Dur­ing BIO this year, I had a chance to mod­er­ate a pan­el among some of the top tech ex­perts in bio­phar­ma on their re­al-world use of ar­ti­fi­cial in­tel­li­gence in R&D. There’s been a lot said about the po­ten­tial of AI, but I want­ed to ex­plore more about what some of the larg­er play­ers are ac­tu­al­ly do­ing with this tech­nol­o­gy to­day, and how they see it ad­vanc­ing in the fu­ture. It was a fas­ci­nat­ing ex­change, which you can see here. The tran­script has been edit­ed for brevi­ty and clar­i­ty. — John Car­roll

Thanks to PPD Biotech for their spon­sor­ship of this event at #BIO2019.

John Car­roll:  This pan­el con­ver­sa­tion orig­i­nat­ed last March when I had an in­ter­ac­tion with some­body on Twit­ter, which was very in­ter­est­ing. It was some­body in AI who made the claim that they were in a po­si­tion to do some­thing dra­mat­ic with the way that phar­ma com­pa­nies de­vel­op new drugs, par­tic­u­lar­ly at the dis­cov­ery stage. And the num­ber that he used was more than $400 mil­lion for lead iden­ti­fi­ca­tion alone (which in­cludes the cost of cap­i­tal). This caused a sig­nif­i­cant re­sponse among a va­ri­ety of peo­ple who don’t have more than $400 mil­lion to do lead iden­ti­fi­ca­tion.

We ex­changed some emails af­ter that. He point­ed to a study that Steve Paul did when he was at Eli Lil­ly, I know some of the guys who were in­volved. One was Bernard Munos. Bernard and I had an ex­change. I said in this study you said the hard cost of lead iden­ti­fi­ca­tion at Eli Lil­ly is $146 mil­lion. I mean, you could start a heck of a com­pa­ny for $146 mil­lion and that’s not just for lead iden­ti­fi­ca­tion. And he came back and we had this ex­change: It’s a huge process that in­volves all this mon­ey and all this work.

So I’ve been think­ing about it ever since then. And what I re­al­ly want­ed to do to­day was to bring to­geth­er a group of ex­perts who are grap­pling with this area, this tech­nol­o­gy of ar­ti­fi­cial in­tel­li­gence and ma­chine learn­ing, be­cause I think every­body’s fo­cused on it in one fash­ion or an­oth­er. And some­times it’s prac­ti­cal and some­times it’s kind of very fu­tur­is­tic. But every­body up here to­day is in a very prac­ti­cal sit­u­a­tion, of hav­ing bud­gets, of hav­ing staffs, of hav­ing plans and work­ing every­thing out so they can ap­ply AI in a re­al world way right now, as it de­vel­ops in­to some­thing else. That is the way I would like to set the stage for this con­ver­sa­tion.

With that, I’d like to in­tro­duce every­body here to­day. We have Badhri Srini­vasan who is the head of Glob­al De­vel­op­ment Op­er­a­tions at No­var­tis. Tony Wood, Se­nior Vice Pres­i­dent, Med­i­c­i­nal Sci­ence and Tech­nol­o­gy for GSK. Rosana Kapeller, for­mer­ly of Nim­bus, cur­rent­ly an en­tre­pre­neur-in-res­i­dence at GV, or a com­pa­ny that I used to call Google. Hugo Ceule­mans, the Sci­en­tif­ic Di­rec­tor of Dis­cov­ery Sci­ences at Janssen. Saurabh Sa­ha, the Se­nior Vice Pres­i­dent of R&D at Bris­tol My­ers. And Shoibal Dat­ta, Vice Pres­i­dent, Head of Prod­uct and Tech­nol­o­gy, PPD Biotech.

So Rosana, you’ve been di­rect­ly in­volved in this at Nim­bus and al­so cur­rent­ly at GV. When you hear some of the dif­fer­ent pro­jec­tions in terms of what you can do with AI to­day, what’s your re­ac­tion? Is this some­thing that is go­ing to take a while to de­vel­op? What’s ac­tu­al­ly hap­pen­ing now?

Rosana Kapeller:  I’m very in­ter­est­ed in the in­ter­sec­tion of ma­chine learn­ing and drug dis­cov­ery. So that’s one of the rea­sons ac­tu­al­ly why I joined GV, to learn more about it. And I think that it’s some­thing that we all have to learn more about it be­cause it’s re­al­ly go­ing to aug­ment what we do in drug dis­cov­ery and al­so what we do in de­vel­op­ing drugs fur­ther dur­ing de­vel­op­ment.

Is AI go­ing to be sort of the panacea that is go­ing to cure all our prob­lems right now? The an­swer, I think is no. I think that ma­chine learn­ing at the cur­rent stage can be ap­plied to dis­crete pieces of the drug dis­cov­ery de­vel­op­ment spec­trum. But it’s not go­ing to get you — let’s iden­ti­fy a tar­get and get you to man in three years. It’s not go­ing to be that.

John Car­roll:  Okay, so every­body here has their own par­tic­u­lar take on this. When you hear it, some­thing like $146 mil­lion for lead iden­ti­fi­ca­tion, where do you see the op­por­tu­ni­ties? Where are peo­ple go­ing right now? Tony?

Tony Wood: John, the op­por­tu­ni­ty from our stand­point isn’t the $146 mil­lion, it’s the lit­tle bet­ter than 10% suc­cess rates dur­ing Phase II clin­i­cal stud­ies. And so our fo­cus re­al­ly is about a prob­lem that if, for ex­am­ple, we’ve shown from pub­li­ca­tions in ge­net­ics that you can in­crease your suc­cess rate to around about 20% if you fo­cus on ge­net­i­cal­ly in­spired tar­gets.

John Car­roll:  That would be 20% for every­thing go­ing in­to the clin­i­cals.

Tony Wood:  That’s right. It’s clin­i­cal study. So in func­tion­al ge­nomics, which if you like is the glue that con­nects ge­net­ics to the drug dis­cov­ery process­es that you’ve just been talk­ing about, that’s a place where we can gen­er­ate da­ta of the right shape to make AI/ML a worth­while pur­suit. And one thing that I re­al­ly want to get across here is that, for me, it’s not so much about the method, it’s about two things.

It’s about which prob­lem are you try­ing to solve? Is it an im­pact­ful one, in our case sur­vival in Phase II. And it’s about what da­ta shape can you cre­ate to sup­port the use of AI/ML method­ol­o­gy. Fun­da­men­tal­ly, AI/ML needs nar­row and deep da­ta. And our prob­lem in gen­er­al, be­cause of the last 20 years of the way we’ve con­duct­ed ex­per­i­men­ta­tion, is we’ve pro­duced wide and shal­low da­ta. So the fo­cus is where can we gen­er­ate da­ta that’s go­ing to re­al­ly al­low this tech­nique to be mas­sive­ly im­pact­ful.

Rosana Kapeller: I couldn’t agree more and I think that’s vast­ly un­der­es­ti­mat­ed; the shape of the da­ta that you need to ba­si­cal­ly be uti­lized by ma­chine learn­ing.

Badhri Srini­vasan:   Yes, and I com­plete­ly agree with both the com­ments here. So some­times it start­ed off as, okay, we can just ap­ply an AI or ML and then just run off with it. And to both of your com­ments, the da­ta that we have, we have spent the last so many years col­lect­ing this da­ta for one pur­pose, which is we get it on the CRF, we send it for some reg­is­tra­tion, be done with it. This is a dif­fer­ent kind of ap­pli­ca­tion. That’s go­ing to be the hard part. How do we get that da­ta? How do we get it in the shape we want and what is that da­ta and what is the prob­lem we’re try­ing to solve? So couldn’t agree more with this. That’s the hard part.

Saurabh Sa­ha: Maybe I can key off of one com­ment Rosana made. AI can be used in a very dis­creet way if you ask the right ques­tion first. You have to ask the right ques­tion, I think Tony men­tioned this. First you have to ask the ques­tion. Sec­ond, you have to have large la­beled da­ta sets. And third you have to have the cross func­tion­al ex­per­tise present to be able to do that analy­sis.

