After these days of learning about Gromax and the PMX Energy Calculations MD, it's my pleasure not to introduce you to Christian Magrater from AstraZeneca, one of the co-organizers of this event. And Christian will talk with more how these applications are used also from the industry point of view for the development of novel drugs. And it's a good example for the usage of the applications. So now we can see Christian's screen. All right, thanks Ross. Thanks for the opportunity to give this talk. Well, hearing for that, I thought what would be interesting to you guys as a different angle from the industry. So we have been applying MD simulations. So I'll talk a bit about that and also how it fits into our pipelines. And this will be a very biased pipeline obviously, because that's what we do in-house. But also if we replace individual steps without tools, the overall workflow scheme will stay the same. So bear with me for a bit. At the end I'll show you where MD simulations can actually impact drug discovery. That doesn't work. So just as a bit of an introduction. So AstraZeneca is a global pharmaceutical company. So we're really considered to be big pharma. And we have three different research and the lights in Gatorsburg in Cambridge and where we are located in Gothenburg. And more specifically, I am part of the Molecular AI department. So Molecular Artificial Intelligence. And we deal with two main questions in drug design. The first one being what to make next. So this is the compound idea generation. What could be a potential new drug? It's often referred to as the novel design. This is also the team I'm in. So we talk a bit more about the exploitation, exploration scenarios that we usually have and the main tools. One of which is reinvent and all of our tools are published. So if you're interested, you can look at those. So these are idea generation tools really using deep neural networks. And the other question is how to make it. And that's something that's often maybe not fully appreciated outside the industry. But it's actually a big problem to synthesize ideas that come out of our idea generation pipeline. The best idea doesn't help if you don't know a way how to make it, right? And this is what the synthesis prediction or deep chemistry team is dealing with. So they built a tool called AI Synth that allows you to predict reaction pathways in order to get the compound out in the end. We're also involved in other activities. Most prominently melody. So that's a data sharing privacy preserve data sharing effort across the industry to build better predictive models. But it's not part of the talk today. So the first day we had a very nice talk about the complications in drug discovery. So that saves me the hassle of going through it in too much detail again. But it's safe to say that drug discovery still is very slow and expensive. Drug record methods have helped a bit there. But if you look at the graph on the right, it's still the case that we start with a lot of compounds in the early stage. And then a lot of them are filtered out along the way, maybe because of liabilities and toxicity. And in the end, you have one marketed medicine after a couple of years and hundreds of millions of dollars. So this is still a problem. And if we can improve our predictions, we have a better... If we start here with better compounds, then the chance that we get something through is higher. So that's the whole idea. And another development that has gotten some momentum recently is that we can divide patients in smaller groups. So that's what we call patient stratification. Because we understand much more about the causes for this disease. And that is great because then we can generate bespoke medicines. But on the other hand side, that also leads to an explosion in the number of targets that we have to address. So to sum that up, our business success requires a reduction in the cycle time. And I'll introduce the A-Cycle on the next slide. And also the costs for the famous LiLO or lead identification optimization phase. So the DMT-A-Cycle design of a new compound set. Then you make it, then you test it in certain assets, you analyze the data. And whatever you learn from there, you feed back into the design stage and then you iterate that.  And our team or department works on the design stage. So I'll focus mostly on the idea generation bit here. We try to publish our stuff and we also try to publish the source code. So if you're interested, you can have a look here into those. But I haven't really explained what makes it so difficult for us to generate new ideas. And I think this might help there. So if you look at the chemical, so the collection of all of the compounds that it could potentially make. And this is just an illustration here. And this is enormous. This is estimated to be on the order of 10 to the power of 60 compounds that it could in principle look at. However, realistically, we can only access something on the order of 10 to the power of 10 or 10 to the power of 12. So what we will end up with is a very, very sparse set of areas of sub spaces in chemical space where we know something about and large where we have absolutely no idea. So, or what it says here, we have access to only a very negligible fraction of the chemical space. What can we do? So one way to generate new new ideas is to, for example, which I'll introduce. So that's a tool that we developed in how that's why I'm mentioning it. But there are a lot of different tools that you can apply to that as well. Is that you take a very, very large data set of molecules for sample database, and then you train a neural network on on that to reproduce that. And if you do that properly, then the network will not learn to reproduce the training examples that would be relatively meaningless, but it will learn the rules of chemistry. And that means that it will deduce implicitly from all the examples it has seen that it's not a good idea to add six substituents to a carbon atom, for example. So what we will end up with and that's the changing color of these weights here is a generative model that has a very good capacity. So if you think back on this picture of space that I just showed, we're able to access a lot of different spaces or a lot of different areas, because our model has a high generative capacity. But so if you start with that trained model, it will spit out just molecules