[0.0][S01] So, I mean, you literally say, make me a drug[3.08] [3.08][S01]for X disease, off it goes, says, here's the molecule[6.71] [6.71][S01]you need.[7.29] [7.29][S02] Yeah, yeah.[7.95] [7.95][S01] Do you think that's possible?[8.71] [8.71][S02] It's possible.[9.78] [9.78][S02]I think, it's possible.[10.44] [10.44][S02]I think everything is pointing in that direction.[12.055] [12.055][S03] Well, I've seen first hand is[13.847] [13.847][S03]what can come out of this explosion of two fields coming[16.21] [16.21][S03]together.[16.71] [16.71][S03]I think when you've got experts in two fields,[18.633] [18.633][S03]and they come together, and they're really curious, deeply[21.05] [21.05][S03]curious about the other field, and they[22.34] [22.34][S03]want to apply their thing to your field, that's when[24.59] [24.59][S03]you get this kind of magic.[25.715] [25.715][S02] In five-years' time,[27.99] [27.99][S02]doing drug design without AI will[30.32] [30.32][S02]be like doing any sort of science without maths.[32.93] [32.93][S02]And to be honest, I think the whole of science[34.85] [34.85][S02]will be like, this.[36.05] [36.05][S02]It's like if you're not using AI, what are you doing?[38.38] [41.845][S01] Welcome back to "Google DeepMind," the Podcast.[44.82] [44.82][S01]My name is Professor Hannah Fry.[46.8] [46.8][S01]Now, you'll already know that Demis Hassabis and John[49.94] [49.94][S01]Jumper won the Nobel Prize in 2024[53.0] [53.0][S01]for their work applying artificial intelligence[55.4] [55.4][S01]to protein folding.[56.76] [56.76][S01]Now, everything comes down to proteins[58.88] [58.88][S01]in the human body-- how they fold, how they function.[61.99] [61.99][S01]But not very long ago, working out[64.11] [64.11][S01]the structure for one single protein[66.72] [66.72][S01]could take months, or even years.[69.25] [69.25][S01]And then, with the release of AlphaFold2, the algorithms[72.93] [72.93][S01]developed at Google DeepMind, the entire field[76.26] [76.26][S01]has been completely revolutionized.[79.15] [79.15][S01]More recently, AlphaFold3 can predict the structure[82.44] [82.44][S01]of all of life's molecules with unprecedented accuracy,[86.56] [86.56][S01]which turns out to be absolutely pivotal for drug design.[91.36] [91.36][S01]These developments have paved the way[93.18] [93.18][S01]for a new company spun out of Google DeepMind.[96.07] [96.07][S01]It's called, Isomorphic Labs, to represent the synergy[98.76] [98.76][S01]between biology and AI.[100.66] [100.66][S01]And joining me today are two of its most notable hires.[104.35] [104.35][S01]Rebecca Paul is Head of Medicinal Drug Design[107.02] [107.02][S01]with years of experience in the process of discovering[109.56] [109.56][S01]new drugs.[110.47] [110.47][S01]And Max Jaderberg is its Chief AI Officer.[113.59] [113.59][S01]Anyone who's been following this podcast will remember Max from[116.73] [116.73][S01]his earlier days at DeepMind, teaching agents to play "Capture[119.83] [119.83][S01]the Flag" and "StarCraft."[121.4] [121.4][S01]And today, he thinks that agents will[123.49] [123.49][S01]be instrumental in the future of drug discovery.[126.73] [126.73][S01]Max, Rebecca, thank you so much for joining me.[128.697] [128.697][S03] Thank you for having us.[130.28] [130.28][S02] Yes, a pleasure to be here.[131.39] [131.39][S01] Well, it's a delight to have you because there's[133.06] [133.06][S01]some big stuff happening.[134.87] [134.87][S01]I know a lot has been made of this claim[137.89] [137.89][S01]that AI is going to be able to solve all diseases.[141.23] [141.23][S01]Is that realistic, Max?[144.703] [144.703][S02] This isn't going to happen overnight.[146.87] [146.87][S02]Let's be clear.[147.67] [147.67][S02]But I think the exciting thing is that we can actually[149.92] [149.92][S02]see there's, perhaps, a practical path[152.74] [152.74][S02]towards that point.[154.405] [154.405][S02]And it's very different at this point in time[156.28] [156.28][S02]than it has ever been, because we've[157.78] [157.78][S02]got these AI machine-learning models that[160.6] [160.6][S02]understand the biological world and the biochemical world[163.21] [163.21][S02]in a completely different manner than what we've had before.[166.85] [166.85][S02]And so that's opening up tons of disease space[170.2] [170.2][S02]that we didn't think was tractable before.[173.17] [173.17][S02]And that's just today.[174.56] [174.56][S02]So as we start to develop these models further and further,[177.53] [177.53][S02]it's really just the very beginning.[179.05] [179.05][S01] Does that mean--[180.49] [180.49][S01]I mean, every disease is on the table here, Becky.[183.455] [183.455][S03] So I would say nothing is off the table[186.43] [186.43][S03]at this point.[187.19] [187.19][S03]And the journey for me here has been a big one.[190.22] [190.22][S03]I used to be much more conservative in this space.[192.71] [192.71][S03]But having come to Isomorphic Labs, seeing the kind of models[195.82] [195.82][S03]that we have, things that I thought in the past[198.175] [198.175][S03]we would never be able to predict[199.55] [199.55][S03]and now being able to do it every day within five,[201.633] [201.633][S03]ten seconds, it's completely shifted my mindset.[204.47] [204.47][S03]So now, I would put nothing off the table.[206.22] [206.22][S01] And is it just in drug design,[208.12] [208.12][S01]or is AI going to affect clinical trials, as well?[211.48] [211.48][S02] I think over time, yes, it will definitely[214.51] [214.51][S02]affect clinical trials.[216.29] [216.29][S02]We're focusing really heavily on the drug-design phase[219.16] [219.16][S02]at the moment.[220.99] [220.99][S02]But you can imagine a world where,[223.04] [223.04][S02]as you start to get better and better at drug design, actually,[225.73] [225.73][S02]more and more of the bottleneck comes[227.98] [227.98][S02]onto the clinical-development side of things.[230.54] [230.54][S02]And so we really need to rethink how we do that.[232.713] [232.713][S02]I don't think we've really changed[234.13] [234.13][S02]the way we do clinical development[235.547] [235.547][S02]for a long, long time.[237.42] [237.42][S02]It's still very slow.[239.74] [239.74][S02]There's, of course, a lot of regulation for good reason.[242.32] [242.32][S02]But as we understand more and more about how[245.04] [245.04][S02]these molecules work, the true mechanisms of disease,[249.7] [249.7][S02]how these molecules also interact[251.362] [251.362][S02]with the rest of the body and affect everything[253.32] [253.32][S02]like toxicity inside of us, we can[255.39] [255.39][S02]start to rethink even how we do those clinical-trial designs,[258.25] [258.25][S02]how we first go into people and start[259.86] [259.86][S02]measuring the efficacy of these molecules.[261.64] [261.64][S02]So that's not right now for us at Isomorphic,[264.13] [264.13][S02]but it's very much in our future.[265.505] [265.505][S01] What is the big idea then?[267.28] [267.28][S01]What's the big ambition of Isomorphic?[269.22] [269.22][S02] It really is stepping[270.72] [270.72][S02]towards that solvable-disease space.[273.13] [273.13][S02]And so this is really-- before you go into clinical trial,[276.567] [276.567][S02]before you even start testing out on people,[278.4] [278.4][S02]you need something to test.[279.9] [279.9][S02]You need to create a molecule and a drug.[282.0] [282.0][S02]And a drug, what is that?[284.38] [284.38][S02]It's something that goes in and modulates[286.5] [286.5][S02]some function of the body, some function in a cell.[289.63] [289.63][S02]And so, really, the key first phase of Isomorphic Labs[294.21] [294.21][S02]is, how can we create this AI drug-design engine that[298.07] [298.07][S02]can take pretty much any disease, any protein[301.43] [301.43][S02]target that's implicated in that disease,[303.93] [303.93][S02]and work out how to create a molecule that will go in[307.1] [307.1][S02]and start modulating the function of these proteins,[310.05] [310.05][S02]the function of cells, and then change the disease[312.2] [312.2][S02]state for positive patients.[314.25] [314.25][S01] The thing is, I mean,[315.96] [315.96][S01]diseases have been cured in the past.[318.38] [318.38][S01]We have come up with drug solutions that, effectively,[320.87] [320.87][S01]make them a remnant of history.[323.88] [323.88][S01]Why are some diseases so much more difficult[326.21] [326.21][S01]than others to solve or cure?[327.925] [327.925][S03] So, for example, some types of cancers,[331.53] [331.53][S03]you can develop a treatment to cure them[333.32] [333.32][S03]because, for example, those cancers might be quite stable.[335.94] [335.94][S03]So there's maybe one mutation that drives that cancer.[338.37] [338.37][S03]And then you treat that. you treat with that molecule that[341.69] [341.69][S03]hits that particular mutation, and that can[343.91] [343.91][S03]be curative in that disease.[345.27] [345.27][S03]But if you had a cancer which was continually evolving[347.63] [347.63][S03]and the cells were evolving to overcome the drug that you're[352.01] [352.01][S03]treating with, then you need to be continually evolving the drug[354.728] [354.728][S03]that you're treating them with.[356.02] [356.02][S03]So that would be then a much more difficult disease to solve.[358.562] [358.562][S01] And, I guess, are there[360.02] [360.02][S01]others where there's just a real gap in our understanding[362.523] [362.523][S01]of what's going on in the body.[363.815] [363.815][S03] Yeah, there's also that massive gap[365.94] [365.94][S03]in understanding, what's actually driving this disease?[368.95] [368.95][S03]Is it multiple things together?[370.57] [370.57][S03]We don't have all the answers yet to biology.[372.61] [372.61][S03]Biology is so, so complex.