0:01

Actually 90% of all drugs fail . So

0:03

basically you're spending five years

0:05

and then you have another five years and 90%

0:08

of the time will fail .

0:09

In a world where 90% of clinical

0:11

drug development falters , companies

0:14

invest a decade for the slim

0:16

chance of success . We

0:18

dive into this high stakes

0:21

journey with Dr Max

0:23

Jacobs , CEO

0:25

and co-founder of DeepMirror

0:27

.

0:28

Because , on one end , you can use machine

0:30

learning , artificial intelligence , to

0:32

take your past data on

0:34

drug compounds that you tested against

0:37

the target and then basically try to

0:39

predict other compounds that might even be more potent

0:41

.

0:42

AI is compressing years of research

0:45

into weeks , tackling

0:47

vast datasets and promising

0:49

to accelerate pre-clinical work

0:51

, but the challenge is steep

0:53

. Studying clinical outcomes

0:56

remains a formidable task

0:58

.

0:59

Individual drug . You have to first make it

1:01

, test it , and so on . It might

1:03

take two , three weeks or so . So testing

1:05

tens of thousands is really not possible

1:07

.

1:09

The goal , Level the

1:11

playing field , increase competition

1:13

and challenge norms . With

1:17

technologies from the 1960s still

1:19

prevalent , the industry is

1:21

ripe for disruption .

1:23

The pacemaker has remained the same from 1960

1:26

to now . Largely , it seems

1:28

like there is a certain level of bias

1:30

that sticks around

1:32

that is very hard

1:34

to think completely

1:36

out of the box .

1:38

From small molecules to advanced therapies

1:40

. We're at the cusp of an explosion

1:43

of ideas and innovation .

1:45

The biggest challenge for any software

1:47

company in this place has always

1:49

been to resist

1:52

digging for gold almost

1:54

.

1:55

Join us on Things of Change podcast

1:57

for a deep dive into the future of

1:59

drug discovery and healthcare

2:01

innovation . If

2:08

you had known how important the technology

2:10

economy was 20 years ago , would

2:13

you have done things differently ? The

2:16

internet , cell phones , the cloud

2:18

and data Things

2:21

have changed . We're here to talk about

2:23

it , hi .

2:25

I'm Jed , hi , I'm Shikhar

2:27

. Welcome to Things of Change

2:29

, your new economics and

2:31

technology podcast . Healthcare

2:40

in general is hard . Biotech

2:43

, healthcare , life sciences industries

2:45

are one of those industries where

2:47

there's just a massive amount of

2:49

data . These are far

2:51

too long Just because experimentation

2:54

. It costs a lot and you need to be precise

2:56

because ultimately , the end users

2:58

are the patients and consumers are there . Having

3:02

technology come in and improve

3:05

and influence this level

3:07

of data that researchers , scientists

3:10

and companies biotech companies , small

3:12

and large operate with is

3:15

a big boon . Your algorithms

3:17

could be like your buddy that could just

3:19

help uncover some patterns that

3:21

you were not seeing before , or

3:23

even identify potential

3:26

drug targets . It

3:28

seems like healthcare in

3:30

general is one of those industries that could massively

3:33

be influenced by the

3:35

wave of AI that we're seeing today . Today

3:38

, we're super excited to have Dr

3:41

Max Jacobs , co-founder

3:43

and CEO of DeepMirror , a health

3:45

tech company building

3:47

software , ai software platform

3:50

that helps researchers accelerate

3:53

experimentation testing

3:55

and , by doing so , unlock

3:58

creativity . Think of chat , gpt

4:01

or GitHub co-pilot , but co-pilot

4:03

for your research , like

4:05

research GPT that names

4:07

you to be trademarked .

4:08

Thank you for having me . We're excited to be here

4:10

, guys .

4:12

I touched on it a bit , but what are

4:14

your thoughts as to why this is

4:16

needed in healthcare in general

4:18

?