that may conform to the rules of chemistry. On the other side of the street, that's fine, but then relevant for project 10. So let's say we want to fit a molecule into this binding cleft here and inhibit some target because that's relevant for disease. Then it's very unlikely that just by chance we get something out that actually fits here. So what we do then with our prior or naive model is that we subject it to reinforcement learning. We select compounds, run that through some scoring function. I'll come back to that on the next slide. And then the score is fed back to update the internal weights. So once in a while, just by chance it will receive a medium score or something that's slightly better than rubbish. And from that it can learn and optimize internal weights. And if you do that long enough and typically we're looking at cycle numbers of 1000 to 3000 iterations, then you end up with a bespoke model that is able to generate relevant project. And for the next project you do the same approach again. So one thing that I should mention are actually really good in generating compounds. So they look good, they conform to certain rules like that. The problem is the scoring. So there are all sorts of different scoring components that it can fill in here. But it's especially binding affinity remains a bit elusive. This is how we construct the scoring function and ignore the equation on the bottom. It's really just a composite of different components. So each of these components describes the molecules in a different way. So this could be molecular weight say. So you penalize everything that's too small or too big and within a certain range it receives a good score. So you will end up in the end with compounds that have a certain size. The second one could be the number of hydrogen bond donors. So you want to limit that maybe not to go crazy and so on and so forth. Now what is really, what would be really cool here is to get structural information in. And that's something that we've been working on quite a bit. And in our first attempt what we did is we wrapped several docking back ends. And that's from a recent preprint that we put on archive. And that is just one of the scoring components. So what happens is that you embed the compounds, you dock them, and the docking score is then fed back to inform the agent. And over time it learns how to generate molecules that produce a better docking score. That's illustrated in this graph here. I won't go into the details, but this was a public data set. We didn't tell the agent anything about the structure to generate other than it should have higher or a good docking scores. And you can see that if you look at any motor similarities, that's the similarity measure of the compounds produced to known actives. So things that we noted are binding. You see that you have at least a couple that might very well come also from the active set that the agent hasn't seen. And although the decoy set was much, much larger, you don't see that in here. That's the idea. Okay, so far I haven't talked about MD simulations at all. But if you think about the limitations that docking has as a proxy for binding affinity, it becomes clear where we are going here. But just to give you an overview, so I thought about what kind of applications we have internally for MD simulations at this moment. And also ask colleagues what they do in everyday life. This is just a small overview here. So all of what I talked about so far is concerning small molecules, so the things we just seen. But there's also a big push towards new modalities or biologics. So this could be peptides or oligonucleotides, for example. So in the small molecules world, what we could do is we use MD simulations for a septic configuration sampling. So instead of just having one big stray structure for a docking virtual screening, for example, or even using it in reinvent as a score component, it could have multiple taken from a MD simulation. Another thing that a couple of colleagues do is using MD simulations as some sort of sanity check for docking poses. They're all in the process of using it for pocket identification. So that is very important if you have to decide whether a target is druggable, as we say. So whether it's actually suitable to be targeted by small molecules or not. And that's still a very tricky question. Because to actually know that you would have to have the product, the molecule already. So you need to define whether a pocket would, in principle, qualify to be druggable or not. But the biggest impact that MD simulations have had and will have to have is relative and absolute binary energy calculations. And I come back to that. Actually, it's very nice that Vitas Talks just introduced that concept so neatly. Yeah, but for new modalities, it's more about the structural understanding. So this is very early days. We're applying it to a lot of different new targets that are not druggable by small molecules. But it's still Wild West. So it's not a really streamlined process. And that's across the industry. It's not just us. But you could also use it, in principle, to predict properties such as the melting temperature from MD simulations. But going to small molecules, this is just what I introduced. So the receptor configurations are. So instead of just taking a crystal structure, you use it to actually run an MD simulation over the course of 100 nanoseconds or so, you cluster it, and then you select representative conformations from these snapshots. And that's actually something that we recently implemented to do ensemble docking in the reinforcement learning group of 3Invents idea generation. So that should help us enrich the configurational sampling a lot. The other thing is just simply running Holos simulations. And we have automated workflows to do that using Gromax. And this is from one of these. This is just a test one. So you have a protein and a docked or actually it's native ligand. So you have a ligand attached to it. And then you see how this part of the protein changes over the course of this simulation. And you can use that to check whether a docking pose is reasonable or not. So if you see that ligand flying away after two nanoseconds, it's probably not a good thing. So this helps to enhance our structured understanding. And as I said, it serves as a sanity check for docking poses.  