[375.69] [375.69][S03]One of the fundamental things we need to do[377.88] [377.88][S03]is find out what biology is actually[380.82] [380.82][S03]driving disease before we can then develop a small molecule,[383.64] [383.64][S03]or some kind of chemistry that then will modulate[386.13] [386.13][S03]the right biology to actually make[388.17] [388.17][S03]an improvement in the symptoms, or a disease[390.51] [390.51][S03]modification to that disease.[391.73] [391.73][S01] Is that what you're trying to do then?[393.813] [393.813][S01]So actually, something like cancer,[395.68] [395.68][S01]for instance, once you get down to the level of molecules[399.99] [399.99][S01]and proteins, it's that something's gone wrong[403.05] [403.05][S01]at that level, which then escalates[406.05] [406.05][S01]into the scale of the human body.[408.228] [408.228][S03] Absolutely.[409.27] [409.27][S03]So you've got little-- you can think[410.77] [410.77][S03]of proteins as mini factories or engines inside your cell.[415.54] [415.54][S03]And those, they have a function, they do something.[418.22] [418.22][S03]So if you have a mutation, or something that[420.91] [420.91][S03]changes about that protein, that means,[423.56] [423.56][S03]for example, it's always switched on,[425.12] [425.12][S03]where, in a normal cell, it might be going on and off,[427.37] [427.37][S03]or maybe it's mostly off.[428.622] [428.622][S03]But now in a cancer cell, something's happened,[430.58] [430.58][S03]it's always on.[431.69] [431.69][S03]It's going to just continually drive a signal.[434.06] [434.06][S03]And that signal could be grow, proliferate.[436.142] [436.142][S03]And that will then drive the formation of a tumor.[438.225] [438.225][S01] And so your target then,[440.073] [440.073][S01]you know that there's something going on with that protein[442.49] [442.49][S01]so you're targeting then that protein with a molecule[445.09] [445.09][S01]of some medicine or drug.[446.132] [446.132][S03] Exactly.[447.048] [447.048][S01] OK.[447.71] [447.71][S03] So you want to-- if you[449.252] [449.252][S03]think of that little factory working away,[451.88] [451.88][S03]you want to design a perfect-shaped wrench[454.81] [454.81][S03]that you can throw into that little set of gears[457.03] [457.03][S03]to stop it working so that then it's going[459.078] [459.078][S03]to stop driving that signal.[460.245] [460.245][S01] Does it come down to shape?[461.475] [461.475][S03] Very important.[462.683] [462.683][S03]You can think of proteins as having pockets, or grooves,[465.73] [465.73][S03]or crevices, and you really need to design[467.8] [467.8][S03]a molecule that perfectly fits inside that groove.[470.32] [470.32][S01] To block it.[471.32] [471.32][S03] To block it.[471.82] [471.82][S01] Oh, really?[472.46] [472.46][S03] It's literally like that, yes.[474.293] [474.293][S01] And is that true of all drugs[476.463] [476.463][S01]that already exist, I mean, like paracetamol, for example.[478.88] [478.88][S01]Is that what it's doing?[480.03] [480.03][S03] Yeah.[480.822] [480.822][S03]So paracetamol is a small molecule, binds to its protein[483.73] [483.73][S03]targets, blocks the function, and in the case of paracetamol,[488.53] [488.53][S03]or painkillers, it's preventing you from experiencing pain.[491.45] [491.45][S03]So, yes.[492.615] [492.615][S01] So in some ways then,[493.99] [493.99][S01]you're playing LEGO at the molecular level.[497.475] [497.475][S03] Exactly, yes, and we love it.[500.115] [500.115][S01] Why would that be a good situation for AI?[503.39] [503.39][S01]What makes this a well-suited problem for AI, Max?[506.325] [506.325][S02] It's actually such a perfect application of AI[509.29] [509.29][S02]and machine-learning, this whole playing LEGO with molecules.[513.07] [513.07][S02]We've seen over the last five, six, seven years the rise[516.789] [516.789][S02]of models like AlphaFold.[518.27] [518.27][S02]We had AlphaFold1, AlphaFold2, understanding[520.69] [520.69][S02]the structure of proteins.[522.01] [522.01][S02]Before AlphaFold2, no one could really understand the structure[525.82] [525.82][S02]without going into a lab and experimentally resolving[529.51] [529.51][S02]these structures.[530.27] [530.27][S02]And that can take months, it can take years.[532.18] [532.18][S02]Sometimes, it's not even possible for some proteins.[534.37] [534.37][S02]Obviously, AlphaFold2-- Nobel Prize winning breakthrough[537.24] [537.24][S02]in chemistry.[538.03] [538.03][S02]And now, we've taken that even further with things[540.113] [540.113][S02]like AlphaFold3, where now we can[542.31] [542.31][S02]understand the structure of proteins with small molecules.[545.59] [545.59][S02]And these small molecules are the little LEGO blocks[547.77] [547.77][S02]that come in, and we use those drugs[549.51] [549.51][S02]to inhibit the function of a protein,[551.79] [551.79][S02]or change the function of a protein.[553.48] [553.48][S02]And the reason why this is such a good domain[556.402] [556.402][S02]for machine-learning is that what we're trying to do,[558.61] [558.61][S02]in essence, is predict the 3D coordinates of this biomolecular[563.97] [563.97][S02]system.[564.82] [564.82][S02]And this fits really, really nicely[566.91] [566.91][S02]into some of our classic, supervised learning, modeling[571.14] [571.14][S02]domains.[571.99] [571.99][S02]It fits really, really nicely into our diffusion modeling[576.81] [576.81][S02]frameworks that have been so, so successful[580.14] [580.14][S02]for things like image generation, or video generation.[583.025] [583.025][S01] And in terms of being suited[584.76] [584.76][S01]for the supervised learning stuff that had already[586.843] [586.843][S01]gone before, is that because there is some metric of success[589.71] [589.71][S01]here, some way of being right, inverted commas.[592.842] [592.842][S02] There's a very clear metric of success[595.05] [595.05][S02]here, which is super helpful for developing these models[597.9] [597.9][S02]and for research.[598.9] [598.9][S02]And the reason is, over the last 50 years,[602.23] [602.23][S02]people have been experimentally resolving these protein[605.49] [605.49][S02]structures-- structure of these proteins with small molecules,[608.26] [608.26][S02]with DNA, with RNA.[609.67] [609.67][S02]They've been doing it by hand in a lab,[611.4] [611.4][S02]and then depositing the results into a big database[614.25] [614.25][S02]called The Protein Data Bank, PDB.[616.18] [616.18][S02]And this gives a really rich source of information.[620.53] [620.53][S02]It's a couple of 100,000 3D structures.[623.29] [623.29][S02]And each structure has thousands of atom coordinates,[626.95] [626.95][S02]so there's a really high information density.[629.23] [629.23][S02]And that's a perfect scenario for supervised learning.[631.75] [631.75][S02]Now, this isn't web-scale data, so it's not the scale of data[635.04] [635.04][S02]that we might be used to for training large-language models.[638.2] [638.2][S02]But incredibly, we've worked out ways to design these[641.01] [641.01][S02]neural-network architectures and these training regimes so that[644.52] [644.52][S02]we can only train on a couple of 100,000 structures[648.42] [648.42][S02]and excitingly get what we call generalization.[651.27] [651.27][S02]We see that these models can generalize.[653.56] [653.56][S02]They can be applied to completely new proteins,[655.792] [655.792][S02]completely new molecules that people have never[657.75] [657.75][S02]seen before in history.[658.96] [658.96][S02]And, of course, that's essential if you're doing drug design.[661.92] [661.92][S02]Drug design is about creating completely new molecules[664.17] [664.17][S02]that we've never seen before in nature, even,[666.12] [666.12][S02]to actually modulate these functions.[667.662] [667.662][S01] But then, also, I guess, the possibilities[671.07] [671.07][S01]of molecules that you could design--[672.79] [672.79][S01]I mean, it's-- well, a very big number, I imagine.[675.725] [675.725][S02] Yeah, that number is huge.[678.51] [678.51][S02]People throw around numbers like 10 to the power of 60[682.71] [682.71][S02]is the possible number of drug-like molecules[685.837] [685.837][S02]out there in the universe.[686.92] [686.92][S02]So it's a huge combinatorial problem.[689.23] [689.23][S02]And that's also a really exciting spot for AI.[693.49] [693.49][S02]We can create great predictive models[696.24] [696.24][S02]of how these molecules fit together, even how strongly they[700.56] [700.56][S02]fit, or the properties of them.[702.51] [702.51][S02]But with the design space of 10 to the power of 60,[705.72] [705.72][S02]we're reaching the level of atoms in the universe.[709.88] [709.88][S02]So even if you had the perfect predictive models[713.59] [713.59][S02]of how this fits together, you wouldn't[715.81] [715.81][S02]be able to exhaustively search that massive space.[718.64] [718.64][S02]So what do you do?[719.39] [719.39][S02]OK, maybe you subsample that space and you search[722.74] [722.74][S02]through some large number--[724.9] [724.9][S02]a million, 10 million, a billion, 10 billion.[727.16] [727.16][S01] It's nothing, is it?[728.08] [728.08][S02] Even though you're not-- yeah.[728.89] [728.89][S01] Yeah.[729.19] [729.19][S02] You're not even scratching the surface.[731.44] [731.44][S02]And that's where we can then fall to new types of models,[734.9] [734.9][S02]things like generative models, search methods, agents,[738.77] [738.77][S02]which instead of exhaustively searching[741.19] [741.19][S02]the full molecular space, we can really smartly start[745.63] [745.63][S02]to explore across that whole space[747.047] [747.047][S02]but without exhaustively searching the whole space.[749.172] [749.172][S01] OK, tell me about AlphaFold3 then.[751.31] [751.31][S01]In terms of designing drugs, what does it actually[754.45] [754.45][S01]allow you to do?