What we’ve learned in im­muno-on­col­o­gy is that one dri­ver may not an­swer all our ques­tions on whether a pa­tient is re­spond­ing or not re­spond­ing. We tend to think in bi­na­ry as hu­mans, but there are a num­ber of mark­ers where if you in­cre­men­tal­ly look at each one in ag­gre­gate, the quan­ti­ta­tive blend of those mark­ers may be much more in­for­ma­tive in terms of giv­ing us an idea of who’s re­spond­ing and not re­spond­ing. And one of the dis­crete ex­am­ples of this is an im­age analy­sis. So when we do any of our clin­i­cal tri­als now in the im­muno-on­col­o­gy space, we try to get biop­sy sam­ples at base­line dur­ing a pa­tient, when the pa­tient’s be­ing treat­ed and post-pro­gres­sion.

Now get­ting that da­ta and look­ing at histopathol­o­gy, it’s al­most im­pos­si­ble for a pathol­o­gist or for a hu­man to be able to look at those slides and be able to tell just vi­su­al­ly look­ing at the vi­su­al land­scape, which pa­tient is re­spond­ing and why they’re re­spond­ing. But us­ing ma­chine learn­ing now we’re able to work with some in­cred­i­ble star­tups in the Cam­bridge area where they can tag cell types. They can look at bound­aries of tu­mor cells ver­sus im­mune cells ver­sus stro­mal tis­sue and be able to feed tons and tons of these da­ta, these slides, these im­ages from all dif­fer­ent types of pa­tients and tell us, okay, what are the mark­ers that are as­so­ci­at­ed with the re­sponse? And which aren’t as­so­ci­at­ed with re­sponse? That’s a very dis­crete ques­tion that we can ask of ma­chine learn­ing at this time.

Tony Wood: That’s a re­al­ly fan­tas­tic ex­am­ple be­cause you have the case of im­age analy­sis be­ing able to gen­er­ate mas­sive amounts of da­ta and ac­tu­al­ly, quite frankly, see things that we can’t. To the hu­man eye, you look at these things, they of­ten look ex­act­ly the same. So that’s, for me, an area where here the method is do­ing some­thing that we clear­ly can­not do with any oth­er ap­proach. And the point about histopathol­o­gy ex­tends al­so in­to cell bi­ol­o­gy and the op­por­tu­ni­ty then to use cell phe­no­type on the back of func­tion­al ge­nomics to in­form changes as­so­ci­at­ed with ge­net­ic vari­ants and what have you.

And so for me, that’s what we’re look­ing for. We’re look­ing for the in­ter­sec­tion of mea­sure­ment tech­nolo­gies and da­ta sets which can al­low us to see the world of bi­ol­o­gy in a dif­fer­ent way from that which we’ve pre­vi­ous­ly per­ceived it.

John Car­roll: So how long has Janssen been work­ing on AI?

Hugo Ceule­mans: We have been at it for a cou­ple of years. I agree with what all the col­leagues here said, that the im­pact and de­vel­op­ment is huge. The chal­lenge with de­vel­op­ment is al­so what is the avail­able da­ta for that? There is an up­side to mov­ing a bit ear­li­er, to mov­ing to dis­cov­ery where cu­mu­la­tive­ly a lot of com­pa­nies have a lot more da­ta avail­able and present and an­no­tat­ed and la­beled, which is ex­act­ly fit for pur­pose if you want to go in­to this AI space. So hav­ing im­ages is fan­tas­tic, but very of­ten you do need to la­bel da­ta to make sense of them. So in the dis­cov­ery space, this is some­what eas­i­er and we do see that there is a lot of in­ter­est in putting all these ques­tions to the test right now. Do we have the right types of da­ta? Can we make true on this promise that is be­ing raised by the AI meth­ods? Can we join forces?

So yes­ter­day we made an an­nounce­ment that 10 phar­ma com­pa­nies are ac­tu­al­ly go­ing to join forces in a very pri­va­cy pre­serv­ing way. So they’re not just grow­ing, throw­ing the da­ta at each oth­er, but in a very pri­va­cy pre­serv­ing way, look­ing at the cu­mu­la­tive ware­hous­es of those phar­ma com­pa­nies to try to see whether we do have in­deed the right vol­ume of da­ta and whether by work­ing to­geth­er we can prove that these make a dif­fer­ence.

Saurabh Sa­ha: And Hugo men­tions a re­al­ly good point. It’s not just the qual­i­ty of the da­ta, which is a para­mount, it’s the ac­tu­al vol­ume of da­ta. Be­cause if you have a large sam­ple size and a few di­men­sions that you’re look­ing at, clas­si­cal sta­tis­tics prob­a­bly work just as fine, that’s what we did in grad­u­ate school. But when you have the num­ber of vari­ables or di­men­sions far ex­ceed­ing the amount of da­ta that you ac­tu­al­ly have, where the sam­ple size is much less than the num­ber of di­men­sions that you have, that’s when ma­chine learn­ing ac­tu­al­ly falls apart. That’s when you have a large dif­fi­cul­ty and the fact of the mat­ter is we’re at that in­fan­cy in stage. We just don’t have enough da­ta to feed in­to the al­go­rithms to get a mean­ing­ful out­put.

John Car­roll: There’s a va­ri­ety of dif­fer­ent per­spec­tives here on this par­tic­u­lar ques­tion and Shoibal, I want­ed to have you ad­dress one point that I’ve been in­ter­est­ed in for a while, which is it seems that if you’re a big­ger com­pa­ny, there are a lot more op­por­tu­ni­ties to see about how you can take ad­van­tage of this. You can in­vest in it over a pe­ri­od of years. You can see it bring fruit, you can try to in­crease your per­cent­ages from 10% to 20% which is a big thing Hal Bar­ron likes to talk about a lot. But is this some­thing that’s just re­served for the big­ger com­pa­nies? Or are there oth­er strate­gies here that the oth­er com­pa­nies can get in­volved in?

Shoibal Dat­ta: No, I don’t think it’s on­ly for the big­ger com­pa­nies. It’s in­ter­est­ing that the con­ver­sa­tion start­ed off with that kind of shape, den­si­ty and topol­o­gy of da­ta. And right now the abil­i­ty to gath­er large amounts of da­ta — it’s uni­ver­sal. I think it’s not unique to any one com­pa­ny. Fun­da­men­tal­ly, I think imag­ing clear­ly was the first to break with prac­ti­cal ex­am­ples of how it could be ap­plied and I think you can fol­low health­care. You saw it come in­to de­ci­sion sup­port first and now it’s go­ing to be em­braced in the main­stream.

I do think that with the per­va­sive­ness of de­vices and sen­sors, the amount of da­ta that will soon be avail­able to an­a­lyze is go­ing to be suf­fi­cient. The ques­tions and the abil­i­ty of ML to ap­proach those kinds of ques­tions, that’s, I think, what will help be the tip­ping point on this one. I do be­lieve we are go­ing to get in­to a new gen­er­a­tion of, for ex­am­ple, dig­i­tal bio­mark­ers or nov­el end­points based on these and they will be soon part of ex­plorato­ry stud­ies, if they’re not al­ready, in clin­i­cal tri­als.

John Car­roll: So Rosana, you work with peo­ple on the biotech side, on the small­er com­pa­ny side as well. What’s your take on this?

Rosana Kapeller: I do agree with what he’s say­ing. I think that biotech ac­tu­al­ly is in­vest­ing heav­i­ly in ba­si­cal­ly gen­er­at­ing the da­ta and cre­at­ing in­ter­sec­tions with ma­chine learn­ing and AI groups to be able to work on that. I to­tal­ly agree with that, but the point that I ac­tu­al­ly want to make is the shape of the da­ta and the con­tent of the da­ta that you use, that you need from ma­chine learn­ing.

So one of my frus­tra­tions, and I al­ways talk to my col­leagues and this is like, “Oh, but there’s so much da­ta in phar­ma. Why can’t we use all that da­ta?” Es­pe­cial­ly in chem­istry. Okay, you have all this sol­u­bil­i­ty da­ta, per­me­abil­i­ty da­ta and some. Why can’t we put this all to­geth­er and use ma­chine learn­ing to teach us all these dif­fer­ent fea­tures of mol­e­cules? You can’t be­cause the da­ta has been gen­er­at­ed over time, dif­fer­ent ex­per­i­ments, dif­fer­ent as­says. There is so much vari­a­tion in there that the ma­chine can­not in­ter­pret it.