So if there is a hydrogen bond, for example, that is supposedly very, very important, then you see it appearing and disappearing over the course of the simulation. And that is something that they can relate to a lot. So what is often misunderstood is that we need to convince chemists to actually invest resources to do what we propose they should do. And we're competing with a lot of other techniques and inputs. So we have limited resources and we need to allocate them the best. So MD simulations are an excellent communication tool as well. But the biggest impact of, at least in the small molecule world, of MD simulations is relative binary energies, at least to this day. So what we often end up with is a representative set of novel ideas or a series. So we have an initial hit, let's say it's this compound here in the middle, and then some way or another we get a whole set around this initial hit. So that could be enumeration, that could be by reinvent, whatever. And these are 2D. So if you want to actually calculate that, so what we actually want to, I should add that. So what we want to have is we want to get some insights whether it would be a good idea to go to this or this or this derivative of the initial hit. And binding affinity is a very important criteria. So the first step is to embed these 2D molecules in 3D. You also take care of things like stereochemistry and protonation states, etc. Once you've done that, you can stop talking to fit these molecules into the bindings left that you have defined using the reference ligand usually. So what we start with internally is most often is one or multiple X-ray structures and some experimentally determined binding affinities. From that, and I'm brushing over a lot of details here, but from that construct what we call a perturbation map. That's how we communicate internally, but you could also see that as a bunch of transitions from here to there, from here to there, from here to there simply. So it's a pairwise thing. And again, for this central one here, let's say we have an experimentally determined binding affinity.  And that's what we communicate to the chemists. So they can see, well, OK, going from here to there, here to there might be preferable. And often they look at the snapshots that we get from the MD simulations and say, OK, this is picking up a new interaction here or this is important because there was a water there stuff like that. So this is really, really good for hypothesis building. And it works better than docking because it takes more into account. So for example, receptor dynamics and a whole bunch of other properties. And as I said, it's helping in making a case. So unfortunately, I cannot show you internal project data, but I can assure you that we have successfully applied that to a number of different projects from different disease areas in the last two and a half years, I would say, or three years maybe. But this is a project of a colleague of mine where he was involved in generating it. This has been published. So you see that here you have this scaffold here. And what they did is they exchanged this R group here by Florent-Claurin and so on. And they were able to identify a couple of nice things that also paved the way. So it was published recently, but it's older work, I believe. So this paved the way for other follow-up studies. So they identified a pocket and also were able to use FEP on those things. And this is great. The main problem, you can already see that probably here, is that these methods require you to have a large chunk of the molecule to be the same. And that's just what Vita has alluded to. So you have to have a certain phase-based overlap here. So in other words, these modifications here, that's small. So if you look at another compound like this, and let's say this is our starting molecule, then it's an easy task to go from here to there. So this number here indicates how far they are apart structurally. But it might be more tricky to do these jumps. And these jumps here might be, so going from here to there might already be out of scope for relative binding for energy techniques. But unfortunately, that's exactly what we are more and more interested in doing these careful hops or jumps outside the non-chemical space. This is very important because sometimes we have liabilities, toxicity issues, off-target effects, or other things that just won't work. And you can already see that. So it's actually quite disappointing if you have compounds that bind well, show good enzymatic inhibition but then for some other downstream problem like toxicity, they will never make it to the market, right? And then we're looking for a backup series. And that would be great if you could do that simply by going from here to there, or even ignoring completely where we started from in the first place. To drive that point home in a slightly different representation, so consider this UMAP embedding here. So this is based on structural descriptors of fingerprints of compounds. So this is representing chemical space. Each of these dots is one compound. And these are completely arbitrary dimensions. Don't worry about them. It just means that these points here are closer together to one another than these points here. And let's assume we start here in this unitization stage, and this is the compound we're looking at. Then using relative binding free energy methods, we could access maybe something within the circle here, right? So we could explore these other molecules in here. And that's great. But one problem, or the biggest problem here is what about the other scaffolds that might be interesting here as well, right? Some of them might be more easily accessible, but for others, these jumps might be just too far. What we do now in this... Sorry, my point to disappear. So what we do at the moment is that we would ask the synthetic teams to synthesize something in here and also get a new crystal structure, and then we take it from there. This takes time. This takes money, and you might be unlucky and pick one of these candidates in here that are just not active. So you would discard this whole area just because this could be a treasure trove, right? But you're just discarding it because you made the wrong pick. And that's not great. What I would prefer, or what we would prefer to do is we could simply access these things right out of the box. Yeah, so much for small molecules. Now I have one slide on new modalities. So