[755.45] [755.45][S03] So as a medicinal chemist,[757.34] [757.34][S03]we always want to be able to visualize how our molecule binds[762.25] [762.25][S03]to our protein, so how our LEGO block fits into the bigger[765.707] [765.707][S03]picture of LEGO blocks.[766.665] [766.665][S01] Thank you for going with me on this analogy,[768.05] [768.05][S01]I appreciate it.[768.717] [768.717][S03] The LEGO analogy.[770.14] [770.14][S03]The reason we need to have that visualization[772.015] [772.015][S03]is because when you're optimizing[773.65] [773.65][S03]a small molecule binding to a protein,[775.76] [775.76][S03]you need to know which vector to explore.[778.37] [778.37][S03]You need to have some kind of target in mind.[780.26] [780.26][S03]I'm going to explore that part of the pocket,[782.135] [782.135][S03]or I'm going to that part of the protein structure.[785.17] [785.17][S03]That looks like a good place to go.[788.17] [788.17][S03]And so for years, we've invested,[789.97] [789.97][S03]as a scientific community, in ways to do that.[792.53] [792.53][S03]So X-ray crystallography is an experimental technique[795.55] [795.55][S03]where you can actually go into a lab,[797.44] [797.44][S03]and you can spend a lot of time crystallizing your protein.[801.26] [801.26][S03]You fire X-rays at it once it's bound to your small molecule.[805.0] [805.0][S03]And then you can actually visualize, atom by atom,[807.85] [807.85][S03]how your small molecule is binding to your protein.[810.23] [810.23][S03]Now we can do that with AlphaFold3, and the latest[813.85] [813.85][S03]iterations of that model, into seconds.[816.56] [816.56][S03]And so something that might have taken me[819.7] [819.7][S03]months when I was doing my PhD or in my early-stage research,[823.55] [823.55][S03]I'm now just seeing on my screen all the time.[825.78] [825.78][S03]And so you can just iterate and iterate in silico[827.932] [827.932][S03]until you get to something that actually looks really quite[830.39] [830.39][S03]promising.[831.15] [831.15][S03]And then you take that into the lab.[832.65] [832.65][S01] So which way around does it work then?[834.27] [834.27][S01]Are you saying, OK, I think something like this, this, this,[836.84] [836.84][S01]and this would work.[837.96] [837.96][S01]Let's try it out and see if it fits.[840.05] [840.05][S01]Or is it the other way around?[843.282] [843.282][S01]Are these models telling you this[845.09] [845.09][S01]is something that might fit?[846.33] [846.33][S03] So in the way that we've[847.913] [847.913][S03]constructed our drug-design platform at ISO,[849.78] [849.78][S03]you can do both.[850.95] [850.95][S03]So I can come in as an experienced medicinal chemist[853.31] [853.31][S03]and I can say, I think this.[854.87] [854.87][S03]And I can test it then and there, a couple of minutes,[857.12] [857.12][S03]and get that feedback.[858.93] [858.93][S03]But you can also take the opposite approach.[861.613] [861.613][S03]I don't actually know what's going[863.03] [863.03][S03]to work here so I'm going to apply the generative models we[865.34] [865.34][S03]have.[865.95] [865.95][S03]I'm going to do some, what we call, virtual screening.[868.68] [868.68][S03]So I'm going to take an area of chemical space[871.49] [871.49][S03]that's commercially available.[872.88] [872.88][S03]I'm going to screen that against my protein.[874.78] [874.78][S03]And I'm going to get the models to tell me[876.53] [876.53][S03]what's best from that subset.[878.425] [878.425][S01] Can I see what it looks like when you're actually[881.15] [881.15][S01]designing something?[882.11] [882.11][S03] So the small molecule[883.76] [883.76][S03]is fitting into this little groove in the protein,[887.28] [887.28][S03]and it's forming interactions with the protein.[891.22] [891.22][S03]So the dotted lines you can see, those[893.01] [893.01][S03]are interactions between that small molecule and the protein[895.77] [895.77][S03]itself.[896.62] [896.62][S03]And as medicinal chemists, we want[898.23] [898.23][S03]to optimize, or increase, the number of those interactions,[900.9] [900.9][S03]because that's increasing the strength[902.49] [902.49][S03]of that relationship between the small molecule and the protein.[905.66] [905.66][S01] Let me describe what I've got, what's going on here.[908.38] [908.38][S01]So you've got-- so the curly stuff is a protein.[911.097] [911.097][S03] Yes, that's the protein, yeah.[912.93] [912.93][S01] And it's folded.[914.463] [914.463][S01]So you've got the three-dimensional structure.[916.38] [916.38][S03] Indeed.[917.17] [917.17][S01] And then over here, you've got--[918.75] [918.75][S01]I mean, this looks like the kind of thing[919.98] [919.98][S01]you would do in GCSE chemistry, one of those kind of diagrams.[922.633] [922.633][S03] Exactly.[923.55] [923.55][S03]This is a small molecule.[924.592] [924.592][S01] This is sort of plugged into the protein?[927.408] [927.408][S03] Yeah.[928.2] [928.2][S01] Wow.[928.66] [928.66][S01]I mean, it really is like 3D jigsaws then.[930.51] [930.51][S01]I mean, that's--[930.72] [930.72][S03] 3D, yes, exactly.[931.74] [931.74][S01] --what you're doing.[932.47] [932.47][S03] That's what we're doing, yeah.[933.24] [933.24][S01] That crevice could be[934.47] [934.47][S01]the thing that's making you feel pain,[936.053] [936.053][S01]or the thing that's causing tumor growth,[938.37] [938.37][S01]or whatever it might be.[939.55] [939.55][S03] Yeah, causing your disease, yeah.[940.83] [940.83][S01] Amazing.[941.663] [941.663][S01]So then you're trying different versions of this molecule[944.7] [944.7][S01]to see if you can get the best possible fit[946.5] [946.5][S01]in that little crevice.[947.255] [947.255][S03] Exactly.[948.172] [948.172][S03]And I can quickly show you.[949.333] [949.333][S03]So we have lots of different functionality on the platform[951.75] [951.75][S03]that you can try.[952.83] [952.83][S03]And this is my favorite, which Max always tells me off[955.08] [955.08][S03]about because I can use my expertise[956.52] [956.52][S03]as a medicinal chemist.[957.478] [957.478][S03]And I can say, OK, I want to make some specific changes[960.45] [960.45][S03]to this molecule.[961.205] [961.205][S01] Oh, wow.[962.08] [962.08][S03] I can actually view what I'm doing in 3D.[964.51] [964.51][S03]So this is now going to fetch that structure prediction.[966.898] [966.898][S03]And I can actually make modifications to this molecule.[969.19] [969.19][S03]And I can see the predicted structure in real-time.[971.25] [971.25][S01] And normally, this would have taken--[973.292] [973.292][S01]I mean, before AI--[974.85] [974.85][S01]a long time.[975.935] [975.935][S03] I mean, if you're going to go into a lab[977.4] [977.4][S03]and experimentally determine this,[978.82] [978.82][S03]it could be anywhere from weeks to years.[980.96] [980.96][S01] Why do you tell her off for this one?[983.482] [983.482][S02] I guess you're referring to the fact[985.11] [985.11][S02]that, over time, we want to do more and more from the model[987.54] [987.54][S02]itself.[988.06] [988.06][S02]The really exciting frontier, from my perspective,[990.143] [990.143][S02]is there's going to be lots of scenarios where Becky will want[993.99] [993.99][S02]to go in and make those changes by hand[995.91] [995.91][S02]and test out very specific hypotheses that she has[999.78] [999.78][S02]on why this molecule works and how we can make it better.[1003.54] [1003.54][S02]And then what we also should be doing[1006.3] [1006.3][S02]is asking our generative models, and our agents to say,[1010.84] [1010.84][S02]hey, this is how I'm thinking about the problem.[1013.36] [1013.36][S02]These are my design constraints.[1014.86] [1014.86][S02]I want a molecule that does X, Y, Z and has these properties,[1017.67] [1017.67][S02]and looks like this, and maybe interacts over there,[1020.25] [1020.25][S02]and makes this sort of shape.[1021.52] [1021.52][S02]What can you come up with?[1022.81] [1022.81][S02]And maybe set this running, go away, have a coffee,[1027.31] [1027.31][S02]go home, come in the next morning[1029.76] [1029.76][S02]and see what the agent has come up with.[1031.91] [1031.91][S01] I guess with all of the projects[1033.78] [1033.78][S01]that you've applied AI to generally in DeepMind,[1036.73] [1036.73][S01]it has gone through that process of starting off[1039.69] [1039.69][S01]with human expertise, and then slowly building[1042.599] [1042.599][S01]in more knowledge and expertise within the model itself.[1045.08] [1045.08][S02] Yeah.[1045.913] [1045.913][S02]And I think there are a lot of analogies to that moment[1048.877] [1048.877][S02]that we had with large-language models[1050.46] [1050.46][S02]where we've had large-language models for a long time,[1053.36] [1053.36][S02]and I've been working on them 10 years ago.[1055.96] [1055.96][S02]But they were rubbish.[1057.99] [1057.99][S02]And they were spitting stuff out that looked like language.[1060.74] [1060.74][S02]It kind of made sense.[1061.91] [1061.91][S02]But it also didn't make sense and you had to correct,[1063.65] [1063.65][S02]and it clearly wasn't human.[1064.817] [1064.817][S02]And then, they got steadily better little by little.[1068.69] [1068.69][S02]And suddenly, they just passed through this human-perceptible[1072.61] [1072.61][S02]threshold where you can't really tell whether this[1075.25] [1075.25][S02]is generated by a human or not.[1077.6] [1077.6][S02]And we're getting to the same point[1081.25] [1081.25][S02]with our molecule-design models where,[1085.81] [1085.81][S02]maybe five years ago in this field,[1088.04] [1088.04][S02]you had generative models of molecules,[1090.19] [1090.19][S02]and they'd spit stuff out.