55

pure trial and error process . We

16:58

have no control over the antibodies our

17:00

immune systems create . We

17:02

then try and make them into drug-like entities

17:05

. Think about the world

17:08

where we say give

17:10

me a molecule with

17:12

this code to

17:15

treat this disease . It

17:19

sounds science fiction In

17:22

many ways . We're not by any means there yet

17:24

, but we're on a path toward

17:28

that destination . This technology was helping

17:30

us start on this to understand that

17:33

language or proteins in really profound

17:35

ways . Some of the

17:37

things that we were able to do were

17:40

just beyond my wildest imagination

17:42

. Some

17:45

of the interesting tidbits

17:47

that I picked up as I was looking into the opportunity

17:50

were the average

17:52

protein . We

17:54

have 20 natural immunosets , the

17:56

average protein may be 200 immunosets long

17:58

. If you think about the

18:01

combinatorial possibilities of proteins

18:03

, it's 20 to 200 pounds

18:05

. That's atoms in the universe

18:07

cubed . The

18:09

search space is extraordinary

18:12

. Oftentimes

18:14

we think about nature as being this all-powerful

18:17

, all-knowing being

18:19

. The reality

18:21

is that nature has always

18:24

surveyed a small fraction of

18:26

that search space . It's just too

18:28

large . If

18:31

you have a technology that will allow you to survey

18:33

those oceans , you're going to find some

18:35

really interesting things . We're

18:37

finding that on a daily basis . These

18:40

technologies are helping

18:42

us understand biology better

18:44

if they're helping us come up with better answers to

18:47

existing conditions , but also giving

18:49

us new tools now to prosecute

18:52

disease in very unique ways we're

18:54

going to get back to the science again .

18:56

I'm just clearly very excited about it . We're

18:59

going to get back to that because I

19:01

want to pause for a minute at the moment of transition

19:03

. You were excited about it then

19:05

and you remain excited about it . You

19:08

join Flagship . You come

19:10

on as CEO at Generate

19:12

2021 . How

19:17

aware were you that you were signing up

19:19

for a post that

19:21

was going to face the headwinds at that

19:23

time ? How aware were you that you were not

19:26

on the scientific and exegesis standpoint You're

19:28

coming with ? Energy . I get it All

19:30

of a sudden . You're like yeah , I'm the CEO of this new

19:32

company we're

19:35

destined to solve all these problems , and then , all of a sudden

19:37

, the whole space

19:40

goes to shit .

19:43

We were really lucky . If

19:45

you think about the peak of the biotech

19:47

market , we started with 2021 . I

19:50

started March of 2021 . I

19:52

don't think people realized that . I think people

19:54

thought it was going to be a momentary blip for

19:57

a while in 2021 . During

19:59

that period , we raised $370

20:02

million as part of our series B financing . We

20:05

raised it from an extraordinary group of

20:08

investors who

20:10

shared our vision

20:12

for the future

20:14

promise of the technology . As

20:17

I think the downturn started to be

20:19

fully realized , we

20:22

were in a very strong position for my capital

20:24

.

20:27

Did you know that coming in , did

20:31

you know this was risky ? There's

20:34

always inherent risk in

20:36

joining a biotech and leading a biotech , but

20:38

we're , relative to the downward

20:41

curve , we're

20:43

in a good position .

20:45

I think this whole idea of

20:47

risk is grossly overstated . Every

20:52

independent biotech endeavor has

20:54

inherent risk . If

20:57

you think about the unmetting and the

20:59

number of opportunities across the biotech ecosystem

21:01

. Failure is part

21:03

of our model . I

21:07

think it's almost your failing for

21:10

valid scientific reasons

21:12

. The biology just is not going to work out . If

21:15

you're giving it a shot , there's going to be

21:18

plenty of opportunity on the other

21:20

side , as long as you manage

21:23

the business side of things Certainly

21:28

in 2021, . If you thought about the

21:30

biotech ecosystem , companies

21:32

were stringing up last and right . Opportunity

21:35

was plentiful . The

21:38

risk of generating not working

21:40

out to me was okay

21:42

. I could do another opportunity

21:45

. I think you've seen

21:47

that across this ecosystem for a long

21:49

time . We're in a glorious

21:52

era of scientific innovation

21:54

. You've

21:57

heard this from , I'm sure , countless people

21:59

. Science has never been better .

22:03

Science needs to be better to

22:06

sustain because we've seen over

22:08

the course of the last 18 , 24 , 36

22:11

months , a lot of great science gets

22:13

shell back burner flush

22:16

. You hit

22:18

an important point as

22:20

long as you manage the business people

22:22

are the rate lender , it's not capital

22:24

.

22:26

Great teams are the rate lender

22:28

. I think there's plentiful

22:30

science and not

22:32

always will the science work out . You

22:35

can have an extraordinary biological

22:37

hypothesis , one of the things that

22:39

you learn this is part of

22:42

. If you think about my time at Merck , it's like

22:44

I got a PhD in this

22:46

industry from Merck . The

22:51

best scientific minds that we've ever

22:53

encountered in life sciences are

22:56

wrong 80 to 90% of the time

22:59

, and

23:01

I'm certainly not in that category . Failure

23:06

is part of this model To

23:11

me , as soon as you acknowledge that the

23:14

way you manage that to

23:16

a certain extent is you build an extraordinary

23:19

team really very people in

23:21

a cross-disciplinary manner . If

23:26

you have an opportunity , you

23:28

find a portfolio or

23:30

platform , and part of what attracted

23:32

me to the opportunity of flagship and opportunity to

23:34

generate was that it wasn't a single

23:36

asset , because , again

23:39

, I'm not smart enough to pick the

23:42

winner , but

23:44

if I have a portfolio , I can then

23:46

manage the

23:49

odds in a very different way . In

23:52

the 2010 assets , the

23:54

expected value starts to become greater

23:56

than one and

23:59

in that sort of a world , I think , a place

24:01

of the strengths of someone who has

24:03

more of a business background

24:05

and

24:07

to me , the period

24:11

of 2021, . The

24:13

biggest issue that we actually encountered

24:16

was delusion of talent . We

24:19

saw this skyrocketing number

24:21

of biotech companies , of public

24:23

biotech companies , of private biotech companies

24:25

, of new startups , and

24:27

that created position and

24:30

title inflation across

24:32

the industry and the world doesn't

24:34

need the sixth and seventh and eighth-in-class

24:37

approach to the same problem . There

24:40

was actually too much . Too

24:43

many companies started and

24:45

unfortunately , I think we've had to go

24:48

through a pretty rough

24:50

datum turn over the last couple

24:52

of years and

24:54

we've seen a number of those companies struggle

24:57

to sustain financing

24:59

through that period . But

25:02

over time , that raw

25:04

scientific foundation is in place , there

25:07

still is a lot of capital and

25:09

certainly the assets

25:12

that show real

25:14

clinical proof are getting funded

25:16

and I think we'll see

25:18

the pendulum . This pendulum swings back and

25:20

forth on product versus

25:22

platform . Right In 2021

25:25

, it was in vogue to

25:27

be a platform , largely because a

25:29

platform technology in mRNA was

25:32

saving the world Sure , and

25:34

in 2022 and 2023 , it

25:36

was much less in vogue to be

25:38

a platform Over time . That

25:40

, oh , the RNA space still .