we're also investing a lot into oligonucleotides because they have different mode of action. So they hybridize with some RNA. And in this case, for ASOS, they hybridize with some RNA in the cell that is degraded. And by that, you can target things that are not drug-able by small molecules. And as I said, this is very early stage. So we're still trying to find the right setups, both experimentally and computationally. These were some initial simulations. We ran our ultimate pipeline. So you have a simulation time here, 100 nanoseconds, and three identical replicates. It's just different, the initial velocities. And what we did is we did a backbone clustering. So you see that if you look at the populations of overall populations, you see that the first cluster is almost 50%. And this is the CMS, the central member structure of that cluster. So it's a fairly stretched configuration. And you see that appear and disappear over the course of the simulation quite a bit in the first two, at least. So here we have a couple of problems that we need to address. And we're recruiting people to do that. But we're also reaching out to academic collaborators because as Vita said, the force fields are not that great and we need to do all sorts of modifications here. So this is really a tricky bit. Also, we don't reach conversions in these small timescales and a lot of other issues that happen here. I just wanted to bring it up because this is a different angle and these simulations will have an impact in the future because structurally, those things are not very accessible. Okay. So that is already the wrap-up slide to bring together what requirements we have to make the biggest impact in pharma. First of all, we need accurate results. And I put accurate in quotation marks here because we're not necessarily concerned with a certain threshold. Say, we don't need the results to be within half a k-cald per mole or something to be useful. For us, it's often enough to just be able to rank them. We have pipelines that require a certain feed-in at any given time. So if I was able to rank a couple of compounds, say a hundred compounds, and I say, take these 20, put them forward, and there are a couple of hits there that's often that would suffice completely, right? And it's also good to build convincing hypotheses. That has changed a lot in the last years because we had successful project impact. People consider MD simulation as being a technique that is more trustworthy than it used to be maybe five or 10 years ago. We also need sufficient throughput because that's something that's also, maybe something that's not immediately clear. These DMTA cycle iterations have become very, very fast. So projects change the goalposts a lot. So if it takes me a couple of weeks to come up with an answer to problem A, problem A might not be relevant anymore once I have my answer. So we need to, that's another thing. So if I had to balance accuracy and throughput, I would often be on the side of throughput just because having a reasonable answer now is superior to having the best answer later down the road, right? Then what we need, this is more of a practical thing, of course, we need automated and stable workflows. So MD simulations haven't had a reputation for being credibly stable and that's a problem because there are a lot of different options for people to do that, for Comchemists to come up with solutions. And if it doesn't work a couple of times, they won't come back. They simply drop that and do something else. So that's also something that we are working on to provide those tools to wrap what comes out from academia or from also proprietary software developers and make that work for our scientists. And that brings me to the next part. So ease of use. You have to think that there's a lot of different people using your software. It will not just be computational chemists that invest a lot of time to setting those things up. So one of the biggest edges that proprietary software has over academic codes is the ease of use. Just the way it's interfaced with a GUI or something, it helps a lot. And last but not least, we have and we want to collaborate with academia because we're not in a position to build our own protocols. So the non-equilibrium approach that we just introduced and the previous talks, we wouldn't have had the resource to come up with something like that. So we rely on academia to develop that with and for us. And that's why BioExcel is so great from our perspective. And we're also shifting a lot of interest on Gromax and especially PMX from the growths lab. So we hope that we can collaborate further with Vitas and Bert. Yeah, and that brings me to the end of the talk. So this is the department at the moment. And if you have any questions, I'm happy to answer them. Richard, this was a very good overview of the different methods used by pharmaceutical companies. There is a question in the chat, which method are you using for relative binding free energy calculation? Do you use FEPPLUS? As of now, yes. FEPPLUS is the one that we mostly rely on in-house. We're trying to explore different things, both from other vendors but also from academia. But FEPPLUS has certain edges that I cannot comment on that we need at the moment. Yeah, as you were explaining, it's very difficult. Area 1 needs to use a lot of different methods to try to find consensus with different applications. It's very challenging. It's not straightforward. We're still not at a point in science and technology where you can just push a button and it will give you a drug that works. But overall, I think science has made tremendous progress in this direction and hopefully we'll get closer and closer to this. Absolutely. And as I said, there are a lot of projects in-house that are running a main or a backup series that was derived using those methods. So this is not hypothetical anymore. This is not what could be done. This is being done. Yes. All right. I don't see any questions in the chat. Well, thank you Christian for the presentation and thank you everyone. Now we are going away for a break until 2 p.m. Central European time when we will continue with docking and specifically presentation by Alexander Bonven about HADOC, one of the very popular applications in the field. So thank you for the morning session. Thanks for all the presenters and we'll see each other in one hour and 30 minutes.