4:18

Yeah , I think it's very important to

4:20

maybe appreciate that in relationship

4:23

to other industries in health

4:25

tech and biotech and pharma these

4:28

data-driven approaches

4:31

are still very experimental

4:33

. Some of the larger companies

4:36

might have some internal research teams

4:38

already that do this kind of work

4:40

, basically providing algorithmic

4:43

solutions to other parts of the companies

4:45

, but it's really

4:47

not yet in the hand of end users

4:49

really so these kind

4:51

of data-driven technologies . It

4:54

is really at a stage now where people maybe

4:56

until a few years ago didn't really believe

4:59

that there could be some sort of vertical

5:01

solution in the industry , because everything is , as you

5:03

said , so complex , so difficult and

5:05

so specialized to the particular application

5:08

that nothing can really help

5:10

everyone in a way . But

5:12

now we're seeing more and more approaches and

5:15

attempts to actually do this

5:17

, to bring these kind of technologies

5:19

that look maybe very difficult into

5:22

the hands of people . I think you mentioned chatGPT

5:25

. That has been demonstrated by especially

5:27

these kind of approaches . Prior

5:29

to that , people worked with large language models

5:31

chatGPT is based on , but

5:34

these were technical people . It

5:36

didn't really get to any sort of end

5:38

users , but this kind of leap

5:41

of design almost made

5:43

it possible for the first time . This is what we're

5:45

trying to achieve as well , like having

5:47

a little leap of design and user experience

5:49

to finally bring AI-powered

5:52

algorithms to all these labs

5:54

that are not really using them at

5:56

this stage because , de facto , it's

5:59

in the hand of a select few companies in

6:01

some way that have successfully applied

6:03

it . But apart

6:05

from that , most companies are like basically

6:07

thinking oh , maybe I should get in

6:10

on it , but I'm not sure how yet

6:12

.

6:13

Yeah , one of the things we were really curious about

6:15

and you talked about it a little bit was how does

6:17

current state look ? What are the

6:19

kind of issues that researchers are facing

6:22

today with regards to coming

6:24

up with drug discovery , and

6:26

all these areas are really important for

6:28

the farm and biotech industry space

6:30

. Can you point out some really high-level stuff

6:32

that's really difficult for researchers

6:34

today to achieve without this technology

6:37

that we're talking about ?

6:38

So it is basically around the

6:40

idea of using

6:42

your past data to

6:44

try to somehow make

6:46

better decisions , right ? So

6:48

at the moment , it takes around

6:50

10 years or so , and

6:53

maybe a billion or two billion

6:55

dollars to take a drug to market

6:57

. Wow , and is that the average

6:59

? That's the average . Yeah , oh

7:02

my .

7:02

God , it's not a .