[1091.31] [1091.31][S02]But you'd give them to a chemist like Becky,[1094.1] [1094.1][S02]and she would probably tear her hair out, like this is rubbish.[1097.373] [1097.373][S01] Well, did you see those kinds of models?[1099.54] [1099.54][S03] Yes, I did, yeah, because I actually[1101.623] [1101.623][S03]worked at an AI company prior to joining Isomorphic Lab,[1104.45] [1104.45][S03]so I've seen that progression in that journey.[1106.47] [1106.47][S01] And tell me, what kind of stuff did they spit out?[1109.685] [1109.685][S03] For a long time, it[1111.06] [1111.06][S03]would just be nonsense because you're obviously giving[1114.58] [1114.58][S03]the model an uphill function.[1115.85] [1115.85][S03]You want it to get to something that's going to bind really[1119.33] [1119.33][S03]potently, for example.[1120.815] [1120.815][S03]But to do that, maybe it just makes the molecule massive.[1123.19] [1123.19][S01] Oh.[1123.9] [1123.9][S03] But then, well, it's[1125.317] [1125.317][S03]not going to actually be absorbed through the intestine[1127.61] [1127.61][S03]into the bloodstream because--[1128.63] [1128.63][S01] Because you've got more[1129.23] [1129.23][S01]to think about than just the molecule itself.[1130.75] [1130.75][S03] Exactly, yeah.[1131.24] [1131.24][S03]So there's a lot to piece together here.[1132.97] [1132.97][S01] Oh, that's interesting.[1134.46] [1134.46][S01]So AI, if we go back to our LEGO analogy,[1136.443] [1136.443][S01]it was just like building a massive LEGO[1138.11] [1138.11][S01]wall all around the protein.[1139.255] [1139.255][S03] Yes, yeah.[1140.255] [1140.255][S01] I see.[1141.52] [1141.52][S01]OK.[1142.02] [1142.02][S03] Essentially.[1142.47] [1142.47][S01] And now, this is--[1144.29] [1144.29][S01]have you seen that moment, that tip over with the language[1147.787] [1147.787][S01]models that Max describes?[1148.87] [1148.87][S03] Yeah, I think--[1150.078] [1150.078][S03]I've been so surprised by the quality[1152.6] [1152.6][S03]of some of the molecules that come out of the generative AI.[1155.1] [1155.1][S03]And sometimes the molecules that come out,[1156.947] [1156.947][S03]you think, oh, I would have-- why wouldn't I[1158.78] [1158.78][S03]have come up with that?[1159.738] [1159.738][S03]That's really amazing.[1161.735] [1161.735][S03]And, of course, you don't have to go and make[1163.61] [1163.61][S03]that exact molecule, but you could then[1165.29] [1165.29][S03]use that as inspiration to do something else.[1167.52] [1167.52][S03]So--[1168.048] [1168.048][S01] So it's working with you.[1169.59] [1169.59][S03] Yeah, you can work together.[1170.76] [1170.76][S03]I mean, I'm guessing at some point in the future,[1172.53] [1172.53][S03]it will be so good that you'll be like,[1174.155] [1174.155][S03]oh, there's nothing I would change.[1176.36] [1176.36][S02] We've had some really fun moments where,[1179.75] [1179.75][S02]for example, we've had our models submitting molecules[1182.24] [1182.24][S02]blind, and then other people looking at them[1184.37] [1184.37][S02]and seeing, OK, what are we going to send off for testing?[1186.185] [1186.185][S01] Oh, really?[1187.05] [1187.05][S02] And people look--[1187.34] [1187.34][S01] Like a chewing test for molecules.[1188.36] [1188.36][S02] Exactly.[1188.69] [1188.69][S02]And people looking at these molecules,[1190.273] [1190.273][S02]very, very experienced medicinal chemists, saying, wow,[1192.755] [1192.755][S02]there's a lot of experience behind the design[1194.63] [1194.63][S02]of this molecule, and actually not knowing[1197.36] [1197.36][S02]that this was designed by a generative model instead.[1200.35] [1200.35][S01] But OK, let me understand this, though,[1203.15] [1203.15][S01]because using the analogy of large-language models,[1206.13] [1206.13][S01]it makes sense there that you have these tokens,[1209.09] [1209.09][S01]you break words down into little bytes,[1213.05] [1213.05][S01]and then you can build up from there.[1215.13] [1215.13][S01]How do you do it in a way that makes sense chemically?[1218.31] [1218.31][S01]I mean, you're not just taking atoms, are you?[1220.55] [1220.55][S02] We are, actually, just taking atoms.[1223.25] [1223.25][S02]For bigger things like proteins, we chunk up into amino acids,[1226.68] [1226.68][S02]so one token per amino acids.[1228.96] [1228.96][S02]So instead of characters of a sentence, letters of a sentence,[1232.23] [1232.23][S02]we have amino acids of a protein.[1234.0] [1234.0][S02]And then for the small molecule, we chunk it up just[1236.6] [1236.6][S02]into its individual atoms.[1238.022] [1238.022][S02]And so we have a sequence of amino acids,[1239.73] [1239.73][S02]and a sequence of atoms, and we put them together[1242.02] [1242.02][S02]and that's one big sequence.[1243.28] [1243.28][S02]And then we feed it through a structured model[1245.25] [1245.25][S02]like AlphaFold3.[1246.76] [1246.76][S02]And AlphaFold3 uses transformers.[1251.55] [1251.55][S02]But unlike in large-language models[1253.77] [1253.77][S02]where transformers are used on one-dimensional sequences[1256.5] [1256.5][S02]of characters, of letters, here, we[1258.63] [1258.63][S02]use what we call a pair former, which[1261.3] [1261.3][S02]operates on a two-dimensional interaction grid of all[1265.5] [1265.5][S02]of these molecular elements.[1267.4] [1267.4][S02]So we can consider every single possible interaction that[1270.78] [1270.78][S02]could occur between every amino asset, every part[1273.51] [1273.51][S02]of the protein, and every atom of the small molecule,[1276.63] [1276.63][S02]and everything in between.[1277.9] [1277.9][S02]And then this creates neural-network features, which[1281.28] [1281.28][S02]condition a diffusion model.[1283.75] [1283.75][S02]And diffusion models are generative models.[1287.86] [1287.86][S02]We probably know them from these amazing image-generative models,[1290.65] [1290.65][S02]video-generative models.[1291.7] [1291.7][S02]And instead of generating the pixels of an image, instead,[1295.17] [1295.17][S02]our diffusion models are generating the 3D-atom[1297.72] [1297.72][S02]coordinates of this whole biomolecular system.[1299.975] [1299.975][S01] And it just so happens they work.[1301.85] [1301.85][S02] And it just so happens[1303.392] [1303.392][S02]this works phenomenally well.[1304.77] [1304.77][S02]You get these amazing structure predictions that-- when[1307.56] [1307.56][S02]you go to the lab and experimentally resolve[1309.45] [1309.45][S02]these structures-- and we do this on occasion.[1313.082] [1313.082][S02]Something amazing is predicted, like a completely new pocket[1315.69] [1315.69][S02]or a new mechanism of action.[1317.098] [1317.098][S02]We go into the lab.[1317.89] [1317.89][S02]We want to check that, are these models[1320.4] [1320.4][S02]grounded at all in reality?[1322.09] [1322.09][S02]And what comes back is, yeah, this[1324.18] [1324.18][S02]is within 1 angstrom, the tiniest unit of distance[1329.61] [1329.61][S02]accurate, which is phenomenal.[1330.965] [1330.965][S01] Yeah.[1332.19] [1332.19][S01]But then through that training process,[1334.39] [1334.39][S01]does it manage to extract a conceptual understanding of how[1339.12] [1339.12][S01]chemistry works?[1339.965] [1339.965][S02] It's really hard to think about concepts[1342.57] [1342.57][S02]in this atom space.[1343.9] [1343.9][S02]But I do believe that there's some notion of reasoning[1348.06] [1348.06][S02]in molecular and atomistic space that these models are doing[1352.08] [1352.08][S02]because of the amount of generalization[1354.0] [1354.0][S02]we're getting out of them.[1355.22] [1355.22][S02]And if you think about what these generative models are[1357.76] [1357.76][S02]trained to do, they're trained to fit to the data distribution[1361.778] [1361.778][S02]that you give them.[1362.57] [1362.57][S02]And so in our case, we give them all the molecules[1366.565] [1366.565][S02]that might exist naturally, that people have worked out before,[1369.19] [1369.19][S02]that people have designed before.[1370.52] [1370.52][S02]When your model gets better and better,[1372.11] [1372.11][S02]you get things that look like they could have been designed[1374.568] [1374.568][S02]before, which, then it starts to be[1377.02] [1377.02][S02]imperceptible from a human design.[1378.475] [1378.475][S01] What do you think?[1379.725] [1379.725][S03] You can certainly see that in the molecules[1381.67] [1381.67][S03]that we get back.[1382.46] [1382.46][S03]They look like molecules that myself or someone else[1385.287] [1385.287][S03]might have designed.[1386.12] [1386.12][S03]You do sometimes get something crazy, though.[1387.83] [1387.83][S01] Do you?[1388.7] [1388.7][S03] Yeah, we still do, but we have[1390.918] [1390.918][S03]ways of filtering that out now.[1392.21] [1392.21][S01] Is it like a hallucination, in a way?[1394.252] [1394.252][S03] Yeah.[1395.043] [1395.043][S03]The model is really confident, but it's confidently wrong.[1397.48] [1397.48][S01] Confidently wrong.[1399.19] [1399.19][S01]OK.[1399.91] [1399.91][S01]What does it look like when it hallucinates?[1402.53] [1402.53][S03] So we get back a number[1405.64] [1405.64][S03]of structures that the models believe[1407.2] [1407.2][S03]are good solutions for this problem,[1409.39] [1409.39][S03]for this particular protein pocket.[1411.41] [1411.41][S03]And then our job is to go, OK, well, which of those molecules[1415.07] [1415.07][S03]should we actually select to put into synthesis?[1417.69] [1417.69][S03]And so this is never a model on its own in a silo.[1420.57] [1420.57][S03]This is a model working really closely with an expert[1422.84] [1422.84][S03]to say, OK, of the solutions you've given me,[1425.76] [1425.76][S03]where's the gold in that?[1427.4] [1427.4][S03]Where are the molecules that are actually going[1430.04] [1430.04][S03]to push this project forward?