People don't think hard enough about what nuclear war is and what it would mean. There's a John Meersheimer video clip from 2016 that's going viral on Twitter right now. As old John Meersheimer clips tend to do in the year 2022 when his predictions that Western actions would lead to the destruction of Ukraine are coming horrifyingly true. In response to a question about what the worst US foreign policy disaster has been, Meersheimer agreed with a fellow panelist that at the moment Iraq looked like the worst, but said he believed US policy on Ukraine would prove much worse in coming years. He spoke of the fact that Russia has thousands of nuclear weapons, and that it's entirely possible those weapons will be used if Russia feels threatened. Because the Cold War is in the distant past, most people, especially younger people, haven't thought a lot about nuclear weapons and nuclear deterrence, and they tend to be quite cavalier in their comments about nuclear weapons, and this makes me very nervous, Meersheimer said. It makes me nervous too. Especially when we've got a steadily escalating proxy war, which the standoff in Lithuania could easily see spin out into a direct hot war between Russia and NATO powers, and when we hear the UK's top army general telling troops to prepare for World War III. Most of what I see in public discourse about escalating aggressions between the US power alliance and Russia reflects the cavalier attitude Meersheimer spoke of in 2016, as do my own interactions with people online. Most of what I'm seeing in the behavior of NATO powers indicates this cavalier attitude about nuclear weapons as well. People, from the rank and file public to the upper echelons of empire management, don't seem to be thinking very hard about what nuclear war is and what it would mean. As Meersheimer said, this does seem to be because we're so removed now from the days when everyone was acutely aware that missiles could start flying at any time. It just doesn't sit well with people's understanding of the world that it could all end through the same nuclear Armageddon scenario their grandparents used to worry about. If two men were holding guns to each other's heads it would be experienced as very dangerous at first, but after a while if nobody pulled the trigger the emotional tension would begin to diminish. If years went by and the men got older it would diminish even further. If they got so old they couldn't hold the guns anymore and had their children take over for them, and then their children's children years later, the emotional experience of that standoff would be all but forgotten. But the guns never got any less deadly. The fact that nuclear war hasn't happened yet means only that, that it hasn't happened yet. Things that have never happened before happen all the time. There didn't used to be nuclear weapons, now there are. Earth is currently a habitable planet. One day soon it may not be. We came within a hair's breadth of wiping ourselves out during the last Cold War, not just once but many times. Any amount of nuclear brinkmanship opens up the possibility of nuclear war erupting in ways that are too hard to anticipate and plan for, because there are too many small moving parts. Too many ways a nuke could be detonated as a result of technical malfunction, miscommunication, miscalculation and or misunderstanding. The further things escalate between the world's two nuclear superpowers, the greater the likelihood of this happening. And of course the powerful have every reason to encourage this way of thinking to continue. If a critical mass of the population really understood that their lives are being threatened with nuclear war for no other reason than the US Empire's willingness to risk everything to secure planetary hegemony, they would immediately become hard to deal with. Empire managers plan on not just engaging in nuclear brinkmanship but also making things much harder on the public financially in their long term agendas against Russia and China. And the only way everyone plays along with this is if they are kept from understanding what's being done to them. This is why the media have been acting so strange in recent years. Agendas are being rolled out which no sane person would consent to if they fully understood them, so their consent needs to be manufactured with massive amounts of propaganda. It's also why internet censorship has taken a high priority during that same period of time. Can't have people using their new found information sharing capability to interfere in the narrative manipulations of that empire. We are being sedated into a propaganda induced coma while immensely powerful people play profoundly dangerous games with their lives. It is in our interest to find a way to awaken as soon as possible.


Live from Washington D.C., it's theCUBE, covering Boomi World 19, brought to you by Boomi. Welcome to theCUBE, Lisa Martin at Boomi World 2019 in Washington D.C., with John Furrier this week. John and I are very pleased to welcome back to theCUBE the COO of Boomi, Chris Port. Chris, welcome back. Thank you, thank you for having me. So yesterday was the partner summit. Today kicks off everything. Let's look at where we were only 11 months ago at Boomi World 18 when we sat down with you in Las Vegas. You now have 9,000 plus customers in 80 plus countries, 580, I think, partners globally. It's amazing the growth, and those are just some of the stats that were shared this morning, the 97% renewal rate, which is huge. Really exciting news coming out this morning for Boomi. You guys have done a great job of listening to your customers and evaluating their data to deliver outstanding cloud-native technology. Talk to us about what's transpired since we last saw you that really has you excited. Yeah, well, look, growth is exciting. So a lot of growth, we just finished an almost 50% growth quarter. So the teams continue to grow. I mean, I think we talked about three pillars last year around product, go-to-market and success. So I can tell you our product team, we've got new people from the leadership level, kind of like Steve Wood was here as the Chief Product Officer. He's still here, but now he's bringing in people from a leadership perspective, augmenting our incredible leadership team that we've already got, as well as kind of as we think about building out that layer as we kind of build out our development teams and our product management teams. So a lot of growth there. From a go-to-market, you just talked about 80 countries, 9,000 plus customers adding six to seven a day depending upon the day. So, and then success, the one thing that we've really done is we've kind of hardened the methodology. We've added a significant number of team members under me as we kind of think about that success equation and really build it out, driving towards the 97, 98% kind of direct side retention on the dollar calculation. And we're now really starting to do some things where we're really starting to look at when we have our success people engaged and what that drives from a cross-sell and expansion, what we really enable our customers to do, and what we've seen is it's about a 30 to 40% uplift. So really kind of giving us even more ammunition to double down on that. So just I saw some demos on the conversational AI that Chris McNabb was demoing with Mandy, actually with the voice attendant there. And they were referencing headcount. Would those actual numbers have 700 new employees added to Bumi in the last quarter? Oh, not in the last quarter. Yeah, yeah, yeah, yeah, on the demo.  