25:42

There's still plenty of opportunities . There's plenty of platform

25:45

.

25:45

Absolutely , but those things will

25:48

go back and forth in terms of sentiment , and

25:50

so I think for

25:53

me , for the industry

25:56

, this period has

25:59

been one where , I think you rightly point out

26:01

, some really great science will

26:03

get caught up in this downturn . Some

26:07

really great teams will get caught up in this downturn . Some

26:10

teams will get caught

26:12

up in a valuation

26:14

trap in some respects where , if

26:16

you raised in 2021 , 2020

26:18

, at the peak of the market , major

26:21

down rounds would be required to

26:24

sustain organizations , and

26:27

that's really hard , and

26:30

so it's been really a rough

26:32

period . But I think , as we enter this

26:34

year , certainly we now have had the two

26:36

best months on record in

26:38

the biotech industry over the last couple of months . That

26:42

gives us a lot of green shoots as we enter 2024

26:45

to start to make a big difference again .

26:47

Yeah , you

26:49

talked about the fact that you probably wouldn't have signed

26:51

on for such an endeavor had it been

26:54

not a single asset company . And on that perfect

26:56

science , it's logical . In

26:58

the platform space

27:00

or the platform play , what

27:04

sort of thing ? You know you got a single asset . You've

27:06

got to hit milestones to make . You know you got

27:08

to make a money , stretch to the

27:10

next milestone , get more money . You know it's kind of

27:12

that leap probably . So

27:16

I understand where the platform

27:18

play gives you more to work with . But

27:21

how does that change ? Sort of the milestone

27:24

picture

27:26

, right Like you've still got to produce something to reach

27:28

a milestone .

27:30

I think , in terms of the executional

27:33

rhythm that you want to instill within the company

27:35

, I don't think it changes anything . There's

27:38

different milestones , right ? I mean , I've

27:40

got to be as

27:43

accountable to my shareholders

27:46

as a single product . So

27:49

if I say I'm going to advance the technology

27:51

on X , y and Z dimension

27:54

, you know the

27:56

. You know I'm going to

27:58

have to do some of the things that

28:00

I've been doing . So I have to do that and

28:03

if I say I'm going to , you know , have

28:05

two programs into the clinic this

28:08

year . I've got to show products

28:10

on that front and so you know it

28:12

just gives you more levers . And

28:15

sometimes those levers , you

28:18

know , when something you know runs

28:20

a file , you can not you can replace it with

28:23

something else . So

28:25

it gives you a bit more flexibility , a bit more optionality

28:27

, but I think the executional

28:32

rigor has to be the same

28:34

, irrespective of whether you're in a one product

28:36

company or , you

28:38

know , a platform organization . Yeah , I

28:40

mean , that's what builds trust with your

28:43

board , with other stakeholders , with your employees

28:45

, for that matter .

28:47

You mentioned your 2021 series

28:49

B . Yeah , so you raised

28:51

the $280 million series

28:54

C , so I'm hoping

28:56

for some insight on that . How

28:58

you made that happen , what was key to it , how

29:00

it was executed .

29:02

I think it was really key to what was . When

29:06

we look back at what we suggest that we

29:08

would do with our financing

29:10

from our Series B . We delivered

29:13

on milestones so

29:15

we had earned the trust of

29:18

all of our existing investors participating in our

29:20

Series . We had 16 Series B investors

29:23

and all 16

29:25

of them stepped into our

29:27

Series C financing and

29:29

as you can imagine , given market

29:31

conditions , everyone

29:34

was in different situations . They

29:37

all decided to continue to

29:39

put the trust in the team at

29:41

Generate and that was really

29:44

, really meaningful to me , to the entire organization

29:46

. At the same time , we were able to

29:48

attract an array

29:50

of different new

29:53

investors in the process . It

29:55

was much . I mean

29:58

, the level of diligence that went into the

30:00

round was night and day

30:02

between 2021 and 2023 .

30:04

Yeah , that's a huge and overused

30:06

term . Let's unpack that a little bit . That's one of the questions

30:08

I have for you . Is you know

30:10

in more of a rudimentary way to

30:13

put it , like what not

30:15

? Necessarily a frustrate to the way the deals

30:18

are being done today , but what's

30:20

different about it ?

30:21

Well , listen , I think you know again

30:23

, I think that's a big deal , I think

30:26

, of these things . You know , sometimes we hit

30:28

different polls in 2021 and may have been a

30:30

little loose In 2023

30:32

, it was probably , you know

30:34

, the orientation 2021 was

30:36

how do I find a way to deploy

30:38

capital In 23,

30:40

? The kind of orientation

30:43

that the starting point for many investors was

30:45

I

30:47

don't want to deploy capital , but let me

30:49

, I want to look , and

30:51

so you're almost finding reasons not to invest versus

30:54

finding reasons to invest . Yeah , and

30:57

you know , listen that that you know within . You

30:59

know the round that we were able to put together

31:02

the . A big key of it was

31:04

our ability not only to show

31:06

the tracker curve of execution but

31:09

also show the enormous

31:11

promise over the next 24 to

31:13

36 months . And

31:16

you know , as we were in

31:18

the process of raising , you

31:20

know we talked about having , you know , multiple programs

31:22

homegrown programs enter the clinic . These

31:24

are programs that were started by

31:27

generate a couple of years ago using

31:29

the platform . The first one

31:31

that went into the clinic was 17 months from concept

31:34

to clinic . The second one was about

31:36

two years . They're

31:38

addressing major medical needs , their

31:41

technology , their approaches that are

31:43

very distinctive , you know

31:45

, for the individual targets that we've gone after . You

31:47

know , in many ways , you know

31:50

we were able to show promise

31:52

to both our

31:54

existing and future investors that they don't

31:56

said . You know this is a great time , yeah

31:59

.