John Car­roll:  And isn’t that why imag­ing is one of the ar­eas where you’re go­ing to find the first ex­ploita­tion be­cause it’s more pure, the in­for­ma­tion is more pure, it’s for the ma­chines?

Shoibal Dat­ta: I think the qual­i­ty of the da­ta and imag­ing in­her­ent­ly lent it­self to these ap­proach­es.

Tony Wood:  I think that that’s crit­i­cal. These meth­ods are very good at find­ing dis­con­ti­nu­ities in da­ta that have noth­ing to do with the ques­tion that you’re try­ing to solve. And when you’re putting a jig­saw puz­zle to­geth­er from pieces that come from dif­fer­ent puz­zles, which is a sort of anal­o­gy that we face in dis­cov­ery da­ta, there’s a re­al prob­lem as­so­ci­at­ed with that.

In ad­di­tion, com­pu­ta­tion­al chem­istry, I’m go­ing to let you drag me in­to com­pu­ta­tion­al chem­istry for a lit­tle while. We’ve been at this for 20 years now and the mes­sage that we al­ready have in place based on quan­tum me­chan­ics, based on oth­er pre­dic­tive ap­proach­es, you know this very well from your his­to­ry, they’re al­ready pret­ty good.

So where­as I’ve no doubt that AI/ML can solve those prob­lems, the im­pact that it’s go­ing to have rel­a­tive to the meth­ods that we cur­rent­ly have is prob­a­bly go­ing to be less­er than in ar­eas like the one that [Hal] and I con­stant­ly talk about be­cause there is a prob­lem that we sim­ply can’t solve right now and one which is en­abled by new da­ta col­lec­tion tech­nolo­gies that we’ve been talk­ing about like im­age analy­sis. So we can’t use any oth­er method to get to the bot­tom of deal­ing with these mas­sive da­ta sets.

So for us, it’s very much about what’s the right prob­lem to solve based on im­pact? Where are the da­ta sets that are go­ing to be suit­able for so­lu­tion of that prob­lem like­ly to be cre­at­ed as a con­se­quence of tech­nolo­gies that are be­com­ing de­vel­oped, crisper imag­ing, et cetera. And then build­ing our fo­cus around that, whilst at the same time putting in place a da­ta in­fra­struc­ture for every­thing else that will pre­pare the cul­ture in our or­ga­ni­za­tion to put da­ta at the cen­ter of every­thing we do.

Whether that’s AI/ML en­abling func­tion­al ge­nomics or it’s more typ­i­cal rou­tines, sta­tis­ti­cal or cal­cu­la­tion meth­ods en­abling the pre­dic­tion of small mol­e­cule or pro­tein struc­tures that will get to the sav­ings that your propo­si­tion that start­ed your ini­tial in­ter­est here.

Saurabh Sa­ha: So I agree with Tony and Rosana. I will say that if you look in the chem­i­cal space, one area that we’ve had some suc­cess, I think the fields had some suc­cess, is ask­ing spe­cif­ic ques­tions on: is a mol­e­cule… a HER chan­nel mod­u­la­tor or not? And so we had mil­lions of da­ta points in­ter­nal­ly and oth­ers in the in­dus­try have the same. And ap­ply­ing ML to that space and ask­ing that ques­tion, we’ve been able to get pre­dic­tion rates from 70% to 95% sen­si­tiv­i­ty speci­fici­ty. So that’s ac­tu­al­ly a very, very spe­cif­ic ques­tion.

Now that’s in a two-di­men­sion­al chem­i­cal space, what mol­e­cules look like on pa­per. But I’d love to ask Rosana — and from the Nim­bus and Schrödinger days — on three-di­men­sion­al space I think is much more dif­fi­cult when you’re try­ing to pre­dict on pa­per look­ing at the en­tropy or the in­ter­ac­tions of a mol­e­cule with the bi­o­log­i­cal prop­er­ties that you can pre­dict affin­i­ty, sol­u­bil­i­ty and oth­er things. And Jonathan Mon­tagu wrote a great blog on this on Bruce Booth’s site, which I think every­one should read. I think it’s a fan­tas­tic les­son.

Rosana Kapeller: It is hard and that’s some­thing that we’re work­ing on right now and I think ma­chine learn­ing is go­ing to be able to aug­ment that. So I think it’s the in­ter­sec­tion of ma­chine learn­ing and physics-based ap­proach­es may be able to ac­tu­al­ly solve part of the prob­lem right now and we’re do­ing a lot of work on that. There is some­thing we call mul­ti-pa­ra­me­ter op­ti­miza­tion be­cause, Tony, you will agree with that, the hard­est thing is to find one mol­e­cule that has the ide­al de­sir­able prop­er­ties that you need to make a drug. That’s why it takes so long to go from a hit to have a drug in peo­ple that ac­tu­al­ly acts. And if we could ac­tu­al­ly im­prove that even by 20% or 30%, that would be huge. And I think that’s where we’re putting a lot of ef­fort on.

John Car­roll: I think that the eco­nom­ics of R&D is some­thing that ob­vi­ous­ly every­one here is very in­volved in as well, try­ing to fig­ure out if there’s a more ef­fi­cient way of do­ing it to cut down not on­ly just the cost, but al­so the time that goes in­to this sort of thing. I was talk­ing to Clay Sie­gall from Seat­tle Ge­net­ics a cou­ple of days ago and he’s at the point now of hav­ing his sec­ond drug hit the mar­ket 21 years af­ter found­ing the com­pa­ny.

And these are long time­lines that we’re talk­ing about. Al­ny­lam took about 20 years to get its first drug out in­to the mar­ket­place. At the same time though, we are al­so see­ing that these com­pa­nies are com­ing along now, not just the the top 15, but all the rest of these com­pa­nies are com­ing up as well. And every­body’s go­ing to have their own kind of per­spec­tives on this. I am cu­ri­ous though, Shoibal, in terms of where the most com­mon pit­falls are right now for com­pa­nies that are be­gin­ning to look at AI, where do you see most of the ini­tial mis­takes?

Shoibal Dat­ta: I think it’s go­ing back to what I think Tony was say­ing — what prob­lem am I try­ing to solve? What da­ta do I have avail­able for it? And what are the right ap­proach­es? Be­cause there’s no mag­i­cal an­swer to that. You still have to go through that. I think every com­pa­ny went through where we have 30 years of clin­i­cal tri­al da­ta and every­body has in some way, shape or form tried to make sense of it. And most have strug­gled to come up with some­thing use­ful.

John Car­roll: So at a com­pa­ny the size of No­var­tis, you’ve got a glob­al op­er­a­tion like sev­er­al of the folks up here have right now. So how do you tack­le this from the ini­tial per­spec­tive. How do you get in­to AI in R&D for the first time?

Badhri Srini­vasan: I ac­tu­al­ly want to go back to what Shoibal said and I to­tal­ly agree. It’s a young sci­ence and sud­den­ly there seems to be a lot of promise and every­body says throw AI at it, ML at it, and sud­den­ly you should have a so­lu­tion. But what does that mean? What is the prob­lem that we’re try­ing to solve? Do we know that we have suf­fi­cient da­ta to ac­tu­al­ly ad­dress that prob­lem? I think there is a lot of ground­work that we need to do. And so we try to look at it in a more dis­ci­plined way, just be­cause we are the biggest, just be­cause we’ve got mon­ey to spend, just be­cause there’s a lot of ret­ro­spec­tive da­ta there.

And this is the oth­er point I want­ed to pick up from the ques­tion you asked Shoibal as well. There is a ton of ret­ro­spec­tive da­ta, but it’s all col­lect­ed with a dif­fer­ent pur­pose in mind. It has been all col­lect­ed over the last 10, 15, 20 years. What AI and ML need now is a very dif­fer­ent form of that da­ta. So when we look at it, we say, do we have that? Should we go col­lect that? And is it to solve a spe­cif­ic prob­lem? And sec­ond, is it a one prob­lem, one so­lu­tion? Or can we scale this ap­proach? Is it some­thing that we can then say, “Okay, now I’ve done this and I can ap­ply this across the board.”