[1431.687] [1431.687][S03]And we'll put them into what we call chemical synthesis, where[1434.27] [1434.27][S03]we make them and we test them.[1435.81] [1435.81][S03]But sometimes we get back results[1437.64] [1437.64][S03]which, actually, the compound doesn't[1440.48] [1440.48][S03]bind to the protein at all.[1441.99] [1441.99][S03]So here, the models, essentially,[1443.45] [1443.45][S03]completely hallucinated a solution[1445.62] [1445.62][S03]and so convincingly that actually, an expert looks at it[1448.547] [1448.547][S03]and goes, yeah, that looks a really good solution.[1450.63] [1450.63][S03]The confidence metrics would suggest the same.[1453.29] [1453.29][S03]So essentially, it is a hallucination.[1455.37] [1455.37][S03]And I think we find it a really fascinating research question[1458.9] [1458.9][S03]to say, OK, how do we find the really good stuff[1462.083] [1462.083][S03]that the model is giving us?[1463.25] [1463.25][S01] Beyond this 3D jigsaw or LEGOs--[1465.66] [1465.66][S01]we're mixing our metaphors.[1468.56] [1468.56][S01]Is it just about structure?[1470.173] [1470.173][S01]Is it just about finding something[1471.59] [1471.59][S01]that will plug a particular hole?[1473.43] [1473.43][S01]Or are there other considerations[1474.805] [1474.805][S01]that you have to have, as well, when it comes to drug design?[1477.347] [1477.347][S03] There are so many other considerations.[1479.73] [1479.73][S03]That's what makes this problem just incredibly complex.[1482.34] [1482.34][S03]So it's even beyond the shape.[1484.133] [1484.133][S03]You can have something that maybe fits in the shape,[1486.3] [1486.3][S03]but it's got to bind really strongly to that protein.[1489.66] [1489.66][S03]And that prediction of binding affinity, as we call it,[1492.78] [1492.78][S03]is actually different.[1493.71] [1493.71][S03]You can't really gauge that from just looking at a picture.[1496.168] [1496.168][S03]The picture is a helpful guide, but you[1497.87] [1497.87][S03]need to be able to predict that binding affinity separately.[1501.41] [1501.41][S03]And then all of those things together,[1503.04] [1503.04][S03]that's just how your molecule binds to your protein.[1505.53] [1505.53][S03]You've also got to think about, is that molecule going to bind[1508.113] [1508.113][S03]to any of the other 20,000 proteins in the body?[1510.42] [1510.42][S03]Because if the answer is yes, that could drive a side effect[1513.14] [1513.14][S03]that you don't want.[1514.17] [1514.17][S03]That's going to drive you some toxicity.[1516.35] [1516.35][S03]Is this molecule going to be stable?[1518.1] [1518.1][S03]It's got to survive the really acidic conditions[1520.1] [1520.1][S03]of the stomach.[1521.01] [1521.01][S03]It's got to survive going through the liver, which[1523.16] [1523.16][S03]is going through a war zone.[1525.05] [1525.05][S03]The liver wants to do everything it can.[1526.867] [1526.867][S03]It's like, this is a foreign molecule,[1528.45] [1528.45][S03]I need to get rid of it.[1529.515] [1529.515][S03]So your molecule has got to be really robust.[1531.39] [1531.39][S03]It's got to survive that journey.[1532.97] [1532.97][S03]It's got to be soluble.[1534.41] [1534.41][S03]When you take a pill, that pills got to dissolve in your stomach,[1537.25] [1537.25][S03]and it's got to stay dissolved all the way[1539.02] [1539.02][S03]through your intestine because, otherwise, it's not[1541.145] [1541.145][S03]going to absorb into your body.[1542.93] [1542.93][S03]And a lot of these parameters are pulling against each other.[1546.32] [1546.32][S03]So for something to be soluble and dissolve really well,[1549.67] [1549.67][S03]it needs to be water loving.[1552.02] [1552.02][S03]But for it to bind to the protein[1553.87] [1553.87][S03]and to gain affinity in that kind of pocket, that LEGO[1556.63] [1556.63][S03]connection, it actually needs to be water hating.[1559.618] [1559.618][S01] Well, how do you possibly[1561.16] [1561.16][S01]solve that if you need opposing characteristics?[1565.25] [1565.25][S03] So we've-- up until this point and still now,[1568.15] [1568.15][S03]to some degree, drug discovery is a very iterative process.[1571.22] [1571.22][S03]So the human brain can only think about so many things[1574.6] [1574.6][S03]at one time.[1575.24] [1575.24][S03]So you're like, OK, I'm going to solve[1577.06] [1577.06][S03]this binding-affinity problem a bit,[1578.89] [1578.89][S03]and then, I'm going to start to think, OK,[1580.64] [1580.64][S03]is my molecule soluble?[1581.768] [1581.768][S03]And then I'm going to start to bring in gradually[1583.81] [1583.81][S03]these other properties.[1584.78] [1584.78][S03]And honestly, it's like Whack-a-mole.[1586.46] [1586.46][S03]You play like this three-year game of Whack-a-mole[1588.55] [1588.55][S03]where you're like, I fixed this problem.[1590.18] [1590.18][S03]Hooray.[1590.68] [1590.68][S03]And then this other one pops up and you're[1592.502] [1592.502][S03]like, OK, I'll fix that, but then the other one's gone bad,[1594.96] [1594.96][S03]again.[1595.69] [1595.69][S03]So to be able to predict all these things in silico[1597.96] [1597.96][S03]is still a really, really hard problem.[1599.66] [1599.66][S01] Are you working on tools that will[1601.59] [1601.59][S01]help with those elements, too?[1602.87] [1602.87][S02] Yeah, absolutely.[1604.203] [1604.203][S02]So we think about, ISO, how do we do drug design[1608.34] [1608.34][S02]end-to-end, which really means solving all of these very, very[1611.31] [1611.31][S02]hard problems with cell permeability, solubility,[1615.31] [1615.31][S02]toxicity, liver clearance, everything.[1618.84] [1618.84][S02]And none of this is solved.[1620.98] [1620.98][S02]These are really hard problems to even model.[1622.96] [1622.96][S02]So we spend a lot of effort, a lot of research,[1625.47] [1625.47][S02]on creating new models to really understand this better.[1628.99] [1628.99][S02]And then as Becky was talking about,[1631.0] [1631.0][S02]how do we then start to find these needles in a haystack[1634.6] [1634.6][S02]molecules that are somehow just balancing the properties[1640.23] [1640.23][S02]just right to be a perfect drug?[1642.43] [1642.43][S02]And it's really, really hard.[1644.14] [1644.14][S02]And actually, there are a lot of analogies to maybe what I used[1648.33] [1648.33][S02]to do at DeepMind in, for example, "Capture the Flag"[1651.17] [1651.17][S02]or "StarCraft" is there's not just one agent,[1656.36] [1656.36][S02]or strategy that solves "StarCraf"[1659.3] [1659.3][S02]or a particular game like "Go."[1661.17] [1661.17][S02]You have to completely start mixing up these strategies,[1664.01] [1664.01][S02]and working out exploits for each individual strategy,[1667.26] [1667.26][S02]and, basically, searching this huge combinatorial strategy[1670.57] [1670.57][S02]space.[1671.07] [1671.07][S02]In the same way, we need to be searching[1672.95] [1672.95][S02]this huge combinatorial-molecule space.[1675.36] [1675.36][S02]So just like you might have tree search in a game of "Go,"[1678.29] [1678.29][S02]where at every move, you elucidate some other possible[1682.42] [1682.42][S02]moves, and you start searching through that tree of possible[1684.92] [1684.92][S02]strategies going deeper and deeper.[1686.85] [1686.85][S02]And just like you can do that for moves in a game of "Go,"[1689.88] [1689.88][S02]you can imagine doing a similar thing for designing a molecule.[1692.847] [1692.847][S02]So you start with a part of a molecule[1694.43] [1694.43][S02]and you start to hypothesize, what[1696.618] [1696.618][S02]are the different things I could add or take away[1698.66] [1698.66][S02]from this molecule?[1699.75] [1699.75][S02]And you get to a whole tree of possible futures[1702.29] [1702.29][S02]that you can then score and work out[1703.91] [1703.91][S02]a value associated with that to create[1705.5] [1705.5][S02]that perfect molecule for this very specific indication.[1708.13] [1708.13][S01] But how do you even know that the perfect molecule[1710.713] [1710.713][S01]exists?[1711.31] [1711.31][S01]Maybe there's just some crevices in the proteins[1713.84] [1713.84][S01]that just are unfittable.[1715.06] [1715.06][S03] We do have this concept of undruggable proteins[1719.15] [1719.15][S03]where the crevice is really flat,[1721.16] [1721.16][S03]and you can't really get anything to grip in there.[1723.89] [1723.89][S03]And those proteins might need different solutions.[1726.33] [1726.33][S03]So actually, we have a whole emerging field[1728.39] [1728.39][S03]which we call molecular glues.[1729.96] [1729.96][S03]And this is where you have two proteins that come together,[1734.34] [1734.34][S03]and the pocket that's formed when they come together[1737.03] [1737.03][S03]is actually a much more suitable pocket.[1738.88] [1738.88][S03]So now, you need to design a molecule that[1740.63] [1740.63][S03]sits in the middle of them and glues them together.[1744.0] [1744.0][S03]So there's this whole explosion of all[1745.61] [1745.61][S03]these different modalities now, which[1747.152] [1747.152][S03]makes this an incredibly exciting field to work in.[1749.802] [1749.802][S02] From my perspective,[1751.26] [1751.26][S02]the fact that we've actually found any drugs at all already,[1754.98] [1754.98][S02]given how hard and complex the problem is--[1757.16] [1757.16][S02]and we've basically been doing a bit of human intuition[1760.7] [1760.7][S02]and a lot of random screening and experimental testing.[1763.8] [1763.8][S02]And we've managed to find molecules,[1765.44] [1765.44][S02]even though the design space is huge.[1767.087] [1767.087][S02]Actually, that gives me a lot of hope,[1768.67] [1768.67][S02]because that means that there's probably a lot of redundancy[1771.54] [1771.54][S02]in chemical space, i.e., there's probably[1774.03] [1774.03][S02]lots of different solutions that could work,[1776.62] [1776.62][S02]but we've just got to find them.