7:03

Gaussian distribution , necessarily , but it's

7:05

a lot of money and a lot of time , and

7:07

this is because there's so many things

7:09

you have to do right . Drug discovery goes from

7:11

initial research of trying to

7:13

figure out what can I attack to

7:16

treat cancer , to then

7:18

trying to find agents like , for

7:20

example , small drugs that might attack

7:22

this target and might diminish

7:24

its activity , then trying

7:26

to make sure that these agents are very potent

7:29

, that they are safe for people , that

7:31

they can be ingested by people

7:33

, to then finally putting them in the

7:35

clinic and by that point you're already like five

7:37

years down , and then you have another five

7:40

years of clinical work and

7:42

then this clinical time actually 90%

7:44

of all drugs fail . So basically

7:47

you're spending five years and then

7:49

you have another five years and 90%

7:51

of the time will fail . But this kind

7:53

of influences the thinking of a company

7:55

is quite dramatically right . And

7:57

then anything that

7:59

reduces the time to

8:01

get a gold shot , which basically means

8:03

shooting into the clinic , right

8:05

. So actually trying clinic once or

8:08

improves the outcome in the clinic

8:10

is super , super valuable . And

8:13

this is what it converges

8:15

to right , because on one end , you

8:17

can use machine learning and artificial intelligence

8:20

to , for example , take your past

8:22

data on drug compounds

8:24

that you tested against the target

8:26

or something like this , and then basically

8:29

try to predict other compounds that might even be

8:31

more potent , instead of trying

8:33

all these compounds . Right , so that obviously

8:35

speeds you up because you wouldn't have

8:37

to do so many experiments , because testing

8:39

sometimes an individual drug

8:41

, you have to first make it , test

8:44

it , and so on . It might take two

8:46

, three weeks or so . So testing tens of

8:48

thousands is really not possible , whereas

8:50

you can maybe test a thousand over a few

8:52

years , and then you really have to be smart which

8:55

ones you test . That's one way in

8:57

which these algorithms can help . The

8:59

other way is , of course , once they

9:01

go into the clinic , you have very little control

9:03

, right ? You do not know what

9:05

is going to happen . However , many

9:08

companies already generated some data

9:11

on clinical outcomes of drug

9:13

compound because they have tested quite

9:15

a few over the last hundred years . Right , see

9:18

, and this is another way you can actually

9:20

improve this you can try to predict what would happen

9:22

in the clinic given a certain drug , but

9:25

there would really have very small data

9:27

and also , in the other case

9:29

, these data set are very small . You

9:31

really have to leverage

9:33

a lot of tricks from very

9:36

nice machine learning technologies to make these

9:38

things work and actually pick up what happens in the

9:40

clinic is much harder than just trying to

9:42

predict stuff that happens before the clinic

9:44

. So I think we're now this day we can try to accelerate

9:47

the work before the

9:49

clinic or preclinical drug discovery

9:51

work . What the stage of trying to

9:53

improve clinical hit

9:56

rates .

9:56

That's still very difficult and

9:58

the companies that were successful also were

10:00

more towards the preclinical

10:03

work there's a direct

10:05

link between them spending all this

10:07

time , all this money and

10:09

still having just At

10:12

best ten percent chance of getting

10:14

a home run . All that is

10:16

still linked to higher

10:18

drug prices because they need

10:21

to recoup those cost that they

10:23

put for ten years and on top of actually

10:25

reducing costs for farmer .

10:27

That itself wouldn't really help because

10:29

it could still charge whatever they want . But if

10:31

you increase competition between companies

10:33

as well by actually enabling

10:35

everyone to get to the

10:37

same level of capability , that's

10:39

also key for this and in

10:41

a way , that is also the thing we're trying to

10:43

achieve a reliving the plane of these

10:46

kind of technologies .

10:47

A large part of what's in the

10:49

medical device industry , farm industry

10:51

and biotech are Improvements

10:55

or iterations from

10:57

what was created in the nineteen sixties

10:59

, of the seventies . And

11:01

you smile over there . I was so

11:03

surprised because I remember my first

11:05

day at walking with an

11:07

abit and they were like , okay

11:09

, this is not like rocket

11:12

science . The pacemaker has remained the

11:14

same from nineteen sixty to now , largely

11:17

. But I'd like to get

11:19

your take on that statement

11:21

that I made . It might not be completely

11:24

true , but it seems like there is a certain level

11:26

of bias that sticks

11:28

around that is very

11:31

hard to think Completely

11:33

out of the box , because this is tried

11:35

and tested and has been used

11:38

with the public . You have public health data

11:40

for forty , fifty years . So you're like , okay

11:42

, you don't want to deviate from that

11:44

normal .