If we take that kind of a dis­ci­plined ap­proach, we feel like we’ll start to get some­where. Not that we’re there now, but at least we’ll start to make progress in the right di­rec­tion. As op­posed to say­ing, “Here was a fan­tas­tic suc­cess, but I don’t know what to do af­ter that.”

John Car­roll: So, I am cu­ri­ous about where we’re head­ed here with this, which is what you were ad­dress­ing right now. If you get the da­ta right, if the da­ta stars com­ing in a uni­form and pre­dictable fash­ion, and you can use this across a va­ri­ety of ar­eas … that’s the first thing. Is the in­dus­try at the point where this is hap­pen­ing now, where the da­ta com­ing in is pure and fair­ly re­li­able? Or is there still more work to be done on that?

Tony Wood: Oh, I think we’re just right at the be­gin­ning of that process quite frankly. There are ear­ly in­di­ca­tions. We’ve talked about them so far, so I won’t re­peat them, where the promise is cer­tain­ly be­com­ing man­i­fest. And you can look across every as­pect from idea gen­er­a­tion through to the launch and mar­ket­ing of med­i­cines, and find ar­eas where you can see the op­por­tu­ni­ty for that to hap­pen. So, I think it will be­come much more preva­lent. I think the big­ger ques­tion then as we think about it is once you get there, how do you get the cul­ture right? Be­cause this is an ex­er­cise in putting the da­ta col­lec­tion strat­e­gy and an­a­lyt­i­cal strat­e­gy at the be­gin­ning of the ex­pe­ri­ence. And not at the end of the ex­per­i­ment when some­body gets hand­ed a hard disk full of ter­abytes of da­ta and has to find some sense in it.

So, one thing that I think is im­por­tant for us to con­sid­er is how the, let’s call them a mul­ti­lin­gual team, is as­sem­bled. And we have in­di­vid­u­als who can, if you like, bridge the gap be­tween ad­vanced an­a­lyt­ics, which comes from peo­ple that have come from a very dif­fer­ent back­ground, and sci­en­tists who un­der­stand the na­ture of the prob­lem that they’re try­ing to solve. So, I see lots of op­por­tu­ni­ty de­vel­op­ing over time. I think we will get to a point ul­ti­mate­ly where the biggest is­sue we face though is one of mak­ing sure that we get the right cul­ture to have da­ta-dri­ven or­ga­ni­za­tions.

Rosana Kapeller:  I’m just go­ing to agree with you in spades. I think that’s the biggest is­sue right now, the mind­set and the lost in trans­la­tion. The don’t speak the same lan­guage, so it’s go­ing across. And if you don’t have these teams to talk to each oth­er, you’re not go­ing to make the best of it.

John Car­roll:  You talked about this, the ad­ver­tis­ing folks. Just talk about that for a sec­ond, just in terms of the lost-in-trans­la­tion as­pect of this.

Rosana Kapeller:  Yeah, it’s com­plete­ly lost in trans­la­tion. My ex­pe­ri­ence was when I was at Nim­bus, be­tween the med­i­c­i­nal chemists and the com­pu­ta­tion­al chemists. And I think that in the be­gin­ning, it was very sur­pris­ing to me, they don’t speak the same lan­guage. They don’t even think about how to solve the prob­lems in the same way, and how to in­te­grate the dif­fer­ent so­lu­tions. And the on­ly way that we ac­tu­al­ly made it suc­cess­ful at Nim­bus was when we start­ed putting them to­geth­er, and mak­ing de­ci­sions to­geth­er. They had to co-lead the pro­grams and make de­ci­sions, to be able to speak the same lan­guage.

So, I think that is the same thing that we’re see­ing in ma­chine learn­ing and AI; a lot folks that come from ma­chine learn­ing, they don’t know much of bi­ol­o­gy. They don’t un­der­stand how you solve prob­lems in bi­ol­o­gy and vice ver­sa, so they don’t have a com­mon lan­guage. So, we have to de­vel­op that com­mon lan­guage.

Hugo Ceule­mans:  One of the chal­lenges is not just mak­ing the right type of da­ta, the right vol­ume of da­ta, the right qual­i­ty of da­ta, but then match­ing those dif­fer­ent da­ta sets. And some of those will be small, by all kinds of re­stric­tions. And it’s this mul­ti­dis­ci­pli­nar­i­ty that comes in in bridg­ing those. So, how do you bridge from a set that an­no­tates pure chem­istry ver­sus some that has al­ready a lot more bi­ol­o­gy, ver­sus some clin­i­cal as­pects? With this ex­change of mul­ti­dis­ci­pli­nar­i­ty comes in, in­to bridg­ing things. I think one of the biggest mis­takes that is of­ten made is we gen­er­ate an aw­ful lot of da­ta in one as­say or a few as­says. We’re re­al­ly good at that. Now, we throw ma­chine learn­ing on it.

To pre­dict what, to try to get to grips with what ex­act­ly? And I think I com­plete­ly agree, hav­ing that mul­ti­dis­ci­pli­nar­i­ty is ab­solute­ly key to bridg­ing all those dif­fer­ent as­pects.

Saurabh Sa­ha:  So, in im­muno-on­col­o­gy, we think that ul­ti­mate­ly mul­ti­ple mark­ers or com­pos­ite bio­mark­ers will lead the way in be­ing able to tell us which com­bi­na­tion of drugs will best be ef­fec­tive for a giv­en can­cer or pa­tient. And to the point that Rosana and Hugo make, it’s very dif­fi­cult … it’s ex­treme­ly dif­fi­cult to trans­late from dis­cov­ery, trans­la­tion­al, through the clin­ic. But it’s more dif­fi­cult just to fig­ure out how to trans­late or link ge­nomics da­ta, pro­teomics da­ta, flow da­ta, T cell ac­ti­va­tion da­ta, imag­ing da­ta just with­in the trans­la­tion­al space. So, just when we can try to come up with mark­ers that can pre­dict which pa­tients would re­spond.

Lis­ten­ing to this con­ver­sa­tion re­minds me of a com­ment that Free­man Dyson once made: does sci­ence evolve through ideas or tools? And if you look back at the era of the steam en­gine, the tools of the steam en­gine pre­ced­ed our un­der­stand­ing of the laws of ther­mo­dy­nam­ics. And that’s not ter­ri­bly dif­fer­ent from where we are to­day in terms of ar­ti­fi­cial in­tel­li­gence. We have the tool. But we don’t nec­es­sar­i­ly un­der­stand, okay, what are all the branch points in the de­ci­sion tree and the prob­a­bil­i­ties. How are they weight­ed to come to a rec­om­men­da­tion that this is what the mod­el out­puts? We re­al­ly don’t un­der­stand this black box.

So, I think peo­ple have com­pared this to ge­nomics of 20 years ago, where we had the op­po­site prob­lem. We had tons of da­ta, but we didn’t re­al­ly have the tools to ef­fec­tive­ly an­a­lyze it. And now we’re at the re­verse sit­u­a­tion. So, we’re kind of re­peat­ing his­to­ry here.

Shoibal Dat­ta:  So, sor­ry, but if I could ask a ques­tion. Do we need to un­der­stand the black box? And if you look at health­care and clin­i­cal de­ci­sion sup­port over there, there is not a fun­da­men­tal need to un­der­stand what the black box is. And in R&D, the goal posts have shift­ed as we un­der­stood more about the sys­tems that we study; in­creas­ing­ly have a need to be able to ex­plain why that’s hap­pen­ing, than it used to be be­fore. What’s the right thing to do over here?

Saurabh Sa­ha: Yeah, that’s a great ques­tion. So, I fun­da­men­tal­ly be­lieve that cod­ing lacks moral­i­ty. So, if you’re a physi­cian and you are get­ting a piece of pa­per that says, “This pa­tient should get this set of drugs,” do you re­al­ly be­lieve there’s qual­i­ty in the da­ta? Was there poor qual­i­ty, good qual­i­ty? Do you have trans­paren­cy as to how that da­ta came about, it land­ed? And how it was an­a­lyzed, this black box? And third, has it been bi­ased in any way? One of the chal­lenges with ML is that you on­ly are re­al­ly be­ing able to pre­dict based on your train­ing sets. Large­ly your train­ing sets that your work­ing, that’s be­ing fed in­to the mod­el.