[1778.18] [1778.18][S01] Have you actually tried[1778.8] [1778.8][S01]to make any of these molecule, or at the moment,[1780.865] [1780.865][S01]do they just exist on the screen?[1782.24] [1782.24][S03] Oh, no, we make a lot of molecules.[1784.282] [1784.282][S03]We've got a huge experimental footprint.[1787.27] [1787.27][S03]Yeah.[1787.77] [1787.77][S01] And how-- do they turn out how you expect?[1790.33] [1790.33][S01]I mean, what are the results like?[1791.78] [1791.78][S03] We've had some incredible success[1794.91] [1794.91][S03]in some of our projects where I'll find Max at his desk[1799.53] [1799.53][S03]and I'll be like, have you seen this thing?[1802.59] [1802.59][S03]And we'll just both be really mind-blown about it.[1804.872] [1804.872][S01] That when you get the molecule, it actually works.[1807.455] [1807.455][S02] Yeah, exactly, exactly.[1809.41] [1809.41][S02]And we have our own drug-design programs,[1811.66] [1811.66][S02]so things that we've started from scratch ourselves.[1814.06] [1814.06][S02]We also work with pharma-company partners, people[1817.41] [1817.41][S02]like Eli Lilly and Novartis.[1819.01] [1819.01][S02]In these collaborations, you'll get specific targets to work on.[1822.21] [1822.21][S02]And these are ones that these companies have high conviction[1825.4] [1825.4][S02]behind and probably a bunch of evidence behind.[1828.67] [1828.67][S02]Some of the collaborations we're in,[1832.15] [1832.15][S02]we've been given very, very hard targets.[1834.47] [1834.47][S02]These are things that people have worked on[1836.53] [1836.53][S02]sometimes for over a decade and not made significant progress,[1841.88] [1841.88][S02]to the point where you've got something on the market.[1844.13] [1844.13][S01] Things like cancer and that sort of stuff.[1845.87] [1845.87][S02] Whole host of therapeutic areas and disease[1848.74] [1848.74][S02]areas.[1849.29] [1849.29][S02]And then Becky and team sit down,[1853.15] [1853.15][S02]start designing with these models,[1855.49] [1855.49][S02]and can start finding completely novel chemical matter[1859.24] [1859.24][S02]for completely novel mechanisms that no one's really discovered[1863.2] [1863.2][S02]before, which is mind-blowing for me--[1865.03] [1865.03][S01] Yeah.[1865.15] [1865.15][S02] --as a computer scientist.[1865.845] [1865.845][S03] Yeah, it's mind-blowing for me.[1868.6] [1868.6][S03]There's been some almost career-defining moments,[1872.6] [1872.6][S03]where the AI will give you a hypothesis.[1877.39] [1877.39][S03]It will suggest something.[1878.8] [1878.8][S03]And you think, I'm not convinced I would do that,[1882.71] [1882.71][S03]but the model's telling me this thing.[1884.358] [1884.358][S03]And it's really quite convinced about this thing,[1886.4] [1886.4][S03]so I should maybe just test this hypothesis.[1888.77] [1888.77][S03]And then, actually, it turns out that the model was right,[1892.192] [1892.192][S03]and you were absolutely right to test it,[1893.9] [1893.9][S03]and it's really pushed forward your project, or even[1896.65] [1896.65][S03]that field.[1897.89] [1897.89][S03]So I think, for me, it's not about how we trust the models,[1902.39] [1902.39][S03]it's about how we are open to testing the hypotheses that they[1905.41] [1905.41][S03]put in front of us and not going,[1908.02] [1908.02][S03]oh, that doesn't fit with my worldview,[1909.77] [1909.77][S03]so I'm not going to test it.[1910.73] [1910.73][S01] But then you are also human, right?[1912.465] [1912.465][S03] Yes.[1912.76] [1912.76][S01] So I do wonder whether[1914.177] [1914.177][S01]if you see lots of hits with the model, as it were.[1917.272] [1917.272][S01]If the model is coming up with lots of good stuff in a row,[1919.73] [1919.73][S01]do you start to maybe trusting it more than yourself?[1922.635] [1922.635][S03] We actually put a lot of trust in the models.[1925.3] [1925.3][S03]We actually use-- for example, some of the models we have,[1927.717] [1927.717][S03]we use them as quite strict cutoffs.[1929.317] [1929.317][S01] What kind of cutoff?[1930.65] [1930.65][S03] For example, we have[1931.39] [1931.39][S03]a model which we call binding probability,[1933.14] [1933.14][S03]and it goes from 0 to 1.[1934.64] [1934.64][S03]So 1 is the model is convinced your molecule is definitely[1938.47] [1938.47][S03]going to bind to your protein.[1939.97] [1939.97][S03]Zero, the model is telling you, this is definitely not[1942.55] [1942.55][S03]going to bind.[1943.19] [1943.19][S03]And when you can build a little bit of confidence[1945.232] [1945.232][S03]over time that the model really does understand, OK,[1947.95] [1947.95][S03]anything below 0.7, it's really got a very low probability[1951.37] [1951.37][S03]of success.[1952.22] [1952.22][S03]So we just define that as a cutoff[1953.797] [1953.797][S03]and be like, we're not going to put anything[1955.63] [1955.63][S03]in the lab that's got a probability of less than this[1958.01] [1958.01][S03]because, actually, the model is quite likely to be right.[1960.68] [1960.68][S03]It's probably not going to be any good.[1962.0] [1962.0][S03]And so you do-- even though-- and that's[1963.667] [1963.667][S03]quite hard because as a chemist, you design something,[1965.92] [1965.92][S03]and you think, that was a really clever idea[1967.753] [1967.753][S03]that I just came up with, and why doesn't it like it?[1971.162] [1971.162][S01] But then what if it makes something[1973.12] [1973.12][S01]that you don't understand, I mean,[1975.08] [1975.08][S01]or that doesn't make sense?[1977.21] [1977.21][S01]I mean, does it need to explain itself?[1979.486] [1979.486][S03] I think at the moment that explainability[1982.09] [1982.09][S03]is quite important for now, because the process is quite[1986.8] [1986.8][S03]driven still by the human.[1988.31] [1988.31][S03]It's not end-to-end yet.[1989.51] [1989.51][S03]We have to go in there, and we have to say, what comes next?[1992.99] [1992.99][S03]So if there's no explainability there,[1995.03] [1995.03][S03]you don't know what would be next, right?[1997.37] [1997.37][S03]And so that would be very difficult to work with.[2000.16] [2000.16][S03]I can imagine in a future state where, actually, the process[2005.53] [2005.53][S03]is a bit more end-to-end, like in one step, the model.[2008.217] [2008.217][S03]Here's a drug.[2008.8] [2008.8][S01] Here's a drug.[2009.26] [2009.26][S03] Yeah.[2009.55] [2009.55][S03]Then actually, maybe you don't need that explainability.[2011.84] [2011.84][S03]But when you've got to go in there as a human[2013.18] [2013.18][S03]and you've got to iterate and you've[2014.68] [2014.68][S03]got to do a bit more of that directionality,[2017.547] [2017.547][S03]then that explainability is important.[2019.13] [2019.13][S03]And that's where I think, for me, the AlphaFold models really[2021.672] [2021.672][S03]come in because, OK, the model is[2023.615] [2023.615][S03]predicting this molecule is going to be good.[2025.49] [2025.49][S03]I can rationalize that with what I'm actually seeing.[2027.68] [2027.68][S03]I know what I would do next.[2028.68] [2028.68][S01] You have a slightly different view[2030.597] [2030.597][S01]on explainability, don't you?[2031.81] [2031.81][S03] I do have a slightly different view[2033.852] [2033.852][S03]on explainability, but I think you need explainability when[2036.43] [2036.43][S03]your model sucks, basically.[2038.3] [2038.3][S03]And we don't have perfect models yet,[2041.18] [2041.18][S03]so I think there's a good amount of room for explainability.[2044.09] [2044.09][S03]But I always hear the call for explainability[2047.05] [2047.05][S03]and think, look, we need to make this model better.[2049.517] [2049.517][S03]And actually, the interesting thing about explainability[2051.85] [2051.85][S03]is it can help you understand the pathologies that this model[2055.57] [2055.57][S03]has, the biases that it has.[2057.8] [2057.8][S03]Where's that wrong, given the science that we know about?[2062.19] [2062.19][S03]And so we can start patching that[2063.65] [2063.65][S03]and make it better and better and get to this point where,[2066.139] [2066.139][S03]yeah, actually, we can just do end-to-end design purely[2068.96] [2068.96][S03]in silico, and maybe do a final round of verification[2072.83] [2072.83][S03]in the lab at the end.[2074.1] [2074.1][S01] So, I mean, you literally say,[2075.929] [2075.929][S01]make me a drug for X disease, off it goes, says here's[2080.06] [2080.06][S01]the molecule you need.[2081.09] [2081.09][S02] Yeah, yeah.[2081.84] [2081.84][S01] Do you think that's possible?[2082.66] [2082.66][S02] I think it's possible.[2084.06] [2084.06][S02]I think it's possible.[2084.75] [2084.75][S02]I think everything is pointing in that direction.[2086.792] [2086.792][S02]We're getting better and better.[2088.19] [2088.19][S02]We're already reducing the amount of experimental cycles[2091.58] [2091.58][S02]you need, reducing the amount of lab time you need.[2095.48] [2095.48][S02]And, yeah, this is just the beginning.[2097.225] [2097.225][S01] Absolutely extraordinary.[2098.767] [2098.767][S01]I mean, I suppose it does depend on knowing what protein you're[2102.2] [2102.2][S01]targeting, too, right?[2103.28] [2103.28][S03] Yes.[2104.03] [2104.03][S01] So still-- and the diseases[2105.948] [2105.948][S01]that we don't have a full understanding are still[2107.99] [2107.99][S01]going to be difficult.[2109.35] [2109.35][S03] Yeah.[2110.142] [2110.142][S03]And that kind of-- we call it target ID space,[2112.31] [2112.31][S03]where you actually need to identify the protein that's[2114.89] [2114.89][S03]causing your disease.[2115.98] [2115.98][S03]It's actually a really important part of drug discovery[2118.515] [2118.515][S03]because if you're not hitting the right biological target[2120.89] [2120.89][S03]from the start, you can design the best molecule in the world.