11:45

It's difficult to deviate from the normal

11:47

if you have to , in the end , get fda

11:49

approval , and it's easier Get

11:52

approval for something that is similar to

11:54

something that is known . But

11:56

that's , of course , only one Issue

11:58

. I think this ties very well

12:00

into what we're mainly doing

12:02

right now , which is so , first way

12:04

people build drugs . So what's more ? Molecules

12:07

, which is like tiny molecules that easily

12:09

go into cells . They have been used

12:11

. There were the first drug that have

12:13

been used for the years and farm

12:16

company is developed quite

12:18

a few of them over the last century or so , and

12:21

initially these

12:23

were found mainly from natural

12:26

analog . So people are just looking at plants

12:29

trying to figure out what the ingredient

12:31

was that you , just you are the certain

12:33

disease of cured headache . Then

12:36

people start deviating from that to be

12:38

to try to find analogs of these

12:40

like slight changes . So you can think of

12:42

this almost . As this is huge desert of

12:44

molecules , most of them are toxic to people

12:46

. There's a few islands in there

12:49

that have promising molecules

12:51

, but the desert in between

12:53

is big and vast , so

12:55

finding these islands is extremely

12:57

difficult . That's why you Tend to often

12:59

go from natural analogs

13:01

right then that

13:03

was the status quo . Then

13:06

in the 80s and 90s people started doing

13:08

more with high content screening , so they would just

13:10

generate millions

13:12

of different compounds and just screen

13:14

them over some

13:16

sort of target that they wanted to attack . That

13:19

of course gave many more results , but often

13:21

these compounds in the end tend not

13:23

to be great in the clinic Because

13:25

the experiments that people did on these compounds

13:28

were actually not very predictive of

13:30

what happened later on in the clinic

13:32

. So it was a bit more difficult . And

13:35

then , especially now after

13:38

covid , we see quite a

13:40

lot of development , in particular people

13:43

called advanced therapies , which

13:45

are like therapies based not on small

13:47

molecules but based on RNA , cell

13:50

therapy , antibody therapy

13:52

, peptide therapy and so on . So

13:54

suddenly this whole zoo of new options

13:56

that pops up and

13:58

the whole space became way

14:01

more complicated . And

14:03

also people are going back to the small

14:05

molecules again and realize , oh shit

14:07

, the ones that we actually work with we're very

14:09

similar with each other , like all the different ones

14:11

that pass through the FDA , but

14:13

often very slight modifications just

14:16

of the same molecule

14:18

. And then a company such

14:20

as in silico , for example , come around the

14:22

corner and start designing molecules

14:25

that To see . If this weird

14:27

because they haven't really been done before , but

14:29

clearly they work . So there is some

14:31

, in some ways , you can harness , like these

14:33

generative approaches to

14:35

come up with new ideas , new

14:38

solutions to all the problems

14:40

, and it's a bit like a An explosion

14:42

of ideas that's happening right now , of

14:45

different viewpoints you can apply to all

14:47

solutions again .

14:49

So it's extremely exciting , can you provide

14:51

customer success stories or something

14:53

where you are able to just take some

14:55

sample data and was able

14:58

to Improve an influence

15:00

there .