I mean, it's grown seven X since I've been back and that's three and a half years. Can you talk about headcount numbers at all? Yeah, we don't really publicly disclose that, but we're north of a thousand. We had a goal in terms of, you know, Chris used to talk about the road to certain dollar figures and I can tell you we just blew through our third goal since I've been here in three and a half years ahead of schedule of all of them. I guess some good leverage from Dell Technologies Absolutely, so Dell Technologies and what they've done to really start to be a little bit more of an accelerant, we're incredibly excited about what Dell Technologies can do with us in the Fed space. I was just in a federal breakout and Dell has such a great presence in the federal space in such great relationships and that should absolutely be a forced multiplier and accelerator for us there as well. Let's talk about that a little bit more from a federal perspective. Here we are in Washington, D.C., Bumi announced maybe six or so weeks ago in August, FedRAMP authorization and one of the first IPAS vendors in the marketplace, but interestingly what Chris McNabb shared this morning was that Bumi achieved FedRAMP certification in five months and one of your competitors, I think I know who it is, took 18 months. So John and I have been talking about time to value with every interview today. Talk to us a little bit about what that FedRAMP marketplace means not just to your federal businesses but to Bumi's platform and capabilities in general. Yes and I think Chris started at this morning right is that when you think about the number of controls that we had to go through to get that certification and the ability to do it in that five month period, I think it highlights, A, where we're at but the investment that we've made but candidly the architecture and back to the customer why do they care? Because granted, federal is very important to us but candidly we have 9,000 plus customers because we just got started, right? We do have our first Fed customer but we're not allowed to disclose who it is yet but 9,000 plus customers that aren't in Fed obviously and why do they care? It's about the increased security. It's effectively the stamp of approval in terms of our scalability and just what we've done to invest in their future because it's so paramount and being kind of a trusted advisor, being a software provider is one thing but trust has just become so much at the forefront. I don't know how many discussions I have on the pre-sale cycle and if it's not in every discussion it's in nine out of 10 now. Yeah Chris, in today's business climate you can't really go a couple of minutes without hearing about we work, pulled their IPO, software economics are driving valuations of real profitable companies like Zoom and others and then there's the unicorns that aren't making anyone losing money kind of the Wall Street's kind of reacting to that but the customers look at the business model of companies they partner with. I want you to take a minute to explain Boomi's business model. You guys are a modern software company so you have go to markets with engagement journeys and direct sales, partnerships to the ecosystem but you're also got the cloud dynamical and you got SaaS. So you got the, I mean SaaS companies are getting great valuations and are highly profitable. So the operating leverage with SaaS combined with how you guys are deploying is very interesting. Can you explain for people that aren't yet Boomi customers what the business model is and how they engage with you and what should they expect? Yeah, well look, I mean I think it all starts from our architecture, right? So the way the software is architected is it just absolutely facilitates an ease of use and a time to value that's unmatched in the space. So bringing to that the 9,000 plus customers so you're honestly talking about because when you look at our space so it all starts there from a strategic construct you have kind of legacy providers as well as some of the newer names that are what I would call high control. And we may have talked a little bit about this last year but they're in this high control. They require a fair amount of development. They have long lead times in terms of getting to that time to value. Then you have kind of the new school and Boomi's certainly over here. We pioneered it, which is high productivity, high time to value. Again, we want to cut projects from nine months historically that a customer would maybe engage on. We want to make that 90 days. We want to make that nine days. So everything starts from there and our entire go-to market has been built off that. So what does that mean? When you think about the backs of our partners you know we really started out with other ISVs that were in the SaaS space and how could we add to their value prop? Because candily integration can be a barrier to a SaaS application. Take a concur, a success factor to their adoption. So we remove that barrier but in the same time, the same speed, the same agility as they do. Yeah so agility, great value purposes. Look that straight in the eye, check, love that. How do they buy? Do they pay? How do they pay you? Just talk about the economics real quick.  