32:00

Is there a difference in your mind ? I mean , obviously

32:03

there are a lot of variables behind

32:06

the decision to invest in a company

32:08

. Yeah , like generate , any company

32:10

for that matter and you mentioned the importance

32:13

of the support of your existing investor

32:16

base , and then you mentioned

32:19

they got some new ones . Do you see any

32:21

, I guess , lines of demarcation between what

32:23

turned the new ones

32:26

to on the generate

32:28

versus those that you already had ? Like , yeah

32:30

, we're the tire kickers more interested

32:32

in the future ? Were they happy to get

32:34

on board with great investors

32:36

who are happy to see results over the past

32:38

18 ? Months . Like what was sort of ?

32:41

Well , I think you know , every

32:43

investor follows a slightly different form . All

32:45

right . So what you said is going to be

32:47

somewhat of a generalization , but the I

32:50

think you know the commonalities

32:52

that I would pull out right . One

32:55

is you know

32:59

people usually back people and

33:02

you know I've been really fortunate to be able to recruit

33:05

a world-class team and

33:07

that team , I think , instills

33:09

confidence in both new and existing investors

33:12

right and

33:14

but on the jacking of the horse and

33:16

I think there's an element to that

33:18

. I think to the horse

33:20

.

33:22

We don't want to discredit the horse , we

33:24

want to discount the horse , but generally they have a pretty

33:26

cool space . Yeah , right .

33:30

I think you know , certainly you know , over the course of

33:32

the past couple of years it's

33:34

become part of the common lexicon

33:36

and I think we started to see you

33:39

know on an individual

33:41

and personal level , the

33:43

transformative impact that these sorts of technologies

33:45

can have . You know the chat GPT

33:47

raised generative AI in

33:50

the conscious and

33:53

it started to show people the

33:55

enormous potential . I you

33:57

know , before the kind

34:00

of generative AI revolution , most

34:02

people thought of AI and all come

34:05

or technologies

34:07

as largely efficiency

34:09

plays . It was

34:11

about speed and efficiency right . What

34:15

generative AI has shown now ? Is

34:17

it aids the creative process ?

34:20

I can have chat GPT be a thought partner

34:23

while maintaining the speed and efficiency as

34:26

well , or at one hand .

34:28

Yeah , all right , but

34:30

the real value in our industry

34:32

comes from

34:35

novelty and creating

34:38

the best assets . You

34:41

know , if I'm a month faster

34:43

than someone and I'm in fear , ultimately

34:47

clinicians shouldn't use my . If

34:50

. I'm a month faster and superior

34:52

. I'm going to

34:54

have a whole lot of use of that product

34:56

. Yeah , and I think what we're finding

34:59

with these sort of technologies

35:01

and why I think you know part of you know

35:03

you go back to your personality generative

35:05

AI is a really interesting force in

35:08

applying it to biology , because the

35:10

early signals and

35:12

some of the early clinical programs and some of the next waves

35:15

of clinical programs are suggesting

35:17

that we're getting better answers

35:19

than historical approaches enabled

35:22

, and I think that's something

35:24

that if you kind of just keep pulling

35:26

that thread , if you've got a capability

35:29

that can give you differentiated products consistently

35:32

, that's a pretty

35:34

exciting proposition to

35:36

get behind Right

35:38

now I don't want to pull .

35:40

I had a conversation with a guy a bit earlier

35:42

today and I don't want to pull words in his mouth so I'm not

35:44

going to name him , but he was getting

35:46

to a point around we were talking about the

35:49

impact of artificial intelligence and

35:51

its early impact on molecular discovery

35:53

, right , and he kind of alluded

35:55

to a point where he was kind of saying

35:57

like to change the paradigm

35:59

you talked about it earlier the costs

36:02

and access paradigm . There

36:04

needs to be more than faster

36:06

, better molecular discovery , because things

36:10

get really tight and expensive

36:12

, totally complex at the clinical level

36:14

. So

36:16

I guess a big picture . If you look at the

36:18

impact of artificial intelligence and the

36:20

way that you're using it to generate , how

36:23

far along the continuum

36:25

does it actually do envision

36:28

it making an impact on better

36:31

and faster ?

36:33

So I would . I'll

36:38

challenge that underlying premise , but let me

36:40

kind of just start with the end to end

36:42

. View right Artificial

36:46

intelligence , machine

36:48

learning , our tools , the

36:52

tools to apply to

36:54

all parts of our lives . They

36:57

will be applied to all parts of our lives and

37:01

all pieces of

37:03

the drug discovery and development continuum

37:05

will ultimately be transformed by

37:08

these underlying technologies . Today

37:12

we've found the sweet spot

37:14

for our corporate company is

37:17

in molecularism . We're

37:19

starting to use it on target

37:21

discovery approaches and

37:23

we're thinking about how we can use

37:26

data-driven digital

37:28

data approaches in

37:31

trial execution and

37:34

some of the biomarker discovery . But

37:39

I think in

37:42

the future , consistently

37:46

, humans who

37:48

embrace AI co-pilots

37:50

are going to explore our research

37:53

alone , and

37:56

that will be true across all

37:58

parts of all industries .

38:02

You bring this to that point brings up an interesting

38:05

conversation that I've had many times with AI

38:07

for this via text , and

38:09

that's this concept of especially

38:12

those who maybe were born , you

38:14

know , pre-ai acceptance

38:16

or understanding . I've

38:18

had many conversations about the

38:21

nuance of successful integration

38:23

of traditional biology

38:25

and computational biology

38:27

. Total At a physical level , at a

38:29

personal level , at a , you

38:32

know , intellectual

38:34

property level , that's right . So don't

38:36

you manage that ? A generator Like what's , what

38:38

do you ? Is there a challenge

38:40

there ? Or is it a situation where

38:43

you know from the company's outset

38:45

it was we're looking for these very

38:47

open-minded biologists

38:50

who have some computational skill ? Or are

38:52

you marrying two different disciplines in a , you

38:54

know , throw them in a room and make them sorted out , kind

38:56

of way ?