And if you have un­mea­sured or un­known da­ta that is im­por­tant for let’s say, pre­dict­ing a re­sponse for a pa­tient, that da­ta’s not be­ing picked up. Be­cause it’s un­known, it’s un­mea­sured. So, by de­f­i­n­i­tion, it hasn’t gone in­to your train­ing set. So, if you’re miss­ing that com­po­nent and you’re mak­ing a rec­om­men­da­tion, and you’ve left that out, then I think it’s a fun­da­men­tal chal­lenge. Be­cause you re­al­ly don’t know how you got there. Maybe it was Google, I don’t know which com­pa­ny it was, where they looked at reti­nal scans and tried to pre­dict MIs, my­ocar­dial in­farc­tion. And what they end­ed up ac­tu­al­ly scor­ing for was the age of the pa­tient, in­stead of ac­tu­al­ly pick­ing up how the reti­na can pre­dict that.

Tony Wood: Let me pick up on the point you made ear­li­er here. Be­cause there’s a great da­ta in­te­gra­tion op­por­tu­ni­ty. We can now de­scribe the char­ac­ter of cells at mul­ti­ple lev­els of phe­no­typ­i­cal ab­strac­tion from DNA struc­ture, all the way through to an im­age. And this is part of the prob­lem with the com­plex mol­e­c­u­lar bio­mark­er propo­si­tion, you need how­ev­er to have some ground truth in or­der to con­nect that mas­sive vari­abil­i­ty to some­thing that you know to be true. And that ground truth in the con­text of our strat­e­gy is hu­man ge­net­ics. So, if you go from high con­fi­dence vari­ance to deep char­ac­ter­i­za­tion of cell char­ac­ter, then I think you’re in a place where we can start to re­solve some of these prob­lems with re­gard to the use of mol­e­c­u­lar bio­mark­ers, pa­tient se­lec­tion.

Be­cause fun­da­men­tal­ly, we’re an­chor­ing the whole ex­er­cise on some­thing that we know to be true. And so, that’s where our fo­cus in func­tion­al ge­nomics comes from. It’s not just about tar­get iden­ti­fi­ca­tion. If we get that right, it echoes out in­to a lot of the oth­er process­es that we’ve been talk­ing about. One ad­di­tion­al point around the black box that I want to bring in at this point, which is prob­a­bly worth­while con­sid­er­ing, I’ve nev­er en­coun­tered a com­put­er yet that didn’t pro­duce an an­swer. That the is­sue is not the an­swer, the is­sue is the cost of find­ing out whether the an­swer is cor­rect or not. And that’s a chal­lenge for ex­am­ple in the syn­thet­ic area.

What we need to be able to do with these meth­ods, is use the fact they can in­te­grate across mul­ti­ple dif­fer­ent di­men­sions. And start not just wor­ry­ing about, can I find an ac­tive com­pound? But can I de­sign an ac­tive com­pound that I can syn­the­size at ton­nage scale lat­er on, with­out hav­ing to go through the de­vel­op­ment process op­ti­miza­tion. Then you’re at a place where you’re de­sign­ing for every­thing right from the be­gin­ning. And that’s when I think you get the huge im­pact from sav­ings, with re­gards to re­al­ly in­te­grat­ing chal­lenges across the time scales.

John Car­roll: I would like to shift a lit­tle bit away from the re­al world, what you’re do­ing to­day, and the chal­lenges to where you think this is go­ing. Be­cause where we are to­day is not where this is go­ing. And there are all sorts of dif­fer­ent ideas about this. So, where are we go­ing to be four, five years down the road? Where is this head­ed? Be­cause four or five years in biotech is noth­ing. I mean, it’s now. So, I’d like to get your ideas. Badhri, what do you think this is go­ing to be for No­var­tis, reimag­in­ing med­i­cine?

Badhri Srini­vasan: So, first of all, I think where we are go­ing is not where we are to­day. And that’s a re­al­ly nice segue in­to this. I think where we will be is … the in­dus­try is ma­ture, you’ve heard a lot of peo­ple here say we are at a bit of an in­flec­tion point. We’re try­ing to un­der­stand the space. This is ear­ly days in us un­der­stand­ing what to do with this da­ta and how to fo­cus it on a prob­lem. Where we will be, I hope­ful­ly where we’re get­ting there, is we’re us­ing more and more da­ta to say, “Okay, do I un­der­stand the dis­ease state bet­ter?” Be­fore I even start any kind of treat­ment, “Do I un­der­stand the dis­ease state bet­ter? If I can un­der­stand the dis­ease state bet­ter, can I bring med­ica­tions that are ap­pro­pri­ate to that dis­ease state?” That depth of un­der­stand­ing I think is where we will be four, five years from now. If you’re then say­ing 10 years or (10)ish from now, again, which is not a long time­frame in a phar­ma biotech world, I think we will start to see that shift to ac­tu­al­ly us­ing that da­ta. To then say, “How are we short­en­ing the time­frame? How are we im­prov­ing our prob­a­bil­i­ty of suc­cess, et cetera?” But in that four, five year time­frame, I think it’s more of a bet­ter un­der­stand­ing of the in­sights that we have. Whether it’s pa­tient in­sights or dis­ease in­sights, or even mol­e­cule, com­pound in­sights, I think that’s where we will start.

John Car­roll:  Tony, I know this is a con­ver­sa­tion at GSK.

Tony Wood:  Yeah, look, we’re go­ing to be in a place where we have bet­ter tar­get iden­ti­fi­ca­tion, along the lines that I’ve de­scribed. And as a con­se­quence of that, an abil­i­ty to bet­ter iden­ti­fy pa­tients who are like­ly to ben­e­fit from our med­i­cines.

John Car­roll: That’s it?

Tony Wood: That’s our fo­cus.

John Car­roll:  Rosana?

Rosana Kapeller:  Would you say I’m an out­spo­ken per­son?

John Car­roll:  Well, I hope so.

Rosana Kapeller:  As every­one is say­ing, we’re very much in the be­gin­ning. And we’re still try­ing to fig­ure out how to put this all to­geth­er, and what’s go­ing to be the ben­e­fit of it. But I think that we right now are fac­ing what Ko­dak faced with dig­i­tal cam­eras. If we don’t em­brace it, most of us will have a Ko­dak mo­ment…

Tony Wood:  Mm-hmm (af­fir­ma­tive).

Rosana Kapeller:  Da­ta is go­ing to con­tin­ue to ac­cu­mu­late, we’ll con­tin­ue to gen­er­ate more da­ta. We are go­ing to fig­ure out how to get da­ta in the right shape, for­mat, et cetera. We will work on the cul­tur­al piece. We will make im­prove­ments for pa­tients and all of that. So, I think peo­ple should em­brace it in­stead of try­ing to push it away. That’s my per­son­al opin­ion.

John Car­roll: I think fear is dri­ving this as much as any­thing; the fear of miss­ing out. Hugo?

Hugo Ceule­mans: I think one key as­pect of where we’ll be go­ing is in­te­grat­ing more. Be­cause the one dif­fer­ence ver­sus con­sumer, is that in con­sumer there were a few mono­lith­ic da­ta own­ers who have mas­sive ac­cess to the da­ta. I think in phar­ma, in clin­i­cal, that is not the case. The stake­hold­er land­scape is way big­ger. So, we will need modal­i­ties to de­ploy AI across mul­ti­ple da­ta own­ers, across mul­ti­ple stake­hold­ers, in or­der to achieve the dream of where to land this. And I think a lot of the new­er play­ers, they will com­bine a vi­sion of method­ol­o­gy com­bined with, “This is com­ple­men­tary da­ta, this is da­ta that big phar­ma or the clin­ic does not have. This is a miss­ing in this.”

And we bring this to­geth­er through an AI that can learn across mul­ti­ple stake­hold­ers, while re­spect­ing the IP rights and the busi­ness in­ter­ests, but al­so the pa­tient rights, the clin­i­cal as­pects of all these stake­hold­ers. And I think that will be a ma­jor chal­lenge, that is very spe­cif­ic to the health­care space. That in some of the oth­er ar­eas where AI has made ear­ly progress, was less promi­nent, the frag­men­ta­tion of the space. That’s some­thing we need to con­quer.