So we moved obviously from this perpetual kind of CapEx model to the SaaS model which is much more OpEx focused but again, it's smaller bytes. I mean, our customers aren't paying us. You know, hey, it'd be great if they did but they don't have to and we're getting bigger and bigger but it's typically through expansion versus this massive long sale cycle. Pay us five million upfront and then pay us a 20% drip for the rest of your life. It's all, you know, it's basically a fixed fee annually. They pay us for that first year and they pay us for the second year and it's my team's job to make sure they're renewing every year so that we continue to be good stewards, good partners with them and hopefully as they find value and we find that they do, typical Boomi customer, particularly in the enterprise, doubles their use of Boomi within about an 18 month timeframe. And that's the Amazon pioneering model which is you lower the price for your customer but your mix of business just gets bigger so you're dropping the health price for the customer but you get more customers. Exactly, it's good economics. Yeah, and I mean, it's just, it's about getting in there, proving the value of the technology and then look, you heard it this morning, you've heard just so many compelling stories. Our customers will absolutely continue to find one more use and one more use and one more use and they will just constantly double and then double again and double again their use of Boomi, so. Integrations isn't going away. It's kind of like storage and data. Like you got to store data. Yeah, absolutely. You always be storage, always be integration. Yeah, yeah. I talked to some customers yesterday, Chris, who articulated just that in terms of the unexpected benefits that integrating Boomi with, whether it's a transport management system or a sales force and suddenly they're starting to see so many more other downstream benefits that they couldn't even have forecast. Yes. When they first started saying, well we've got to integrate these two things and the opportunities. But one of the things that came up in some of those customer conversations that I want to talk with you about is from an architectural differentiation standpoint. Boomi says, cloud native, single instance, multi-tenant cloud application delivered as a service. Talk to us architecturally about how that is, what is that and why is that so unique for Boomi to deliver? Sure, that's a great question. So single instance means that every single one of our 9,000 plus customers is on the same version of Boomi. So we do 11 releases a year. We don't do it in December because a lot of retail customers and a lot of customers go under moratoriums in December. So we don't disrupt business in December but 11 releases a year and what that means is every time we do a release that all 9,000 plus customers on its way to 10,000 and 20,000, they get the same version of Boomi every month. They're all working off that same version. Now, they like that because there's no physical upgrades but the reason single instance means so much is again, Chris talked about the 30 terabytes of anonymized data. You can't do that unless you have a single instant software. So that's kind of the secret sauce. Our ability to do things like Boomi suggests that Chris talked about which candidly was the first real use of AI in middleware, right? Michael Morton is going to talk tomorrow about this insights platform that we're now launching that really will start to get into data privacy to start but there's so many different things. I mean, again, this is literally our fundamental fair advantage. I mean, like nobody else has this. Nobody else hasn't even close to 9,000 plus customers. We see everything they do and it's our opportunity to unlock that and show them the value, not just suggest, not just automated regression testing, not just insights tomorrow, but what are the next three, five, 10 things we can do to absolutely accelerate that job? That's data driven. That is absolutely data driven. That's definition of data driven. Okay, so I got to get your definition of something I'm hearing a lot of. I kind of got my view of this, but I want to get yours. What is, in your Boomi's world, what does event driven mean? Because we hear about event driven architecture. What is that? Well, I mean, look, think about real time. I mean, historically there's been a lot of, from a process perspective, you know, batch. You know, it's not necessarily done in real time. Event driven is more listening and responding. So how do I become much more, from a software perspective, how do I become much more real time to listen to those different events that are in my ecosystem? Could be something a customer's doing. Could be something that you're doing as a finance employee. So it depends on what the use case is, but how do I respond to that event with a subsequent event, but more in a real time kind of, you know, away? So the classic definition of events being something happened, a trigger, software policies, to stuff that you can react to. Yeah, yeah. And that's my definition. You should talk to Steve Wood, talk to Michael Morton. I'm sure that, that'll be much more eloquent, but that would be my perspective. We're going to pin them down. My final question is culture. Boomi's got a very cool culture. I asked this last year. You guys are still feeling very much like a startup. And the culture and the customers get great customer loyalty. Lisa was pointing that out on our opening. So this has got a good momentum there with the culture. Your thoughts on how it goes next level, because as you're growing, you got to keep an eye on culture. You want to grow as fast as you can, but within the norms of what's workable. Yeah, well, look, I mean, I'll say it's the number one priority for the entire company. That starts from Chris all the way down. So we have leadership meetings that then cascade down. I have my own leadership meetings. My leaders have their meetings. There's only one topic that is non-negotiable that should be on every agenda. How are we doing? How are our people doing? How are we doing as humans? Because, look, I look at it, I've been in a lot of companies. I got to be in management consulting. So I got to see a lot of leadership teams that were both good and maybe had opportunities for improvement. I got to see a lot of companies. I've now been part of something, but candidly, these three and a half years, I've never been part of something like this. And it's a family. And it's just totally different. It's totally different. I say it all the time in our town halls, but I mean it. So I look at this as a once in a lifetime. These opportunities just don't come around that often. To go from how many people we had, just even when I got back three and a half years ago, to how many we have today, to think that my team is, my own personal team now is two and a half times bigger than Boomi was three and a half years ago to give you a scale perspective. And so it's just, it's a topic every day is how do we invest in people and how do we keep this going? You guys got a lot of challenges too with the growth. And I want to get your thoughts on this. One is the new branding looks awesome. We wore some Boomi t-shirts out last night. We were at the Washington Nationals game and give it a test drive. People were like, what's Boomi? Very strong reaction. But that's the question. What's Boomi? You got to answer that question. So that's one comment I want to get from you. The other one is the focus on community and education is some work areas for you guys. So the new