15:01

research we did a lot of consulting

15:03

work prior to actually embarking on

15:05

product development , and

15:08

some of the consulting work , for example , was

15:11

image analysis , where we

15:13

essentially help people interpret

15:15

microscopy data In

15:17

early drug R&D . And

15:19

there it's straightforward because typically it

15:21

takes people a few minutes to analyze

15:24

an image where it takes an AI working

15:26

a few seconds . So you're really talking

15:28

about For the

15:30

fifties speed up here , so

15:33

that's very straightforward . In

15:35

in other ways , we did some work

15:37

on optimizing molecules

15:39

, as I mentioned , and there

15:41

, as you can see , with some of the bigger

15:43

companies , you can speed these things

15:45

up by what five to ten

15:47

X like you can find the most

15:49

optimal drug candidate about five to

15:51

ten times faster if you use some sort

15:54

of AI powers decision

15:56

making and In

15:58

turn and this is also

16:00

where we see our initial

16:03

product now , which we have been developing

16:05

last year , because people have been

16:07

working with small molecule

16:09

in the dawn of time , right

16:11

, and this is also the

16:13

modality that most people have experience with

16:16

. So we now pushing out

16:18

or initial product exclusively for small

16:20

molecule drug discovery , where

16:23

we essentially take customer data

16:25

. Let's say somebody tested twenty

16:28

, fifty compounds in the lab , then

16:30

we would they upload the data

16:32

to our app and then they can predict

16:35

the same properties that they measured

16:37

for these compounds for ten , twenty

16:39

hundred thousand other compounds . Then prioritize

16:42

the next step , take that back

16:44

to the lab , get the results back into

16:46

the app , then close the design

16:48

, may , test and analyze cycle

16:50

and this is really what

16:52

we're doing . So it's this idea of laboratory

16:55

optimization , by reducing the

16:57

amount of work you have to do and suggesting

16:59

the work you should do next , and there

17:01

we did a few case studies , which you can

17:03

also find the no web page , where

17:05

we think it can speed up by

17:07

about four , x and it's current state

17:09

. But we're still working on some benefits

17:12

there as well . Yes , really

17:14

exciting , because you go , you design a

17:17

compound in the app which is similar to your compounds

17:19

. You potentially make it

17:21

, then you test it , then you go back

17:23

again and design the next one based on the

17:25

information you have before . Yeah

17:28

, instead of like sometimes stabbing

17:31

a bit in the dark , you can really use this

17:33

to guide your thinking and

17:35

also , sometimes something might come up which you would not

17:37

have thought about . We have molecule

17:39

that maybe you have never really synthesized

17:41

before , but make sense and then

17:43

you might find something which is maybe a new

17:45

way , says this past desert .

17:48

Very interesting . So the

17:50

way you're describing it , it's like an application

17:52

that researchers can

17:54

basically log onto on

17:56

a browser , get into

17:59

the are , into your portal and

18:01

input their data set . And is it

18:03

something that they get right away ? Or

18:05

is it one of those things where , okay , they input it

18:07

and then they head back and then you're

18:09

doing the post processing for a day or

18:12

two and then they come back , see all okay

18:14

, what these are the potential outcomes , and

18:16

then test it out . Is that the workflow

18:18

that you're seeing ?

18:20

It has to be faster

18:22

than that because otherwise , okay

18:24

, becomes more of a consulting

18:27

business again and we really want to make

18:29

these inside available

18:32

like almost instantly , because

18:34

in the end people do

18:37

a lot right . They might try this and

18:39

they might try this . They want to play a bit

18:41

around on the app . So , yes , it's a cloud

18:43

, simple clouds , vertical

18:45

SAS app you would call it , and

18:48

we initially when

18:50

an interface with which people log in

18:52

the upload a bit of data that they click a button

18:54

, predicts or other data

18:56

that they might have provided themselves , what

18:59

generated using generate a guy

19:01

on our platform and

19:03

then it should take the pain . On

19:05

the data set size , anything

19:08

between four , what's

19:10

a two minutes to a

19:12

few hours for the really big ones

19:14

. But then nobody in

19:16

early drug discovery has big data sets

19:18

because you would start with Maybe

19:21

tens of compounds , that a couple hundred

19:23

compounds . Once you have a thousand compounds

19:25

, you either failed or

19:28

one in the clinic already , but that

19:30

you don't need a I am anymore because

19:32

you already had your shot .

19:34

That's really cool the people who are

19:36

ultimately their researchers .

19:37

And on top of the game , they

19:40

come into your platform knowing maybe

19:42

one of these hundred might work , so

19:44

they've already narrowed the problem space

19:46

and then finding the oasis

19:48

in that Problem

19:51

space is a lot quicker

19:53

and probably gets more signal yes

19:55

, so basically , initially it's

19:57

just about finding something that might

20:00

what we just call

20:02

the hit , which is like maybe one

20:04

, two , three compounds , so , and

20:06

then , once you have a hit , try

20:08

to generate a few compounds that are like in

20:11

this molecule space , to just have

20:13

a look , and then at that point

20:15

we , like already have narrowed

20:17

into the space a little bit . I

20:19

really want to look into that region , which

20:21

could still be massive right , and

20:23

then generate a few in that area

20:26

. Then it's really where I was , so where

20:28

would come in ? because it would basically take

20:30

the initial Results and

20:32

then go from that to generate

20:34

new suggestions . That might make

20:36

a lot of sense , because

20:38

going really from zero

20:41

to something here Is

20:43

it would be really interesting . But

20:45

in terms of what ML AI is

20:47

capable these days it's

20:50

really not that straightforward because

20:52

there's

20:55

basically infinite molecules you can make

20:57

right and then trying to

20:59

do that . We have a grant

21:01

now pending . That tries to do something like

21:03

this is a bit more , let's say , high

21:05

risk , so let's see if it works out

21:08

.