38:57

Well , our two co-founders who still

38:59

are very active at the company , molly Gibson and

39:01

Gavore Gorgon . Both

39:03

are computational

39:06

scientists that

39:09

have studied biology , right

39:11

so . Gavore was a professor at Dartmouth , but

39:13

in both computer science and

39:15

biology Right . Right Molly

39:18

was a PhD in computational systems

39:20

biology from Washington University

39:22

, with a computer science undergraduate

39:24

degree . These folks

39:27

have , you

39:29

know , done a brilliant job

39:31

of creating

39:34

an organization and an

39:36

ethos as of an

39:38

organization where our

39:41

computational scientists are

39:43

not in service of our experimentals

39:47

, Our experimentals are not in service of our

39:49

competition . We are collaborative

39:51

across the domain and we embrace

39:54

people who have

39:56

that multidisciplinary training and

39:58

the single biggest

40:01

predictor of success within January

40:03

is intellectual curiosity , yeah

40:05

, and people who are voracious

40:08

learners , who are interested in

40:11

the domains that none

40:13

of us are experts . In

40:16

every component of

40:18

this complex you know

40:20

way maze that we call drug

40:23

discovery , development , manufacturing , commercialization

40:25

, we

40:27

all are dependent on certain handoffs , but I think

40:29

the people that can stretch across multiple parts

40:32

of this Biological problems

40:34

are really profound

40:36

that we're trying to address . Biology

40:38

is extraordinarily complex and unfortunately we've

40:41

seen , you know , while we've had , you know , a

40:43

lot of organizations with extraordinary data

40:46

in individual disciplines , we've

40:48

only made so much progress , right , having

40:51

people that can span these different domains . I

40:53

think you're going to give us a different set of answers . It's

40:55

not a solve everything .

40:56

No , no , but it's certainly so . I mean

40:59

, I think about it in the context of , like you know

41:01

, a three year old , nine or four year

41:03

old generate had

41:06

the company launch with the same mindset

41:09

, even three years earlier , much less 10

41:11

years ago . Right , that

41:14

talent deficit that you talked about earlier would

41:16

have been even more exacerbate

41:18

, right yeah . Like

41:21

, in what you're doing , are you seeing improvement in

41:23

terms of , you know , replicating Molly

41:25

, and who else did you mention ? Yeah , are

41:27

you seeing , like , are you seeing improvement

41:30

in replicating those skill sets down

41:32

the line , not just at the founder level , all down the line

41:34

? So coming out of academia .

41:36

I mean there were about 100 people

41:38

in the world when I started that generate . They

41:40

were skilled at the intersection of machine learning and protein

41:42

and cheese . We were all under it by

41:44

name . Yeah , I mean

41:46

it was a small niche , community , Right , Good

41:49

place to be .

41:49

I think for those folks , right

41:52

, but you know where ?

41:53

they actually all worked . Yeah

41:56

, deep mind , microsoft

41:58

research , facebook , salesforce

42:01

all had protein teams . Yeah

42:03

, that's interesting and you know , obviously somewhere

42:05

within academia as well , but it

42:07

wasn't a classical path of training and

42:10

you know market dynamics

42:13

are very powerful , supply

42:15

and demand dynamics are very

42:17

powerful . Let's just say , when tech

42:19

enters into the bidding war for

42:22

those types of talent , salaries

42:25

, well , they should change

42:27

Right . And so

42:29

you know there has

42:31

been an influx of

42:33

people , you know

42:36

, that are now entering these , these areas

42:38

, to the point where we

42:40

had a . Our ML team had

42:43

a , an internship program last year

42:45

, and we had five

42:47

spots . We

42:49

had 2000 applications .

42:51

Really yes , and

42:53

these kids like it . I said kids because

42:55

, I'm just these young

42:58

aspiring professionals that are interested

43:00

in a position like that , are they ? Are they coming , these

43:02

2000 people coming to the , the

43:05

, the opportunity with like

43:08

?

43:08

biology , phd , phd

43:11

learning , math biology

43:13

experience .

43:14

That's what I'm wondering , and is it if they're coming

43:16

at it with that scientific

43:18

background and curiosity that

43:20

you would require in an ideal world and

43:23

in the same , like some of our team

43:25

you know , may just be brilliant

43:27

mathematicians that are extraordinarily versatile

43:29

.

43:30

Others will be extraordinary biologists with

43:33

a natural predisposition toward computation

43:35

Right . Some are

43:37

computer scientists . You know that you

43:39

are very versatile , and so

43:42

I don't think there's one single phenotype

43:44

. But you know the

43:46

we're

43:49

really fortunate to have some extraordinary people

43:51

on our team . You know

43:53

our both our computational team

43:55

, our experimental teams , are

43:59

filled with

44:01

brilliant people , and even our experimentalists

44:03

oftentimes have competition , because

44:06

this doesn't work . You know , one

44:08

of the things that you know has been super important

44:11

from the inception of generate

44:13

was how

44:16

do you view data , and

44:19

unfortunately , in many organizations

44:21

, data is power . It's

44:24

my experiment , then

44:27

I have the data , I see what you're getting at and

44:30

you know , when you have a machine learning approach

44:32

, data has to be a collective

44:34

asset and

44:36

you need an orientation

44:39

on the experimental side that recognizes

44:42

that every data point is

44:45

captured to refine the computational approach

44:47

, and that is not your data

44:49

, it's our data , because

44:52

you know the whole machine operates

44:56

on that currency and

44:58

so you know there's a

45:00

huge cultural piece that I think is really

45:03

important for these companies that are starting at this intersection

45:05

. And you know , one of the debates I had for

45:07

a long time with colleagues at Merck was

45:09

will it be the incumbents or the insurgents

45:11

that actually craft this cup

45:14

of tea ? I think incumbents have huge advantages

45:16

, right . Whether you're attacking

45:18

incumbent or you're a pharmaceutical incumbent , you

45:20

know , if you're attacking , you have some of the best computational

45:22

talent , you have extraordinary

45:25

resources . Right , if

45:27

you're on the pharma side , you

45:30

have some of the best biological talent in the world

45:32

and extraordinary

45:34

resources . What you know these insurgent organizations

45:37

can do , though , is create a culture that

45:40

actually blends the best of both worlds

45:42

, and I think it's really

45:44

, really important that

45:46

you know , in pharma , oftentimes

45:49

the computational scientists are in service of the

45:51

biologists , and in

45:53

tech , if you have biologists , they're

45:56

oftentimes in service of the technologists

45:58

. In these sort of

46:00

organizations , the

46:03

ability to create

46:05

alignment and

46:08

you know , kind of a parody that you know these

46:10

two one . You know it's a

46:12

symbiotic relationship in many respects

46:14

between you know , the computational theme and

46:17

the experimental theme , and you know that

46:19

is something that I think has been , you know , really

46:22

, really extraordinary to

46:24

watch , to cultivate , and

46:26

if you're an experimentalist and you don't subscribe

46:28

to , one of our

46:30

five core values is

46:33

around being digitally native and

46:36

around how data has to be a collective

46:38

asset , and if you don't subscribe to that generally

46:41

, it's just not the right place for you . It doesn't mean

46:43

you're a bad person .