brand is going to get awareness. What's Boomi? You got to answer that. What is Boomi? And then community and education is a focus area. As COO, how are you going to attack those opportunities and challenges as a leader? On the brand, I think this is a real opportunity for us to really accelerate and amplify our voice in the market. In Mandy's here, I think the things we're doing, I think you're going to see us really start to target the CX level. Like what is that CEO, that COO, that CIO? What are he or she thinking about? And really go after them to make sure that when they start thinking about integration, flow, hub, whatever it may be, that Boomi absolutely is part of their vernacular. And I think it's today, the number of times I hear that today that you're saying like, what's Boomi? So much less than it was three and a half years ago. So I think we've made some good inroads there, but I really think this is our next level, our opportunity just to completely, let's get that out of the way. We want to be a household name. We want the B to be iconic. So, and I think we're on our way, right? It's going to be a journey, but I think this is a great kind of launching pad. In terms of learning certifications, yeah, that's what we talked about today. We launched Boomiverse, very excited. 65,000 members? Absolutely, and we need that to be double, triple, quadruple. And that's all part of like accelerating this journey. We were literally doing five certifications. This is a global number, but five certifications a day, three years ago, we literally just closed a week where we did 50 a day. So 10X, we've opened it up and that's kind of, our big thing is like, it's free. We want the world to come in and learn about Boomi, build that skill set. The hundreds and thousands of jobs when you just start going and looking for Boomi in terms of job sites. It's just, it's not about a lack of opportunity. It's about our ability to fill those jobs. And we, and I look at that as my responsibility, our team's responsibility, because I want it to be an iconic brand. When you have a resume, I want Boomi to be front and center in terms of the skill sets that you're highlighting because it truly can change people's careers. And you saw some of the stuff we're doing with veterans and- It's fantastic. It really is and it's because of the opportunity that we see and forget 20,000, we need 50,000, 100,000 certifications and we're well on our way. And I think you'll just see us accelerate that and I think Boomi versus that launching pad. Great stuff. Well, you guys all look very well rested for how much innovation is going on at scale. Chris, thank you for joining John and me on theCUBE today. It's been a pleasure. Thank you so much. For Chris Port and John Furrier, I'm Lisa Martin and you're watching theCUBE from Boomi World 19. Thanks for watching.


The three problems for English learners in Vietnam. With the gross domestic product growing at the rate of about 6.88% per year, Vietnam is one of the fastest growing economies in Southeast Asia as well as the world. The increase in disposable income has made extra language sessions outside of school, much more accessible to middle and lower class families. Similar to other Asian countries, exposure to Western cultures through the internet has made English the most desirable language to learn in Vietnam. Opportunities. For many new college graduates, being fluent in English can give them a tremendous leap in salary. For example, the average Vietnamese office worker earns about 8.3 million Vietnamese dong or about $354 per month, whereas the average monthly income for someone, who works at an international company can range from 15 million to 20 million Vietnamese dong depends on the experience level. With the expansion of Vietnam's economy and the increase in investments from foreign enterprises, the demand for an English-speaking workforce has been soaring in recent years. However, despite the government and the people's effort to implement English in the early stages of the educational system, many Vietnamese still have trouble with English due to the lack of proper training for teachers and traditional schools, the lack of environment to practice English, and the reserved culture. 1. English learning and educational system. A decade ago, children won't start learning English until elementary school, where they will then jump straight into learning about grammar rather than vocabularies. In the recognition of the importance of English, children are learning basic vocabulary starting from preschools. In spite of the changes in the educational system, most teachers in traditional schools are not properly trained to teach proper English, especially teachers from the previous generations. Older teachers often speak English with heavy Vietnamese accents. Thus, the students also learn to speak English with heavy accents. Since the students are familiar with this particular way of English pronunciation, it's hard for them to fix their accents as they get older. This resulted in a generation of young Vietnamese people who are proficient in English grammar, but struggle to get people to understand their spoken English. 2. Non-native speaking environment. Another problem that Vietnamese students face when learning English is the lack of English-speaking environment for them to practice. Students in large metropolitan cities such as Ho Chi Minh City or Hanoi often have excellent to perfect English grammar. However, they don't get to practice their English much besides from classrooms. On the other hand, young children and teenagers from more rural areas, such as Sapa are more fluent in spoken English due to their everyday exchanges with tourists despite not knowing how to read and write in English. This is a disadvantage for many students, as they are unable to answer or present their works and situations, such as job interviews or when they need to present a project at school or work. 3. EFL in early childhood. Lastly, the biggest problem for English learners in Vietnam is the reserved culture that they are being taught at a young age. To be able to learn and practice English effectively, students must be confident in asking and answering questions, even if their English is far from perfect. This contradicts with the reserved Vietnamese culture where students are taught at a young age to not question authorities, such as teachers making them shy and timid in other environments as well. Some students are afraid to make mistakes. Thus, shy away from speaking English. Even if they get perfect scores on English exams at school, most students lack the basis of spoken English. Therefore, cannot actually use their English in everyday situations. Do you want to teach in Vietnam? Take a TEFL course to have a competitive advantage.