21:08

But yeah , maybe we'll know

21:10

next year's like a baseline

21:12

, and then you walk through it and you paper it

21:14

. Now you can only do a .

21:16

I am that's awesome once you know something

21:18

and where you get that something from

21:20

is really key .

21:22

Does it get better as you onboard

21:24

more customers onto the platform , or

21:27

are these companies like super touchy

21:29

about their data sets that you can never

21:31

use everybody else's data

21:33

sets to establish baselines for

21:35

new customers ?

21:37

Let's depend . some customers are really interested

21:39

in that , because particularly

21:42

academic customers are very interested

21:45

in making data more widely available

21:47

so that sometimes even ask Whether

21:49

this is an , and of course

21:52

it can be done because our database can

21:54

distinguish between , say

21:56

, hi Lee , it's the

21:58

tiny secret , just part of the database

22:01

where very happy , happy and I

22:03

could never be used for anything but serving

22:06

to a customer . But

22:08

other customers can also . We call

22:10

centers Multiplayer . They can

22:12

engage in multiplayer mode , which basically

22:15

means that they provide some data and then

22:17

that data might be used For other

22:19

people as well , which is not

22:21

so interesting when you try to make

22:23

drugs against the particular

22:25

target , because it really tends to work on

22:27

different things anyways . But it becomes

22:30

really interesting for the things

22:32

I mentioned before the idea

22:34

of trying to predict what happens in the clinic , because

22:37

if you have , if you have a few

22:39

compounds and you know whether these

22:41

metabolize well or whether these

22:43

are toxic , group

22:45

that with many other drugs of which

22:48

you know how they metabolize or how toxic

22:50

they are , that's really cool and

22:52

there's not that much data on

22:54

that right now . If people are willing to

22:56

share that , that's

23:07

something people often willing

23:10

to engage with . It also helps

23:12

the problem , because typically people do not want

23:14

to share the structure of a compound because that's

23:16

IP , because as soon as that becomes public knowledge

23:18

, it can be patented anymore

23:21

. But if you just say , all

23:23

we can use that model to build better

23:25

models for everyone , but your structure will never

23:27

be retrieval from that model , that's

23:29

really something some people are interested in so

23:32

, max , the biggest news , at least

23:34

in the US , is ozampic and

23:36

those weight loss drugs .

23:38

It's like the biggest news . Everyone's always hang

23:41

on , so now I can . It's

23:43

a really cool thing because you lose the weight

23:45

and then it's easier to control it then . But

23:47

it's interesting that it

23:50

stems from diabetes

23:54

drugs that we've used all this

23:56

one . When I was reading through

23:58

your blogs and the papers , it

24:00

just feels hey , hang on . Something like this where

24:03

you could expand what a

24:05

drug could do or just think of

24:08

it as a new way , like a new paradigm

24:10

through an AI

24:12

, would be a lot faster than 60

24:15

years of trial over the

24:17

world's population . So that seems

24:20

like something that we

24:22

might just start uncovering , where we use traditional

24:24

drugs and running

24:27

the co-pilot and we're like hang on , this

24:29

could also do this and go from there .

24:31

Yeah , Things like we are doing

24:33

is , let's say , you have a bit of information

24:35

, initially like again 20 , 50

24:37

compounds and so which are completely new and

24:39

have never been done before , but they're not

24:41

really that potent . They're quite toxic , so

24:44

they're quite annoying , but you

24:46

can use the information from them

24:48

to basically go and

24:50

screen public databases

24:52

. There's millions of compounds out there that you can just buy

24:54

, so you can then basically use

24:56

this information to try to repurpose one

24:59

of them . For example , I don't know

25:01

, these 50 compounds are really

25:03

good against killing this

25:05

particular cancer , but

25:07

they are also doing all kinds

25:09

of other bad stuff . So maybe I

25:11

use this information from these compounds

25:13

to then screen other compounds which

25:15

then flag up and maybe these

25:17

compounds have never been used against this

25:20

disease , and then you can

25:22

start just repurposing them because they're already

25:24

FDA proof . So that's

25:26

pretty cool and history is littered

25:28

with these kind of , let's say

25:30

, super versatile compounds

25:33

, right ? Doxycycline is one of them . I

25:35

tend to discover a new application of that

25:37

every month by chance . Then

25:40

Viagra is actually one of the most

25:42

famous ones , because Viagra was

25:44

initially . I can't remember what the

25:46

clinical trial was about Hard

25:48

, hard .