46:45

When you say collective asset , I'm curious about your

46:47

take on collective meaning

46:49

. You know within the four

46:51

walls , so to speak , of generate

46:54

versus data being a collective asset to

46:57

the industry , and

47:00

where the lines fall and

47:03

I'm sure it's probably both right

47:05

. Yeah , but how do you like

47:07

when you look at it from an industry-wide perspective

47:10

? Open source data

47:12

, publicly available data , which is incredibly

47:15

important , Huge . How

47:18

does that kind of shake out in terms of generates

47:20

, approach to differentiation and competition

47:22

?

47:22

directly I mean . So when the company started

47:25

, you know , the ratio

47:27

of public to proprietary data obviously was

47:29

one to zero . Right , it's

47:31

all public . Yeah . And you know the

47:34

reason generally it was actually found

47:36

to dissolve the protein challenge in summer's facts

47:38

was because there were two extraordinary

47:40

public data sets . One was

47:42

the sequencing revolution has

47:45

given us 180 million amino acid

47:47

sequences across all species . And

47:50

then , number two , the structural

47:52

biology community gave

47:54

us an extraordinary database called the Protein

47:56

Data Bank , where 200,000

47:59

high-quality , high-resolution structures

48:01

of proteins were exceptionally

48:03

well curated . And

48:05

what that allowed us to start to do is establish

48:07

the interrelationship

48:09

between protein sequence and protein

48:12

structure . And

48:14

if you think about technologies like alcohol or , from deep mind

48:16

, those in some ways were trained

48:19

on these two data sets . So

48:21

the ability to go from a sequence to

48:23

a structure is a prediction challenge , right ? And

48:26

if I start to understand those two data sets I

48:28

can start to make predictions around different sequences

48:30

and different you know structure

48:32

, structural or holding patterns . Generally

48:36

it was always oriented toward adding a third leg to

48:39

the stool . As a therapist

48:41

companies . Sequence and structure are interesting

48:44

. Functions is

48:47

where the business is right . We

48:49

care about how you know

48:51

proteins interact , how they drive

48:54

biological function , right , and

48:56

unfortunately there

48:58

is no public database

49:01

function and there's

49:03

a number of inherent

49:06

limitations in

49:08

the industry to

49:11

create a kind of an open source functional database

49:13

. Primary among them is

49:15

inherent variability of measurements

49:18

. So if you were to run

49:20

an assay and I were to run an assay in

49:22

different labs

49:24

. We make it different values and

49:28

, as we all know , with machine learning based

49:30

technologies , the quality

49:32

of data is paramount . Garbage

49:34

in , garbage out . So

49:37

part of what we've tried to do within the company

49:39

is create proprietary data sets

49:41

of extraordinarily well

49:43

curated data

49:46

. We use a

49:48

lot of automation , we

49:51

use miniaturization techniques

49:53

, microfluidics to do experiments

49:56

at greater volumes , so that

49:58

we can . You know , when we run experiments

50:01

we want to collect

50:03

as much data as possible , and

50:06

that's allowed the company , over the course of the last five

50:08

years , to go from a starting

50:11

point of 100 examples

50:14

to an individual experiment , to

50:16

the biggest experiment we've now run as a million defined

50:19

variants and every one of those

50:21

data points either biophysical

50:23

, measurement or

50:26

functional measurement are captured and

50:29

fed back to refine the computational approach . So

50:31

you know , we've now generated

50:33

sequence and built into full

50:36

length proteins and tested

50:38

around five million proteins and

50:40

all that data now is informing

50:43

our models . And that's helping us now to understand

50:46

. You know what does it mean to

50:48

be a . You know go from a protein to

50:51

a drug . You know key parameters

50:53

affinity how tightly

50:55

do I bind to the target Potency

50:58

, right Immunogenicity

51:01

All of the manufacturability

51:04

and developability parameters that

51:06

are really important . Those are things that

51:08

are measured to actually

51:10

help inform our modeling , to say

51:12

, how do I optimize to go from

51:14

protein to optimal

51:16

therapeutics as quickly , as quickly

51:19

as time , as fashion as possible ? And so the

51:21

data sets are accumulating and

51:23

there are major data gaps that we still see

51:25

, that we're making either

51:27

sizable investments ourselves or we're looking to

51:30

partner right . And

51:32

you know , on the internal

51:34

investment , we've made an investment

51:36

in cryoEM microshills . So

51:39

these are four . We have four cutting

51:42

edge , world class high

51:44

resolution cryoEM microshills . The

51:47

reason we bought those were in

51:50

that protein data bank . Of

51:52

the 200,000 high quality , high resolution

51:54

structures that I mentioned , only

51:57

about 2,000 are

51:59

protein-protein interactions . Most

52:02

of them are pre-standard proteins and

52:04

in medicine or

52:07

in the pursuit of medicines you

52:09

want the interaction . You

52:11

understand what residues are driving function

52:14

in that antibody , antigen

52:16

interaction , and

52:19

we

52:21

decided that let's figure out how to supplement that dataset

52:24

because that data is really valuable for our models

52:26

. On the other side

52:29

, there's a whole array of data that we're

52:31

still very data poor as a company

52:33

and as an industry . Part of why we

52:35

did the partnership we have

52:37

with MD Anderson MD Anderson

52:39

Cancer Center , one of the world's

52:42

busiest cancer center

52:44

in the season many patients at any center in the United

52:46

States was

52:49

we wanted to have understandings

52:52

of the drivers of certain cancer types

52:54

. We understood that at a

52:57

foundational level our technology

53:00

would allow us to drug those

53:03

targets and

53:05

it's been a great partnership because it brings the data

53:08

from a world-renowned

53:10

organization with our unique capability

53:13

to create medicines for patients and hopefully get

53:16

better medicines for patients faster .