John Car­roll:  I saw a study done not too long ago, about a month and a half ago, where they had looked in­to the suc­cess rates of drug de­vel­op­ment. Which is kind of an ob­ses­sion in R&D … and one that I share. It was in­ter­est­ing, be­cause they looked at all the dif­fer­ent phas­es, go­ing through the whole pre­clin­i­cal, Phase I, Phase II, Phase III. And they saw a sig­nif­i­cant in­crease in Phase III suc­cess­es. Every­thing else was pret­ty much the same, and noth­ing has re­al­ly changed all that dra­mat­i­cal­ly. It’s pret­ty bad. The 10% or what­ev­er that might be, is poor. And I think that most peo­ple would agree if you could make that 20%, you’d be cel­e­brat­ed around the world.

I think one of the rea­sons why the late stage de­vel­op­ment is work­ing out bet­ter, is com­pa­nies, big­ger com­pa­nies and all com­pa­nies, are do­ing bet­ter in terms of de­cid­ing what they want to take in­to Phase III. And al­so, there’s been a dri­ve to­ward more spe­cial­iza­tion. Where you see the com­pa­nies, par­tic­u­lar­ly the larg­er com­pa­nies, are be­ing very clear about where they think they can make a dif­fer­ence. Where they can come up with the new prod­ucts that’ll have an im­pact on mar­ket. So, does AI con­tin­ue to dri­ve spe­cial­iza­tion? Does it make it more im­por­tant to un­der­stand par­tic­u­lar ar­eas of spe­cial­iza­tion? And will this be a con­tin­u­a­tion of that process?

I’m cu­ri­ous from Janssen’s per­spec­tive, be­cause you do cov­er quite a lot of ter­ri­to­ry.

Hugo Ceule­mans: You need to in­te­grate across a big­ger space. Be­cause those nich­es will get small­er and small­er. Dis­eases will be split up in sub­types. You need suf­fi­cient da­ta pow­er. But I ab­solute­ly do think if you have a very clear vi­sion about, “This is the type of dis­eases. This is the type of treat­ments we want to aim for,” this will be en­abled by AI and by in­te­gra­tion across larg­er and in­ter­con­nect­ed da­ta sets.

Tony Wood: Okay, I think whether or not dis­eases are split in­to sub­types is an in­ter­est­ing ques­tion. And our fo­cus is very much in the ear­ly phas­es, to fo­cus on the bi­ol­o­gy. And it may be that by fo­cus­ing on the bi­ol­o­gy and what we learn there, that we get to think about the con­stel­la­tion of dis­eases in a very dif­fer­ent way. So, for me I think the ju­ry’s still out on that one. We may find that what we do is just group dis­eases in a dif­fer­ent way from the pre­vi­ous ap­proach we’ve tak­en through di­ag­no­sis and pre­sen­ta­tion.

Saurabh Sa­ha: John, to your point about suc­cess rates, so I think we may be look­ing at the same study. I think it was a CRM study pub­lished in Na­ture Re­views Drug Dis­cov­ery. The suc­cess rate from Phase I to launch is about 7%, 8%. From Phase II to launch is about 14%, 15%. And Phase III to launch is about 60%. So, if you look at that da­ta, there’s in­her­ent­ly some­thing very in­ter­est­ing about that. It says that, “Why are we do­ing these long com­plex Phase I stud­ies, if you’re re­al­ly not gain­ing much ben­e­fit and suc­cess from Phase I to II, in terms of trad­ing, your port­fo­lio? You’re re­al­ly see­ing huge leaps when you go from Phase III to launch.”

So, I think where we as a com­pa­ny and I think the in­dus­try would ben­e­fit from AI or what­ev­er you want to call it, is in­creas­ing the prob­a­bil­i­ty of get­ting that Phase I study to read out much more da­ta. Much more rich da­ta, where you have a clear proof of con­cept. That no­tion’s been around for over 20, 30 years. But a re­al proof of con­cept, one that tells you that, “You know what? Stop the pro­gram, or con­tin­ue and you’re go­ing to have a Phase III-like suc­cess rate in the Phase II set­ting.” One of the things that is clear, I think is that sci­ence evolves. Or there’s rev­o­lu­tions in sci­ence based on be­ing able to mea­sure things bet­ter.

(If) we were to mea­sure a pa­tient’s tu­mor, for ex­am­ple, to an in­fi­nite amount. Like, know every­thing that’s go­ing on in a pa­tient’s tu­mor and have the tools to be able to do that, to an­a­lyze the con­se­quences of those da­ta that we’re get­ting out of that tu­mor, one could pre­dict I think with al­most cer­tain­ty what will hap­pen ul­ti­mate­ly to that tu­mor, and what with Dar­win­ian evo­lu­tion may end up hap­pen­ing based on the fun­da­men­tal ge­net­ics of that can­cer. So, what I think when you say four to five years, in the last two years, we’ve seen a huge dif­fer­ence in the can­cer space. Be­cause it’s on­ly in the last two years that we’ve been mean­ing­ful­ly col­lect­ing sam­ples at base­line, on treat­ment, post-pro­gres­sion.

And it’s on­ly in that two-year pe­ri­od that we’ve ac­tu­al­ly de­ployed all these now tools to look at an­dro­genic­i­ty by se­quenc­ing mu­ta­tions, look­ing at in­flam­ma­tion, by look­ing at PD-L1, look­ing at CD8, and look­ing at these mark­ers. And an­a­lyz­ing the tran­scrip­tome, look­ing at gene ex­pres­sion in sin­gle cell RNA analy­ses. So, all that’s hap­pened in the last 18 months or two years. Now prospec­tive­ly in the next four to five years? Think about where that’s go­ing to be. We’re go­ing to have all that da­ta, be­ing able to mea­sure a tu­mor as pre­cise­ly as we pos­si­bly can. So, there’s go­ing to be great ad­vances in maybe the next two years.

John Car­roll:  I’d like the CRO per­spec­tive here, be­cause in a lot of dif­fer­ent re­spects, you can’t spe­cial­ize. You have to cov­er a large ter­ri­to­ry for a va­ri­ety of dif­fer­ent peo­ple. But at the same time they’re com­ing to you and ask­ing about this, so how do you ad­dress what … where you’re go­ing to be in four or five years from now?

Shoibal Dat­ta:  Sure, and I think to try and … dis­till some of the con­ver­sa­tion on this ques­tion, I mean, the in­dus­try as a whole is mov­ing … I think we’ve gone past the plumb­ing, com­put­er in­fra­struc­ture, cu­ra­tion, in­ges­tion is­sues. Every­body’s kind of fig­ured that part out. We’re be­gin­ning to un­der­stand what the lim­i­ta­tions are. And back to the ques­tion of da­ta qual­i­ty, da­ta depth, da­ta den­si­ty, and we re­al­ly see dig­i­tal­ly en­abled tri­als, the per­va­sive­ness of clin­i­cal grade con­sumer, sort of, de­vices and sen­sors as pow­er­ing some of that. And our fo­cus is re­al­ly to be able to run those kinds of stud­ies to the same stan­dards of in­tegri­ty and qual­i­ty like we do tra­di­tion­al tri­als. So we have to pre­pare for that. And it’s not just a tech­nol­o­gy prob­lem. It’s a tech­nol­o­gy process… It’s a mul­ti­func­tion­al, cross-dis­ci­pli­nary ex­er­cise.

John Car­roll: Saurabh, you brought up the idea about what’s go­ing on in can­cer right now. Ob­vi­ous­ly, Bris­tol My­ers has a stake in the can­cer field, in I/O in par­tic­u­lar. And there’s a lot of mon­ey go­ing in­to that. It seems like those com­pa­nies that are deeply in­vest­ed in­to cer­tain spe­cif­ic ar­eas are go­ing to find it the most pro­duc­tive when it comes to us­ing AI as they build up ex­per­tise, and as they con­tin­ue to build up the da­ta and every­thing else with it.

I’ve seen some of the sta­tis­tics in terms of the in­vest­ment in can­cer ver­sus every­thing else. It’s al­ways way out front. There’s been these mas­sive ad­vances. You’re get­ting in­to small­er and small­er nich­es here, where you’re break­ing up can­cers in­to very spe­cif­ic sub­pop­u­la­tion groups, and so on. So you can see how AI could make a big dif­fer­ence there. You could see how that would hap­pen.