25:49

It was the hard .

25:50

Yeah .

25:51

There's also so many drugs that kind of failed

25:53

clinical trials , but they were already

25:55

shown to be safe for

25:57

patients and we don't even know

25:59

if they can do anything yet . So

26:02

that's also then really sad and often . These

26:04

then sometimes become available

26:06

and you can maybe ask a farmer company

26:08

if you can look into them on top of

26:10

this vast chemical space and say

26:12

we haven't yet explored . We still don't

26:14

really fully understand how , first of all , some

26:16

of the drugs we use actually work and

26:19

then , second of all , how

26:21

we can actually repurpose them to

26:23

do even completely different things , right

26:25

, because we already know that they're safe . Right , so

26:28

they can do so many things

26:30

potentially . It was really exciting . We

26:32

have medicine . We only scratch the surface

26:34

until now , and now we like

26:36

starting developing all these new therapies , all

26:39

these different approaches . Cell and gene therapies

26:42

are really awesome , especially for like cancer

26:44

.

26:44

Yeah , yeah , it's super exciting .

26:46

One of the things we wanted to ask was as

26:48

you envision the future of deep

26:51

mirror and the problems that you're solving today

26:53

. You look five , 10 years down

26:55

the line . How do you see

26:57

that ? What are the challenges that you

27:00

foresee right now that you're solving

27:02

, and how you'll get to the future state that

27:04

you want ?

27:06

The biggest challenge for any , let's say

27:08

, software company in this space has

27:10

always been to

27:12

resist digging for gold

27:14

, almost . Because once you have

27:17

something that kind of works

27:19

, everybody will try to tell you oh

27:21

, now you have to go all the end to the

27:23

patient . But then you tend to put

27:26

all your money onto a single component

27:28

at some point and then again

27:30

90% failed and then

27:32

you might have your cool platform software

27:35

which sped up your preclinical

27:37

work . Let's say it's five , x or

27:39

something like that . But that still

27:41

doesn't necessarily mean that afterwards you

27:44

will have an higher success rate necessarily

27:46

. But people still

27:49

think in this old school biotech

27:51

way where you basically go and

27:53

okay , so you play around a bit , you find

27:55

something , and then you put all your hosts and then you

27:57

become a proper biotech company that

28:00

then gets bought by a farmer or your partner

28:02

with some farmer companies and

28:04

so on , and staying clear of that

28:06

like trying to resist this digging

28:08

and really trying to stay a service provider

28:11

and delivering value to everyone

28:13

. That will be quite tricky

28:15

, but we see other companies in this space

28:18

that kind of manage to become

28:20

a fully vertical Sastau

28:22

solution . Benchling is maybe

28:24

one of the most famous one . Nobody

28:26

thought that something like Benchling would work when they came

28:29

around because effectively

28:31

it was like a workflow solution for researchers

28:34

, and why would researchers pay for that ? But

28:36

then over time people realized researchers go to

28:38

farmer companies , have big pockets and

28:40

suddenly Benchling has contracts that are more

28:43

than a million and annual revenue .

28:45

You said something that is so interesting

28:47

. I

28:49

see the promise of

28:51

this technology . I'm thinking , oh

28:53

, massive amounts of data . You put AI there

28:55

. We will be able to uncover

28:57

certain patterns . But you mentioned

28:59

something so interesting where sometimes

29:02

it might just be helpful to get

29:04

a cleaner set of data , a smaller set

29:06

of data , and then expand from

29:08

there . So that is really

29:10

interesting I have not thought of that

29:12

before and how AI can help with

29:15

.