53:18

I've been trying to learn about spatial biology . Yeah

53:20

Right , this is a new trend

53:22

. Is that ? Spatial . Are

53:25

you guys part of the player ? Not

53:29

a huge part .

53:31

We combine all components of these sort

53:33

of technologies , but the reality

53:35

with us , for us , is when

53:38

we step back and we think

53:42

about the opportunity that's before us

53:44

. There are these data sets that you rightly

53:46

point out that are missing

53:48

. Ultimately

53:50

, what we're trying to do is

53:52

be able to master

53:55

sequence , the function in

53:58

a relationship , because

54:00

if I know the sequence

54:02

, the function in a relationship

54:05

, what ? That allows me to do

54:07

is program . I

54:11

can sit there and type in the

54:13

code to drive

54:15

the desired function . That's

54:18

what if you ? think about the

54:20

real , long-term

54:22

massive transformation

54:24

taking drug discovery

54:27

from the artisanal craft that is today and

54:30

making biology engineer yeah

54:32

, making it programmed . We're

54:37

on a path toward that .

54:42

We've spent a lot of time talking about AI , and

54:45

that's good . You're giving great concrete examples of

54:47

the work that it's doing . I'm

54:49

curious about your take on the

54:51

investor appetite form because there's

54:53

no doubt a curve a high curve right there's

54:56

a high curve going on right now . For

54:59

the past three years , we've been talking about investor

55:02

discernment . I've got

55:04

all these reasons to be discerning . Why

55:08

, why , right , I don't want to invest

55:10

in you , I should . I'm

55:12

like AI is one of those things that I feel like there

55:15

are so many companies that are riding that high

55:17

curve right now . That's an area where investors

55:19

should be discerning . If you come out , you're

55:21

coming out at a conference like this and

55:23

you say , well , machine learning and

55:25

AI , let's

55:28

pull back covers and have a closer look . Give

55:31

me some color around

55:33

. How , if

55:35

you're speaking to the investment community , which you

55:38

are , I get a lot of VCs listening to

55:40

you how they should

55:42

be discerning from reality

55:44

around artificial

55:47

intelligence forward .

55:48

Yeah , Biotechs right now . I

55:51

mean I always start with the people Right

55:54

. I mean I carefully

55:56

scrutinize the team of

55:59

the organization that we would

56:01

potentially work with partner , with the

56:04

people . Yeah , don't

56:06

look at , we've got an act , we're

56:09

outsourcing to a Look

56:11

at the individuals that are actually driving that

56:13

technology , and are

56:16

these folks among

56:18

the world class contingent

56:22

in those domains ? I

56:25

look at the publications that they've done

56:27

, neither while

56:29

they're within their organizations or in

56:31

prior lives , to

56:34

understand the quality of

56:36

their science . You

56:39

know , we recently published

56:41

one of our models . This is a model called

56:43

from up , and it

56:45

was in nature Right

56:47

. So when people ask me like you know , how

56:50

do I know the quality

56:52

of your machine learning ? You

56:55

can see the major publication Right

56:57

, you have a readout

56:59

Right and

57:02

you'll see . You know

57:04

, we're really blessed with an

57:06

extraordinary team of machine learning

57:09

scientists . I

57:11

think then you know you obviously have

57:14

to look at the technology as well , because

57:16

you know it's great to have great people , but if they're

57:18

not translating that into a technology

57:20

that that's really meaningful , then you

57:22

know the technology , though

57:24

, or the concept , I

57:27

think you know we can all use the same

57:29

words . The question then becomes

57:31

the translatability , and

57:34

you know , what I ultimately want to be judged

57:36

by is

57:39

the output . Are

57:41

we creating better drugs that we could have

57:43

otherwise ? Yeah , and

57:46

you know there are going to be a series of interim markers

57:48

until we hit the real marker . Do

57:51

these drugs make a difference in patients' lives ? And

57:55

that is , you know , the definition

57:58

of success that I

58:00

aspire to , that Generate aspires to . Because

58:03

of the nasancy

58:05

of some of these technologies , we're

58:08

just early in that journey and I think , unfortunately

58:11

, sometimes the long

58:13

lead times in pharmaceutical discovery

58:15

and development obscure

58:18

progress or

58:21

the view of progress . Right

58:23

, the revolution is happening right now . Yeah

58:26

, the outcome of

58:28

that revolution , given the

58:30

regulatory paradigm that

58:33

we have in front of us , is

58:35

maybe a decade away .

58:44

So that long lead time will remain in the interim

58:46

.

58:48

I don't think we clap clinical trials under

58:50

the current FDA guidance in

58:52

the very short term . So we're getting two clinical

58:54

trials quicker . We're getting two clinical trials

58:56

quicker . I mean you're taking , you know , a 10

58:58

to 15 year process coming

59:01

off a couple of years . Ideally

59:05

and theoretically you should

59:07

be getting the better answers with changes probabilities

59:09

, but again we're going to have to

59:11

wait time to judge that Right

59:13

. The

59:16

, I think you know

59:18

, will there be opportunities for further compression

59:20

? Absolutely , will we find

59:22

ways of compressing the clinical trial dynamics

59:25

? I believe so . I believe both . There'll

59:27

be better preclinical models and

59:29

ways of enriching clinical trials to

59:32

do that better . But you

59:34

know the nature of our business

59:36

right now . You know is

59:39

, you know , that

59:41

is the path that we have to

59:43

take and these technologies should

59:46

be held to the same level of scrutiny . As

59:49

you know , a technology that was discovered through more

59:51

traditional means .

59:54

Yeah , so I'll look

59:56

at your crystal ball right Questions

1:00:00

. Everyone hates that answer

1:00:02

. How long

1:00:04

before you and I are sitting here having a conversation

1:00:07

about the impact of these technologies on

1:00:09

clinical trials ? Because

1:00:11

that is at the inflection point

1:00:13

that we're at right now in terms of its

1:00:16

impact on molecular discovery

1:00:18

and design .

1:00:19

I think we're going to start to see impact over the next

1:00:21

few years . Yeah , I

1:00:24

think we'll look

1:00:26

back in the

1:00:28

very near future and

1:00:31

these technologies will be pervasive in

1:00:33

everything we do . Yeah Right , I mean

1:00:35

you know we're just learning

1:00:37

how to interact with

1:00:40

the chat GPT , Do you ?