I’m cu­ri­ous from the rest of every­body’s per­spec­tive here, what are the oth­er ar­eas where you’re go­ing to find the great­est pro­duc­tiv­i­ty? [Daphne Koller] works with Gilead. Gilead knows the liv­er re­al­ly well. This is like one of those ar­eas where, “Give me the liv­er,” and then they’re go­ing to fig­ure it out. So I’m cu­ri­ous, what are your spe­cif­ic ar­eas where you find the great­est op­por­tu­ni­ties as it re­lates to the dis­eases? Which dis­eases, oth­er than can­cer per­haps, are we go­ing to find the great­est ad­vances in? Badhri?

Badhri Srini­vasan: So I think on­col­o­gy al­ways has a tra­di­tion of lead­ing, so that’s for sure, and as you point­ed out as well. But I ac­tu­al­ly think the ap­pli­ca­tions go far be­yond that. I can think of oph­thal­mol­o­gy, for ex­am­ple. I can think of res­pi­ra­to­ry and liv­er. We don’t un­der­stand re­al­ly what’s go­ing on in that, for ex­am­ple, in that space, in the im­munol­o­gy, he­pa­tol­ogy space. I think the ap­pli­ca­tions are far be­yond and ap­ply to oth­er ar­eas.

They are catch­ing up now. Used to be that on­col­o­gy was al­ways in the lead, and the oth­ers were a lit­tle bit more “tra­di­tion­al,” but I think the oth­er ar­eas are catch­ing up. Some of the im­age analy­sis that we see, etc, is ex­tend­ing to oth­er ar­eas as well. So I think it’s across the board.

Tony Wood: And where you un­der­stand the ge­net­ics of the dis­ease, and you can take that un­der­stand­ing and point to the cells in which quan­ti­ta­tive trait loci are ex­pressed, you have the ba­sic in­gre­di­ents to fol­low a func­tion­al ge­nomics ap­proach en­abled by AI/ML, to ad­dress any par­tic­u­lar dis­ease with high con­fi­dence in the way that I’ve de­scribed. So we don’t think about it so much from a point of view, is it can­cer, or is it neu­rode­gen­er­a­tion? But rather, where are the ba­sic in­gre­di­ents that al­low us to con­nect to­geth­er these tech­nolo­gies and im­prove our chances of suc­cess?

Saurabh Sa­ha: So I would add, in our case … So we’re not just a can­cer com­pa­ny. We have a num­ber of oth­er dis­ease ar­eas — in car­dio­vas­cu­lar, fi­bro­sis, in im­muno sci­ences. Au­toim­mune dis­ease, I think, is one that we’re go­ing to re­al­ly make some ma­jor head­way. So as a com­pa­ny, we be­long to a con­sor­tium that looks at the UK Biobank da­ta that’s com­ing out. It’s about half a mil­lion pa­tients. And to Tony’s point, there’s just an in­cred­i­bly rich amount of ge­net­ic da­ta from ex­ome se­quenc­ing in that biobank.

Rosana Kapeller: So au­toim­mune dis­eases, I think, it’s go­ing to be big.

John Car­roll:  Hugo?

Hugo Ceule­mans: All types of dis­eases where-

John Car­roll:  And add on a ques­tion. Where are we not go­ing to find progress? I’m kind of cu­ri­ous about that too.

Hugo Ceule­mans:  And that’s a re­al­ly tough one to an­swer. I think the nice thing about im­munol­o­gy, and the nice thing about me­tab­o­lism is that they come back in so many dis­eases, and that’s where AI is good at. You bring it in­to a new area, and it says, “Hey, wait a minute, I rec­og­nize this. I’ve seen this be­fore some­where else.” And I drag that in­for­ma­tion, and I give you sug­ges­tions on where to go. So that’s where it will be help­ful.

Now it’s very hard to pre­dict where it will do that. So it may be that in some rare dis­ease, you’ll rec­og­nize some­thing you have seen be­fore in one of the more tra­di­tion­al ar­eas that got a lot of ex­plo­ration. And there’s a lot of serendip­i­ty there. So pre­dict­ing up­front, “Here’s where I am go­ing to rec­og­nize those pat­terns,” is tough. But I think com­mon themes are the things like im­munol­o­gy, like me­tab­o­lism, that ac­tu­al­ly play in every dis­ease.

John Car­roll:  So I did want to turn to the au­di­ence here. Alex! How did I know you were go­ing to ask a ques­tion? Go. Go.

Alex Zha­voronkov:  Right. So there is a lot of hype about AI, and nowa­days it’s ac­tu­al­ly quite tough to dis­tin­guish who is who, be­cause there are so many ar­eas where it can be ap­plied. So I’ll ask a very con­crete ques­tion, very quan­ti­ta­tive. So my ques­tion is that cur­rent­ly in your com­pa­nies, how long does it take to go from tar­get pre­sen­ta­tion to lead in an an­i­mal, and how much does it cost in your opin­ion? So those two fig­ures, how long, how quick­ly, and did you al­ready see the im­prove­ment ex­per­i­men­tal­ly with AI?

Badhri Srini­vasan:  So it’s high­ly var­ied, so I’m try­ing to think how to an­swer your ques­tion very specif­i­cal­ly, and I don’t be­lieve I can an­swer your ques­tion very specif­i­cal­ly. Have we al­ready seen the im­prove­ment? I think not yet. I think we are in the space now where we’re start­ing to ex­plore. So the an­swer of hav­ing seen the im­prove­ment, no. We’re in the process, I would say.

Tony Wood: I guess to me that’s a sort of “how long is a piece of string” ques­tion. There are so many oth­er fac­tors that are im­por­tant in that process, which de­ter­mine its speed. What’s the rel­a­tive fo­cus? What’s your con­fi­dence to pick of your points and to use an­i­mal mod­els at the end of that se­quence? What’s your con­fi­dence in a re­sult in that an­i­mal mod­el be­ing mean­ing­ful?

There’s vol­umes writ­ten, for ex­am­ple, on the dif­fer­ence in qual­i­ty in an­i­mal mod­els. So it can be quick if you’re fo­cused on a great tar­get, and you have con­fi­dence in trans­la­tion, and you’re pick­ing an area where we al­ready know how to ex­e­cute very ef­fec­tive­ly. So for me, I think this is a ve­hi­cle to help us make de­ci­sions. It is not a panacea that will fix oth­er prob­lems that AI/ML sim­ply can­not.

John Car­roll:  In most cas­es, isn’t AI be­ing ap­plied in ad­di­tion to every­thing else you’re do­ing? It’s re­al­ly an aug­men­ta­tion as op­posed to a re­place­ment of any­thing that may be go­ing on, it seems to me. Any­one else want to weigh in here?

Tony Wood: Most­ly, the ap­proach­es, at least in the ear­ly R&D phas­es, is a hy­poth­e­sis gen­er­a­tor and aug­ment­ing some­thing else you’re al­ready do­ing.

But let me come back to the point about imag­ing, be­cause that is a very clear area. We can­not do it any oth­er way. So there are these unique pieces where it is the on­ly method that can be ap­plied.

Speak­er 2: Much has been writ­ten about the emer­gence of Chi­na as an AI su­per­pow­er, both be­cause of heavy in­vest­ment by the Chi­nese gov­ern­ment at all lev­els, and per­haps a low­er stan­dard of pri­va­cy than we have here in the Unit­ed States. From your per­spec­tives as glob­al com­pa­nies, do you see a no­table rise in Chi­na in terms of their strength in AI, rel­a­tive to the Unit­ed States? And is the US gov­ern­ment do­ing enough to both in­vest in AI, and are the pri­va­cy stan­dards in the Unit­ed States an en­cum­brance, in your opin­ions?

John Car­roll:  Is there any­one you’d like to di­rect that to in par­tic­u­lar? Okay. It’s up for grabs. Who wants to grab it? Hugo? It’s yours, Hugo.