29:15

Sometimes the less is more You're also

29:17

defining the question very well

29:20

for that particular data set . Like

29:22

in our early days , we once got contacted

29:25

by a company that essentially asked

29:27

us oh , we have these millions

29:30

of images here . Can we pipe

29:32

those through an algorithm and learn something

29:35

? And then again

29:38

, initially you think , OK , maybe

29:40

, but then you look into this and then

29:42

, as long as you don't really ask a

29:45

proper question about the data , you

29:48

just end up with nothing , Like

29:51

if you don't ask a question you don't know . you

29:53

won't find an answer because everything will be statistically

29:55

significant , and then you might be

29:57

amplifying noise as well , because it's a big data

30:00

set and you can't just have a look at it , and

30:02

you might not learn anything . It's like this whole

30:04

idea of looking at each grain

30:06

of sand on a beach and then trying to understand

30:08

what a beach is . It doesn't

30:11

really help . You have to have some sort of

30:13

question about the data you might

30:15

analyze . There's something similar

30:17

happening in academia now , where people were

30:19

thinking a lot about something called spatial

30:22

transcriptomics , which is basically the idea

30:24

of taking images of tissues

30:27

and then also looking at genetic information

30:29

in these images and basically generating

30:31

not even terabytes but petabytes

30:34

of data on this . And still

30:36

they don't really know how to apply this , because

30:38

just by looking and collecting data

30:40

without narrowing the question

30:42

, we don't know . This is where

30:44

we come in , right that people still are very

30:47

much required to define a

30:49

question , define the hypothesis

30:51

and then help get a

30:53

machine to help with the number crunching . Right

30:55

Can't replace it yet .

30:57

Yeah , max this was really

31:00

great . Before you leave us , we always

31:02

like to give our guests the

31:04

mic . You already have a mic

31:06

, but we want to pass the virtual mic

31:08

to you so that you can give a shout out to your team

31:11

, the work that you're doing and where people

31:13

can reach you , because we have a few

31:15

OVCs and founders who listen to

31:17

us and they're always interested to hear

31:19

what people are building and how they

31:21

can reach them .

31:22

Yeah , people can reach me straight on the max

31:25

at deepmirrorai , and

31:28

without the team , nothing of this would

31:30

have been possible . I'm

31:32

quite fortunate because I have two co-founders One

31:35

is more on the technical side , one is more on the product

31:37

side . I think the

31:39

synergy between the two of them is pretty

31:41

much amazing . We

31:44

also over the last year hired two

31:46

more people who help us more with

31:48

customer service and things , like Cecilia

31:50

and Jacob

31:52

, who's like basically

31:55

taking charge of all the machine learning , and potentially

31:57

in the next year we'll grow the team to

31:59

10 people . So I think

32:01

we might be looking to hire soon . That's

32:03

very exciting and at the moment we're very much

32:06

bootstrapped for our consultancy

32:08

contract . We have a bit of investment

32:10

. We might be looking for some later

32:12

this year to re-scale up the product Nice

32:15

. But , yeah , that's all . Very

32:17

. Sometimes I would say we're the pre-seed just

32:19

looking to find the product with which we'll go

32:21

on the seed rocket . But

32:24

you really have to . In

32:26

such a complex space , as we mentioned , you really

32:28

have to watch out that the thing you're building is actually

32:30

something useful .

32:33

I wanted to say thank you for coming on the show . It

32:35

was really nice to meet you and hopefully we

32:37

have you on again . Check in a couple

32:39

of years' time . See how deep mirrors do we

32:45

leave you with thought . The

32:47

future of the pharmaceutical industry

32:50

is on the brink of transformation

32:52

. From AI-powered drug

32:54

discovery to the emergence of novel

32:56

therapies , the landscape is

32:58

shifting rapidly . These

33:01

innovations usher in a new

33:03

era of health care . Only time

33:05

will tell . Stay tuned , stay

33:08

informed and join us again to

33:10

explore how things are changing

33:13

around the world . Until

33:15

next time , stay curious

33:17

.