1:00:42

have any concerns ?

1:00:43

Oh yeah , well of course I

1:00:47

mean these are extraordinarily

1:00:49

powerful tools . But

1:00:52

we already have some pretty extraordinary powerful

1:00:54

tools . These are just imbobial

1:00:56

You're dressed up with right , that's right . And

1:00:59

so you know , I mean like all

1:01:01

of these things have to be pursued with

1:01:03

due caution and due consideration

1:01:05

. But you know

1:01:07

, the reality is , I think the good outweighs

1:01:09

the concerns in a pretty

1:01:11

massive way . You

1:01:13

know , for us , you know it's

1:01:16

interesting . You know , one of the things that people say to me all

1:01:18

the time is like well , mike , how do you deal

1:01:20

with the hallucinations of generative

1:01:22

AI ? Well

1:01:24

, in a construct where

1:01:26

you have this tight integration of dry and wet lab

1:01:28

, we experimentally validate

1:01:31

everything . So

1:01:33

the hallucinations actually are enormously

1:01:36

powerful for our modeling

1:01:38

Because it's iterative .

1:01:40

Yeah , because we learn .

1:01:42

We learn from failure in a way that the industry's

1:01:44

never learned from failure . And

1:01:47

if the model is ultimately hallucinating

1:01:49

that protein doesn't express

1:01:51

and it doesn't work and that data

1:01:53

is already fed back , right

1:01:55

and so it helps us get to a better answer

1:01:57

faster , yeah , and you know

1:01:59

, those sort of bugs are actually features

1:02:01

in many respects in

1:02:04

this sort of world where you're experimentally valid

1:02:06

, I think if you're just thinking that computation

1:02:08

is going to solve everything , you know

1:02:10

I greatly discount organizations

1:02:14

in biology that

1:02:16

think computation will solve everything . Our

1:02:21

workforce is skewed . Everyone

1:02:24

considers us an AIML , a drug discovery company

1:02:26

. Our workforce

1:02:28

is skewed toward experimentalists

1:02:30

. We

1:02:32

have about two-thirds of our workforce that

1:02:35

are experimentalists . That's

1:02:38

just because we have to validate , we

1:02:40

have to measure , we have to generate the data . The

1:02:42

only way to do that is

1:02:45

through experimentation , to

1:02:47

do it reliably For

1:02:50

us . Certainly in the short , medium

1:02:52

and I'd say medium

1:02:54

to long-term , the

1:02:58

paucity of

1:03:00

data , functional

1:03:02

data , the data that we really care

1:03:04

about across a number of

1:03:07

domains in life

1:03:09

sciences , will lead

1:03:11

us to a place where we

1:03:13

are constantly

1:03:15

needing more and more information to

1:03:18

refine these computational approaches , to get to the best

1:03:20

answers to the status as possible .

1:03:22

Yeah , it's fascinating

1:03:24

. I'm curious about where

1:03:26

you are . If you want to share some information on

1:03:28

where you are right now and what the next

1:03:30

step might be for generating

1:03:32

.

1:03:32

Yeah Well , I mean excitingly

1:03:35

, our first two molecules entered the clinic in 2023

1:03:39

. You

1:03:41

know that is a

1:03:43

major milestone for any company . We're

1:03:46

extraordinarily proud that we're

1:03:49

now starting to see the benefits in

1:03:53

people . Those

1:03:57

programs we think could

1:03:59

address major areas of unmedical

1:04:01

need . Behind it , though , we have

1:04:03

roughly 20 programs . A

1:04:06

number of those are in partnership . We have six

1:04:08

in partnership with Amgen . I mentioned

1:04:11

the MD Anderson partnership , where we're now working on

1:04:13

three different targets . We're working with Roswell

1:04:15

Park Cancer Center on

1:04:18

a number of other targets . Then

1:04:21

we have an internal pipeline for generate

1:04:25

around 8 to 10 programs that we're working

1:04:28

on from a proprietary standpoint

1:04:30

. We're in this transitional

1:04:32

state of an organization where for

1:04:34

the first few years we were really a

1:04:36

pure technology company . We

1:04:39

were developing a technology . Now

1:04:43

we're morphing

1:04:45

into a technology

1:04:47

company that discover drugs

1:04:50

and that

1:04:52

develop drugs . You're

1:04:55

seeing a bit of a transformation within the organization

1:04:57

to bring in some

1:04:59

of the skills and capabilities I

1:05:03

think from building the organization that

1:05:06

creates some interesting cultural dynamics with

1:05:09

what we're working through . Clearly

1:05:12

we need

1:05:15

the experience of seasoned

1:05:17

drug hunters and drug developers

1:05:20

with the pioneering

1:05:22

spirit of the original technology

1:05:25

developers . It's something that we spend a lot

1:05:27

of time thinking through . Then

1:05:30

, from a technology perspective , we're

1:05:32

scratching the surface of application

1:05:35

. I always say to the team the

1:05:38

one thing I know definitively is

1:05:40

I don't know the best use of the technology

1:05:42

at this point . I

1:05:45

know there are multiple multi-billion dollar applications

1:05:47

for the technology , but

1:05:51

where this will have the greatest impact on

1:05:53

humanity we're still searching

1:05:55

and that leads to a bit of

1:05:57

a dynamic where

1:05:59

you're constantly balancing exploration

1:06:02

and exploitation . When

1:06:04

you find those areas where we can make

1:06:06

impact , how do we do more

1:06:09

of those versus expanding the

1:06:11

aperture ? The technology

1:06:13

is protein modality agnostic . It works

1:06:15

for antibodies , peptides , enzymes

1:06:18

, cell gene therapy , all our protein

1:06:20

, those

1:06:22

ABCs , bi-specifics . That's

1:06:25

a good place to be Right

1:06:27

. You have a ton of real estate

1:06:29

to cover . Which

1:06:32

of these will have . Which of those domains

1:06:34

will we have greatest advantage over traditional

1:06:36

approaches ? We'll still learning . My

1:06:38

guess is it's going to

1:06:40

be a more complex biology . If

1:06:43

you think about where

1:06:46

computers routinely outperform

1:06:48

humans , it's

1:06:50

in complex multiparticular optimization

1:06:52

. The more

1:06:54

complex the biological

1:06:56

construct in menu or stacks , I think the

1:06:58

computer ultimately will

1:07:01

show inherent advantage . Sure

1:07:05

, those challenges as we go forward

1:07:08

are super interesting

1:07:10

. You're trying to do that while you're trying

1:07:12

to execute on these clinical programs , bring the next generation

1:07:14

of programs to the fore and

1:07:17

developing a technology that I

1:07:19

believe will ultimately change

1:07:22

the way the

1:07:24

whole industry creates large

1:07:26

molecules . I

1:07:28

don't say that lightly

1:07:31

. We can debate how long

1:07:33

that may be , but

1:07:36

I think it's inevitable that over time

1:07:38

these approaches will

1:07:41

provide a

1:07:44

foundationally better

1:07:47

way of coming up with the right answer

1:07:49

than the traditional artisanal

1:07:51

approach .