[2124.138] [2124.138][S03]It's not going to do what you want it to do[2125.93] [2125.93][S03]when you put it into a human.[2127.5] [2127.5][S03]So there's a lot to be done in that target ID space.[2129.768] [2129.768][S03]And I think AI's got a big role to play there, as well.[2132.06] [2132.06][S02] It's one of the big frontiers of AI[2134.45] [2134.45][S02]for biology is really understanding,[2137.33] [2137.33][S02]what are those driving mechanisms of disease?[2139.59] [2139.59][S02]Can we start to understand how mutations in our DNA[2144.89] [2144.89][S02]translate into changes of expression of RNA,[2147.83] [2147.83][S02]and how that changes the type of proteins and expression[2150.26] [2150.26][S02]levels of proteins, how those proteins interact[2153.392] [2153.392][S02]with each other and build up into these signaling pathways,[2155.85] [2155.85][S02]and how changes in those signaling pathways[2158.42] [2158.42][S02]change the disease state, as well?[2161.48] [2161.48][S02]And, of course, if we can start to understand these bits,[2164.15] [2164.15][S02]we can start to work out, where do[2165.8] [2165.8][S02]we need to modulate this biological system?[2167.91] [2167.91][S02]But all of this is really, really hard.[2170.097] [2170.097][S02]And there's some amazing breakthroughs[2171.68] [2171.68][S02]happening in the field understanding DNA[2173.66] [2173.66][S02]better, understanding this translation better.[2175.97] [2175.97][S02]Even through understanding how proteins interact,[2178.35] [2178.35][S02]can we build up these interaction networks better?[2182.55] [2182.55][S02]This is some of the really exciting frontier research[2184.77] [2184.77][S02]that we're also doing at ISO.[2185.96] [2185.96][S01] So you have a team working in that space, as well.[2188.26] [2188.26][S02] Yeah, that's right.[2189.07] [2189.07][S02]We have a whole computational biology team,[2191.13] [2191.13][S02]whole machine-learning modeling team[2192.63] [2192.63][S02]focused in this space, yeah.[2194.1] [2194.1][S01] But then what about personalized medicine?[2196.35] [2196.35][S01]Because I guess each person is different in some ways.[2198.83] [2198.83][S02] I mean, this is the really exciting,[2201.81] [2201.81][S02]potential future where we can understand much more[2205.59] [2205.59][S02]about, for example, cancer individuals' mutations[2209.01] [2209.01][S02]in their tumor, and through generative AI[2214.44] [2214.44][S02]and design agents, be able to come up[2217.38] [2217.38][S02]with molecules that work specifically[2219.75] [2219.75][S02]for these sort of mutations.[2220.925] [2220.925][S02]Now, there's a whole question of,[2222.3] [2222.3][S02]how do we actually operationalize that, and get[2224.258] [2224.258][S02]these drugs to patients, and approve this framework?[2227.11] [2227.11][S02]But we're moving towards a place where that technology[2230.61] [2230.61][S02]could be potentially there.[2232.075] [2232.075][S01] I mean, I'm thinking here about chemotherapy drugs,[2234.7] [2234.7][S01]which come with really devastating side effects.[2236.62] [2236.62][S01]You think there's real hope on the horizon[2237.88] [2237.88][S01]for that kind of thing?[2238.65] [2238.65][S03] Yeah, I mean, we think about chemotherapy drugs.[2241.34] [2241.34][S03]They're basically drugs that are nonspecific.[2243.56] [2243.56][S03]So they're going into the body, and they're[2246.25] [2246.25][S03]trying to halt that rapid cell proliferation.[2249.58] [2249.58][S03]But what we have now is an ability[2251.59] [2251.59][S03]to think about, actually, what's the specific target, the protein[2254.83] [2254.83][S03]target that we want to inhibit, we want to stop its function?[2259.45] [2259.45][S03]And that might have the same effect.[2261.35] [2261.35][S03]But you're not just generally using[2263.14] [2263.14][S03]something that's just very toxic to rapidly dividing cells.[2266.63] [2266.63][S03]Yes, it's going to stop your tumor cells dividing,[2268.86] [2268.86][S03]but it's also going to stop the cells that[2270.61] [2270.61][S03]line your stomach and your intestine,[2272.74] [2272.74][S03]going to make you feel nauseous and sick.[2274.61] [2274.61][S03]It's going to stop your hair follicles.[2276.35] [2276.35][S03]You're going to lose your hair, whereas, we now[2279.16] [2279.16][S03]know we can go in, we can target a very specific protein,[2282.74] [2282.74][S03]the one that's actually causing the disease.[2285.17] [2285.17][S03]And if you inhibit that particular protein,[2287.27] [2287.27][S03]that's not going to cause--[2288.67] [2288.67][S03]hopefully, if you get it right, it's[2290.41] [2290.41][S03]not going to cause all these other side effects.[2291.99] [2291.99][S02] You can do some also really, really cool stuff[2294.13] [2294.13][S02]with targeting particular cells.[2295.76] [2295.76][S02]So if you know that a particular cell type is expressing[2299.02] [2299.02][S02]something on its surface, you can start programming things[2303.88] [2303.88][S02]like antibodies to come in and find those particular receptors.[2308.23] [2308.23][S02]And so you're delivering your payloads directly[2312.07] [2312.07][S02]to that particular cell type and not more broadly to the body.[2314.942] [2314.942][S01] I just want to go back to the point[2316.9] [2316.9][S01]that you made earlier, Becky, about,[2318.478] [2318.478][S01]once you've got the drug design, then[2320.02] [2320.02][S01]once you put it into the human, there's[2322.03] [2322.03][S01]all of these other potential problems because, I mean,[2324.89] [2324.89][S01]there have been examples of this before where[2327.195] [2327.195][S01]drugs have been made and looked like they were very good.[2329.57] [2329.57][S01]And then once you actually put it into a human,[2331.537] [2331.537][S01]it causes some massive problems.[2332.87] [2332.87][S01]I think there was one which people were[2335.02] [2335.02][S01]very excited about the impact it was going to have on pain,[2338.2] [2338.2][S01]but it turned out that protein also[2340.0] [2340.0][S01]was quite crucial to making sure your heart kept beating.[2343.37] [2343.37][S01]How do you mitigate against that, or can you not at this[2345.82] [2345.82][S01]stage?[2346.155] [2346.155][S03] Well, one of the problems we have[2348.113] [2348.113][S03]is that we often use animal models to then translate things[2351.25] [2351.25][S03]into the clinic.[2352.19] [2352.19][S03]And animal models, they don't replicate human physiology[2355.97] [2355.97][S03]very well at all, actually.[2357.18] [2357.18][S03]So we know when we're working in the discovery[2359.54] [2359.54][S03]and preclinical space, which is all of that space[2361.73] [2361.73][S03]before you go into a human, we're[2364.01] [2364.01][S03]working with different animal models, which[2366.29] [2366.29][S03]might model the disease we're interested in.[2368.28] [2368.28][S03]And we're looking for molecules which[2370.1] [2370.1][S03]have an effect in those animal models.[2371.84] [2371.84][S03]And we have to show that they're not[2373.34] [2373.34][S03]toxic in those animal models.[2375.03] [2375.03][S03]And then we use that bank of evidence[2376.67] [2376.67][S03]to go to the drug-regulatory bodies and say, right,[2379.47] [2379.47][S03]we're ready to go into a human.[2380.85] [2380.85][S03]But from that point until the market,[2382.92] [2382.92][S03]there's a 90% failure rate.[2384.57] [2384.57][S01] Wow, 90%.[2385.94] [2385.94][S03] So all that investment[2387.44] [2387.44][S03]up to that point, which is huge.[2389.005] [2389.005][S01] What makes them so likely to fail?[2390.922] [2390.922][S03] So molecules fail in the clinic for toxicity.[2394.5] [2394.5][S03]They fail in the clinic for lack of efficacy.[2397.02] [2397.02][S03]And I think a lot of it comes back to the animal models[2400.07] [2400.07][S03]we use just are not very good at replicating human physiology.[2403.74] [2403.74][S01] Because the mouse is different to a human.[2405.99] [2405.99][S03] A mouse is different.[2407.448] [2407.448][S03]So we can cure a mouse disease, probably be quite good at that.[2410.575] [2410.575][S01] We've got loads of medication that works.[2412.885] [2412.885][S03] Yeah.[2413.51] [2413.51][S01] Yeah.[2413.8] [2413.8][S03] But, yeah, that translation[2415.79] [2415.79][S03]is a big part of science that we need to fix.[2418.42] [2418.42][S01] Can AI help here, as well?[2420.69] [2420.69][S01]I mean, if animal models are this stumbling[2423.74] [2423.74][S01]block with such a low level of success,[2426.183] [2426.183][S01]what can you do about it?[2427.225] [2427.225][S02] Well, this is where we can actually[2429.59] [2429.59][S02]use some of the technology and models we've been developing[2432.26] [2432.26][S02]and think about, OK, how can we understand toxicity better,[2436.13] [2436.13][S02]understand the effect on human cells[2438.77] [2438.77][S02]better, and see how that translates to organs?[2442.86] [2442.86][S02]If you think about some of these off-target effects,[2445.26] [2445.26][S02]probably, there are many drugs that you go into the clinic[2449.11] [2449.11][S02]and you're hitting your target of interest[2450.86] [2450.86][S02]and it's curing your pain, but then it's[2452.81] [2452.81][S02]hitting another target that's in another protein in your heart[2455.953] [2455.953][S02]and stopping the function of your heart.[2457.62] [2457.62][S02]That's an off-target effect.[2459.01] [2459.01][S01] I mean, side effects, in general,[2460.58] [2460.58][S01]are off-target effects, though, aren't they?[2461.76] [2461.76][S02] Yeah, exactly.