Hugo Ceule­mans:  Oh, it’s mine. So first of all, I’m based in Eu­rope, so mak­ing state­ments about ei­ther the US or Chi­na, I may not be best po­si­tioned. Now one thing, pa­tients are so cen­ter to our at­ten­tion, we can­not make progress while at the same time los­ing fo­cus on pa­tients. So just waiv­ing pa­tients’ rights, pa­tients’ con­cerns, that’s who we do it for. So we should not low­er the bar on that one. Do we do enough in ar­ti­fi­cial in­tel­li­gence in the West, to just in­clude a bit of Eu­rope?

So prob­a­bly we can do more. Prob­a­bly we can do bet­ter at giv­ing the right in­cen­tives. I do think we should al­so be­come stronger in work­ing to­geth­er. I think a lot of the ini­tia­tives in the West have been iso­lat­ed, frag­ment­ed. I think that com­ing to­geth­er in a biotope will give us a crit­i­cal mass that will be need­ed in such a com­pe­ti­tion. But by no means I think we should jeop­ar­dize pa­tients’ rights and pri­va­cy rights to ac­com­plish that. I do not think that that is the an­swer.

Speak­er 3: Hugo, just to fol­low up on that ques­tion. Where­as we have a lot of star­tups that are in­ter­est­ed in AI … Alex, we’ve ac­tu­al­ly start­ed an al­liance with some of you as well, and I think that goes to my ques­tion. How are we go­ing to com­bine, col­lab­o­rate, part­ner? Be­cause a lot of peo­ple are say­ing, “Hey, the da­ta, the or­ga­ni­za­tion, the type of da­ta that you put in, is the out­come that you’re go­ing to have at the end, and how we speed up this R&D.”

So how are we go­ing to de­moc­ra­tize and speed up the R&D process by com­bin­ing nim­ble star­tups that are dis­rupt­ing, and have the right data­bas­es, the right codes, and have asked the right ques­tions? Be­cause all of us have an al­liance, be­cause each of us are ask­ing dif­fer­ent type of ques­tions. So how are we go­ing to de­moc­ra­tize, and how are we go­ing to group to­geth­er and col­lab­o­rate? So that’s my ques­tion to maybe each of you.

Tony Wood: I guess from my per­spec­tive, it goes back to, it’s about the da­ta. And for me, where we need to be fo­cus­ing are the tech­nolo­gies that are go­ing to en­able the gen­er­a­tion of ap­pro­pri­ate and rel­e­vant da­ta against the back­drop of some la­bel­ing, which gives you a grand state of truth. So I’m wor­ried less about the method­ol­o­gy de­vel­op­ment. I think what we should be much more fo­cused on are some of the prob­lems in da­ta col­lec­tion that you’ve heard my fel­low pan­el mem­bers talk about, am­bu­la­to­ry mol­e­c­u­lar bio­mark­er mea­sure­ments, things like that. I’ll stop there. It’s about da­ta de­vel­op­ment, da­ta ac­qui­si­tion tech­nol­o­gy, not about the an­a­lyt­i­cal method.

Speak­er 4:  So no mic, but I’m go­ing to speak loud enough. Build­ing off Maria’s ques­tion, let’s go one step deep­er. Let’s talk about busi­ness mod­els. So 130 plus AI in “drug dis­cov­ery” com­pa­nies, 15 top phar­ma com­pa­nies. How do you ad­ju­di­cate, eval­u­ate, and then how do you think about busi­ness mod­els from a da­ta as­set mod­el eco­nom­ics?

Tony Wood: That’s a re­al­ly good ques­tion be­cause it’s very dif­fi­cult. (Laugh­ter.)

Speak­er 4:  Hugo asked that to me last week at the Lon­don con­fer­ence. That’s his ques­tion.

Tony Wood: Yeah. Let me just put a plea out there. We need some means of as­sess­ing da­ta on per­for­mance stan­dards, right? This all start­ed with the orig­i­nal im­age net­work, where quite frankly there was a glob­al stan­dard that every­one was aim­ing against. You didn’t have to … You could get away from this sort of cur­rent col­lab­o­rate, suck it in, oh, you’ll find out when you pay the cost of find­ing out whether or not our pre­dic­tions are right. That’s fun­da­men­tal­ly the prob­lem we face.

So how do we cre­ate an en­vi­ron­ment, be it in the con­text of this se­cure da­ta phi­los­o­phy that you de­scribed, Hugo, or oth­ers, where we just sim­ply have a set of Im­a­geNet-Like stan­dards that one can cre­ate syn­thet­ic datasets against, what have you, but an im­par­tial means of judg­ing where are the best ex­am­ples? Where are there in­vest­ments that are re­al­ly de­liv­er­ing mas­sive im­prove­ments? And where are the just many ver­sions of the same fla­vor? We just don’t have that right now, and it makes it very hard.

John Car­roll: Okay.

Alex: So I just want­ed to add to that. There are a few Im­a­geNet-like projects, for ex­am­ple, gen­er­a­tive chem­istry. So there are two cur­rent­ly. So peo­ple cre­at­ed large da­ta sets, opened them up, la­beled them, cre­at­ed mod­els, and cre­at­ed a leader­board. But what is in­ter­est­ing is that most of the phar­ma com­pa­nies, when they’re part­ner­ing, they’re not look­ing at those re­sults. They’re typ­i­cal­ly … It’s kind of … I think it’s a lit­tle bit more of a crony rep­u­ta­tion play at this point in time. So peo­ple do not re­al­ly look at the bench­marks.

Tony Wood: I guess I just re­spond by say­ing it’s nec­es­sary but not suf­fi­cient in our analy­sis.

Matt Clark: Great. Hi, Matt Clark from El­se­vi­er. One as­pect of AI that we haven’t dis­cussed much is how it changes mak­ing de­ci­sions. Right? We have all the AIs, we have all the read­outs, but we ac­tu­al­ly have to make de­ci­sions that we wouldn’t have oth­er­wise made based on this in­for­ma­tion to change the core. So for ex­am­ple, as part of the drug suc­cess rates at dif­fer­ent clin­i­cal tri­al phas­es, when I was in big phar­ma, I read hun­dreds of project re­ports, and I nev­er saw a drug team kill its own project.

They’d usu­al­ly go on a cou­ple years be­fore some­one out­side said, “Hey, we’ve got to stop this. You can’t save it any­more.” So I would say, what is the as­pect of think­ing in your or­ga­ni­za­tions for, now that you have this da­ta com­ing in from AI, us­ing it to change how you make de­ci­sions, and make dif­fer­ent de­ci­sions than you made in the past? How are you chang­ing the or­ga­ni­za­tion to ac­count for that, as well as just the raw sci­ence of I have a new piece of in­for­ma­tion com­ing in?

John Car­roll: Saurabh?

Saurabh Sa­ha:  Yeah, so I think your ques­tion doesn’t nec­es­sar­i­ly have to be scoped just to AI. I think just reg­u­lar sta­tis­tics, a p val­ue should be enough to kill a project. I think you just have to have the mind­set to be able to pull the trig­ger and say, “This is done.” This is tra­di­tion­al­ly very dif­fi­cult for com­pa­nies to do. One thing that we’ve adopt­ed is a no­tion of truth seek­ing over pro­gres­sion seek­ing. So hav­ing this no­tion that when a tar­get is iden­ti­fied, all the way through when the mol­e­cule may al­ready be in the clin­ic, you nev­er stopped val­i­dat­ing that tar­get as­set pair. You’re con­stant­ly val­i­dat­ing it.

Whether you’re us­ing AI, whether you’re us­ing reg­u­lar sta­tis­tics, what­ev­er the means is, ex­ter­nal da­ta, com­peti­tor da­ta, aca­d­e­m­ic da­ta, what­ev­er it is, you’re con­stant­ly eval­u­at­ing whether the tar­get as­set pair that you’re pur­su­ing at year five is still as com­pelling and ex­cit­ing as it was five years ago. And if you can mea­sure up to that stan­dard, then I think we’ll have high­er suc­cess rates.

John Car­roll:  Okay. Well, I said we’d get out here sharp, about 8:30, so it’s 8:31. I know you guys have a busy day. I want to thank every­body on our pan­el here. We’re go­ing to hear a lot more about AI. I’d like to par­tic­u­lar­ly thank PPD Biotech for spon­sor­ing to­day’s con­ver­sa­tion, and hope to see you all lat­er on. Thanks for com­ing.

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