1:07:53

I know we're running short on time here so I can ask

1:07:55

you questions Well into the evening

1:07:57

, but I'm curious about one of the things you mentioned

1:08:00

is the cultural change

1:08:02

of generating to

1:08:04

a drug development company , not away

1:08:07

from a tech but being built on a technology company

1:08:09

. I'm curious about

1:08:11

how , if you're doing that , if you work leading a

1:08:13

company that's doing that on an island

1:08:15

unto itself , the

1:08:17

challenge , it occurs to me might be more daunting

1:08:20

than it would be for flagship

1:08:22

pioneering partner CEO . How

1:08:25

much support like how advantageous is

1:08:27

it for you I'm

1:08:30

not insinuating that perhaps you're snuggling

1:08:32

drug development telling from other flagship companies

1:08:34

but how advantageous is it for you to be

1:08:36

part of that ecosystem ?

1:08:38

I mean , listen , it's a special

1:08:40

ecosystem . What

1:08:42

flagships we're doing for the bill is

1:08:45

really extraordinary .

1:08:47

It's not for the faint of heart .

1:08:51

Most innovation occurs in an incremental fashion

1:08:53

. Most

1:08:55

innovation from most organizations

1:08:58

is around the edges . Flagship

1:09:01

lives in the white spaces and

1:09:04

even the name of the organization is

1:09:06

highly symbolic , right Flagship

1:09:09

, pioneering yeah . A

1:09:12

lot of the time we spend thinking about

1:09:14

is what does it mean to be a pioneer

1:09:16

? What does it mean

1:09:18

to push into

1:09:20

new areas

1:09:22

, frontiers ? How

1:09:25

do you orient and how

1:09:27

do you make it inhabitable for

1:09:29

other settlers ? How do you keep

1:09:31

?

1:09:31

pushing boundaries Right , the

1:09:33

immigrant mentality I agree 100% .

1:09:35

Every time I talk with a flagship Absolutely

1:09:38

, and I think

1:09:40

one of the other things that we spend a lot of time thinking

1:09:42

about are dualities

1:09:44

of leadership . Oftentimes

1:09:47

we think about the

1:09:50

world in black and white terms

1:09:53

and every once says well

1:09:55

, no , let's live in the gray zone

1:09:57

. Oftentimes

1:09:59

, what we believe is that you need

1:10:02

polar experience

1:10:04

, you need to be able to balance between the two poles

1:10:06

, not in between , and

1:10:08

it's those sort of mindsets

1:10:10

that , I think , become super

1:10:12

valuable in these sort

1:10:15

of domains . And

1:10:19

you probably have heard a new bar talk about paranoid optimism

1:10:21

. That's one of those

1:10:23

dualities . Sometimes we need to be

1:10:25

paranoid , oftentimes we

1:10:27

need to be optimistic , and

1:10:31

rather than sitting there in between , how

1:10:33

do we embrace both of the poles as

1:10:36

we develop these sort of organizations ? And so

1:10:38

certainly it's

1:10:42

really nice when you're doing

1:10:45

things that people haven't done before

1:10:47

and you have

1:10:50

guides that have

1:10:52

been on similar journeys that

1:10:55

can help you understand from their own experiences

1:10:57

, and the team

1:10:59

at Flagship , as

1:11:02

I mentioned , stefan , who is

1:11:04

a member of the board who's created

1:11:07

, obviously , moderna . They've

1:11:09

been huge , I

1:11:13

think teachers For

1:11:15

me , for the team that generates , sharing

1:11:17

their experiences in a very

1:11:19

open , transparent way and

1:11:22

, more importantly , not just the successes

1:11:25

but the failures

1:11:27

, because we oftentimes talk

1:11:29

a lot about what went well

1:11:31

, but you sometimes learn the

1:11:33

most from

1:11:35

the mistakes that organizations

1:11:38

make on these sort of journeys .

1:11:39

Sure , it's stated

1:11:41

in the outside it's a failure

1:11:44

, is inherent , and if you're not taking it away from that

1:11:46

, then that's right , that's on you .

1:11:48

You're losing . And

1:11:51

the team at Flagship New

1:11:53

Bar , jeff Aba

1:11:56

, that we're kind of , the founding team of

1:11:58

Generate have been

1:12:00

extraordinary

1:12:03

mentors , ambassadors

1:12:06

, teachers , guides

1:12:08

. As we've gone on this journey and

1:12:11

for someone that is coming from a

1:12:13

big company doing

1:12:15

this for the first time , being

1:12:18

able to surround myself with those

1:12:21

sort of people and a whole cohort of other

1:12:23

CEO partners that are

1:12:25

also doing this journey

1:12:27

for the first time , with an array

1:12:29

of different backgrounds- that's

1:12:32

an enormous asset Because , as you know , you

1:12:34

talk to a lot of folks in these sort

1:12:36

of roles . These roles can be really

1:12:39

lonely , and having

1:12:41

that sort of ecosystem to pull upon

1:12:43

is of huge , huge

1:12:45

value and it

1:12:47

gives you a sense of

1:12:49

calm and

1:12:52

massive places of

1:12:54

uncertainty , and I'm enormously

1:12:56

grateful for that .

1:12:58

Well , I'm enormously grateful for you taking time

1:13:00

on this busy week to spend with me

1:13:02

on the business biotech , so I really

1:13:04

appreciate it . I'm Matt Pillar and you just listened

1:13:06

to the Business of Biotech , the weekly podcast

1:13:09

dedicated to the builders of biotech

1:13:11

. We drop a new episode with

1:13:13

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1:13:36

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1:13:39

, as always , thanks for listening .