[2462.982] [2462.982][S02]But you can imagine that if we've been building models that[2465.44] [2465.44][S02]understand really well how this molecule interacts with[2467.84] [2467.84][S02]your target of interest, you could also ask the question,[2470.85] [2470.85][S02]well, how does this molecule interact with every other target[2473.55] [2473.55][S02]in the human body, all 20,000 proteins?[2477.31] [2477.31][S02]And you can start building up this fingerprint of interactions[2480.955] [2480.955][S02]that this molecule, your drug molecule,[2482.58] [2482.58][S02]is having across the body.[2484.15] [2484.15][S02]And so that can give you clues, maybe even concrete signal,[2487.89] [2487.89][S02]into the toxicity or side effects of this molecule.[2491.05] [2491.05][S02]And the nice thing is we can get that signal, not when you're[2495.36] [2495.36][S02]going into humans, but actually, at the very, very beginning[2499.35] [2499.35][S02]of the design process.[2500.59] [2500.59][S02]So by the time you've gone through all of your molecule[2503.147] [2503.147][S02]design and you get to the point where you're like, yeah,[2505.48] [2505.48][S02]I want to go into humans, you've been[2507.03] [2507.03][S02]thinking about these side effects in a very rational way[2510.18] [2510.18][S02]for a long time.[2512.38] [2512.38][S02]And so, hopefully, your chances of actually hitting some[2515.31] [2515.31][S02]of those radically reduces.[2517.04] [2517.04][S01] You're taking your structure of LEGO bricks,[2519.572] [2519.572][S01]or your jigsaw, and you're just trying it[2521.28] [2521.28][S01]with every other possible combination[2522.99] [2522.99][S01]that it might encounter in a human body.[2524.465] [2524.465][S02] Yeah.[2525.01] [2525.01][S01] That's amazing.[2525.42] [2525.42][S02] We're going to make every possible LEGO[2526.83] [2526.83][S02]combination.[2527.33] [2527.33][S01] Well, if that's the design stage, then, Becky--[2529.87] [2529.87][S01]I mean, you also have to put this into clinical trials.[2532.18] [2532.18][S01]Just talk us through the process of clinical trials,[2534.403] [2534.403][S01]if you could.[2534.945] [2534.945][S03] So the first time that your molecule ever[2538.12] [2538.12][S03]goes into a human, that's a phase I clinical trial.[2541.4] [2541.4][S03]So it will be a small number of patients.[2543.95] [2543.95][S03]Some of those might actually be healthy volunteers.[2546.245] [2546.245][S03]They don't necessarily have the disease[2547.87] [2547.87][S03]that you're interested in.[2549.58] [2549.58][S03]And what you're looking to see is, does your drug actually[2552.887] [2552.887][S03]reach the level of exposure in the patient that would be[2555.22] [2555.22][S03]needed to generate an effect?[2557.21] [2557.21][S03]And is the drug well tolerated, or do you suddenly[2560.08] [2560.08][S03]start to see some side effects that you weren't anticipating?[2563.08] [2563.08][S03]If all is good, you'll proceed to a phase II clinical trial,[2566.39] [2566.39][S03]which is now you're going into people[2568.12] [2568.12][S03]who actually have the disease, and you're[2570.49] [2570.49][S03]going into larger numbers.[2571.88] [2571.88][S03]You're really looking to answer the question,[2574.04] [2574.04][S03]does your molecule actually have efficacy against the disease[2577.51] [2577.51][S03]that you're interested in?[2578.95] [2578.95][S03]And this is where we do see that big failure rate.[2581.33] [2581.33][S03]So 70% of molecules going into phase II[2583.96] [2583.96][S03]don't actually pass through into phase III.[2586.04] [2586.04][S03]For those that do pass into phase III,[2587.84] [2587.84][S03]that's where you're going into much bigger patient populations[2590.65] [2590.65][S03]seeing if your drug is effective across that bigger population.[2593.927] [2593.927][S03]It's not just got to be safe, but it's[2595.51] [2595.51][S03]got to be better than the standard of care.[2597.26] [2597.26][S03]There's got to be some--[2598.26] [2598.26][S03]for doctors to actually prescribe this[2600.34] [2600.34][S03]to their patients, they've got to say, this drug is better,[2603.1] [2603.1][S03]or this drug is safer than what I currently use.[2606.09] [2606.09][S01] And this whole thing has a 90% failure rate,[2609.29] [2609.29][S01]as you said.[2609.86] [2609.86][S03] Yeah.[2610.12] [2610.12][S01] I mean, does that mean[2611.86] [2611.86][S01]that there are people who work in this space[2613.93] [2613.93][S01]who never, never succeed?[2616.806] [2616.806][S03] Yeah.[2617.65] [2617.65][S03]So I'm a medicinal chemist, and we often[2621.16] [2621.16][S03]have this number where, actually, only 1[2624.1] [2624.1][S03]in 20 medicinal chemists will ever get a drug to market.[2627.74] [2627.74][S03]So 19 of us out of every 20 will never get a drug onto the market[2632.5] [2632.5][S03]through our careers.[2633.74] [2633.74][S03]So, yeah, we are a profession where[2636.28] [2636.28][S03]we're used to seeing significant failure.[2639.05] [2639.05][S01] You're comfortable with failure as a profession.[2640.25] [2640.25][S03] We're comfortable with failure.[2641.18] [2641.18][S03]We learn from it.[2642.08] [2642.08][S01] Extraordinary, extraordinary to imagine.[2644.89] [2644.89][S01]How long do you think it will be until the first AI-design drug[2649.03] [2649.03][S01]is on the market?[2650.0] [2650.0][S01]Because all of these additional levels really take some time,[2654.155] [2654.155][S01]don't they?[2654.655] [2654.655][S03] So those AI-designed drugs[2657.92] [2657.92][S03]that are in the clinic now in clinical trials[2660.83] [2660.83][S03]and the different levels of AI input into those current drugs,[2665.09] [2665.09][S03]I would imagine that in the next five years[2667.427] [2667.427][S03]or so, we're going to see an approval of one[2669.26] [2669.26][S03]of those medicines.[2671.06] [2671.06][S03]But for me, the big thing is going[2672.5] [2672.5][S03]to be, when can AI start to really fill out this pipeline[2676.76] [2676.76][S03]and start to get drugs into the clinic really quickly[2679.1] [2679.1][S03]and really start to deliver molecules for patients?[2681.51] [2681.51][S03]That, for me, will be when AI is having a really big impact.[2684.7] [2684.7][S01] When you can start to say, here's the target,[2687.69] [2687.69][S01]and then it pops out a drug at the end.[2689.64] [2689.64][S03] Yeah, and you can put that[2691.307] [2691.307][S03]straight into the clinic.[2692.349] [2692.349][S01] And be confident that it's not[2694.099] [2694.099][S01]going to cause any damage to a person.[2695.83] [2695.83][S03] And even a slightly improved level[2697.85] [2697.85][S03]of confidence in where we are now would be quite impactful.[2700.56] [2700.56][S03]Yeah.[2701.06] [2701.06][S02] Yeah, because as Becky said,[2702.39] [2702.39][S02]there's already molecules in the clinic that[2703.91] [2703.91][S02]have been touched by AI, that have[2705.41] [2705.41][S02]been enabled by AI in some way.[2707.353] [2707.353][S02]We're just going to see more and more of that.[2709.27] [2709.27][S02]In five years' time doing drug design without AI[2713.8] [2713.8][S02]will be like doing any sort of science without maths.[2716.62] [2716.62][S02]And to be honest, I think the whole of science[2718.54] [2718.54][S02]will be like this is, if you're not using AI,[2721.16] [2721.16][S02]what are you doing?[2722.18] [2722.18][S02]There's just so much information to be gained there.[2726.28] [2726.28][S02]So yeah, as Becky said, it's more like,[2728.41] [2728.41][S02]how do we actually see that rapid increase in disease areas[2732.587] [2732.587][S02]that we're able to tackle the targets,[2734.17] [2734.17][S02]that we're able to unlock, ultimately, patients[2737.315] [2737.315][S02]that we're able to help.[2738.315] [2738.315][S01] Amazing.[2739.48] [2739.48][S01]Thank you both.[2740.33] [2740.33][S01]That was really interesting.[2741.195] [2741.195][S03] Thank you for having us.[2742.64] [2742.64][S03]It was so much fun.[2743.51] [2743.51][S02] Yeah, it's been great to be here.[2744.83] [2744.83][S01] I think I now realize[2745.87] [2745.87][S01]that medicinal chemistry is one of the hardest[2747.94] [2747.94][S01]jobs in the world.[2749.0] [2749.0][S01]It takes years to design a drug.[2750.92] [2750.92][S01]Even if you get it to clinical trials, 90% of them fail.[2753.98] [2753.98][S01]And only one in 20 of your colleagues[2755.74] [2755.74][S01]ever manages to see their medicine improving[2758.35] [2758.35][S01]the lives of patients.[2760.13] [2760.13][S01]But strangely, that is precisely what I think[2763.24] [2763.24][S01]is so exciting about this space because if everything[2765.97] [2765.97][S01]we've done up until now has, effectively,[2767.76] [2767.76][S01]been like working in the dark, slowly, laboriously navigating[2771.81] [2771.81][S01]the most infinitesimally small areas of the vast landscape[2776.13] [2776.13][S01]of possibilities, it's like someone has just[2779.07] [2779.07][S01]turned on a floodlight.[2780.78] [2780.78][S01]And, OK, of course, we are still very, very far away[2785.04] [2785.04][S01]from a big AI button that's just going to solve all diseases.[2788.92] [2788.92][S01]But there is so much headroom here for improvement,[2791.59] [2791.59][S01]so much scope to move the dial, and simultaneously,[2795.43] [2795.43][S01]so much opportunity to directly impact the lives of all of us.[2800.31] [2800.31][S01]You have been listening to "Google DeepMind," the Podcast,[2802.77] [2802.77][S01]with me, Professor Hannah Fry.[2804.4] [2804.4][S01]If you enjoyed this episode, then[2806.1] [2806.1][S01]do subscribe to our YouTube channel,[2807.72] [2807.72][S01]or leave a review on your favorite podcast platform.[2810.52] [2810.52][S01]And, of course, we have plenty more episodes[2812.85] [2812.85][S01]on a whole range of topics to come, so do check those out.[2816.25] [2816.25][S01]See you next time.[2817.74] [2817.74][MUSIC PLAYING][2821.39]