 Hello, everyone. On today's panel, the theme is drug discovery and how artificial intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, Principal Scientist at the National Centre for Natural Products Research, with a focus on botanicals. Specifically, there are pharmacokinetics, which is essentially how the drug changes over time in our body, and pharmacodynamics, which is essentially how drugs affects our body. And of particular interest to him is the use of AI in pre-clinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment, and aging. Welcome, Ryan. Thank you for coming. Good morning. Thank you for having me. The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for high performance computing and artificial intelligence, with a PhD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles, and social network analysis. Hey, Rangan. Welcome. Thank you for coming. We have also our distinguished Chris Davidson, he's leader of our HPC and AI application and performance engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services, and life sciences. Yes, our particular interest is life sciences. He spent 10 years in biotech and medical diagnostics. Hey, hi, Chris. Welcome. Thank you for coming. See you. Well, let's start with you, Chris. Yes, you regularly interface with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know, tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. Right. And that's a good question. I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into, I would say, four different areas. There's the research and development space, the preclinical studies space, clinical trials and regulatory review. And if you're lucky, hopefully approval. Traditionally, this is a slow, arduous process. It costs a lot of money and there's a high amount of error, right? However, this process by its very nature is highly iterative and has just a huge amount of data, right? It's very data intensive, right? And it's these characteristics that make this process a great target for kind of new approaches and different ways of doing things, right? So for the sake of discussion, right? Go ahead. Yes. So you mentioned data intensive. It brings to mind artificial intelligence. So artificial intelligence is making a difference here in this process. Right. And some of those novel approaches are actually based on artificial intelligence, whether it's deep learning or machine learning, et cetera. You know, a prime example would say, let's just say, for the sake of discussion, let's say there's a brand new virus causes flu-like symptoms, shall not be named. If we focus kind of on the R&D phase, right, our goal is really to identify a target for the treatment and then screen compounds against the, which, you know, which ones we take forward, right? To this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy, can provide us a near atomic biomolecular map of the samples that we're studying, right? Whether that's a virus, a microbe, the cell that it's attaching to, and so on, right? AI, for instance, has been used in the particle-sticking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right, whether they have good resolution or not, whether it's in the field or the frame. Image recognition is a huge part of this. It's massive amounts of data, and AI can be very easily, you know, used to approach that. Right. So with docking, you can take the biomolecular maps that you've achieved from cryo-electron microscopy, and you can take those and input that into the docking application, and then run multiple iterations to figure out which will give you the best fit. AI, again, right? This is an iterative process. It's extremely data-intensive. It's an easy way to just apply AI and get that best fit. Doing something in a very, you know, analog manner that would just take humans very long time to do, or traditional computing a very long time to do. Ryan, Ryan, your work at the NCNPR, you know, very exciting. You know, after all, you know, at some point in history, just about all drugs were from natural products. So it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call polypharmacology is the answer to complete drug therapy. Please tell us a bit more with your work there. Yeah, thank you again for having me here this morning, Dr. Goh. It's a pleasure to be here. And as you said, I'm from the National Center for Natural Products Research. You're going to refer to it as the NCNPR here in Oxford, Mississippi, on the Ole Miss campus. Beautiful setting here in the South. And so what, as you said, historically, what the drug discovery process has been, and it's really not a drug discovery process. It's really a therapy process, traditional medicine, as we've looked at natural products from medicinal plants, okay, and these extracts. And so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR. One of those prime assets, unique assets, is our medicinal plant repository, which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right, is to screen, mine, that repository for activity. So whether you have a disease of interest, or whether you have a target of interest, then you can use this medicinal plant repository to look for active, in this case, active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants, which leads me to the second unique asset here at the NCNPR. And that is what I'll call a plant deconstruction laboratory. So without going into great detail. But what that allows us to do is, through a high throughput workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants. Right. And so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or taxol, right, as in any cancer drug, right. Now we can do that with this system on the matter of days or weeks. Now we're talking about activity from the plant and extract down to phytochemical characterization on a time scale, which starts to make sense in modern drug discovery. All right. And so now, if you look at these phytochemicals, right, and you ask yourself, well, sort of who is interested in that and why right well we are traditional pharmaceutical companies, right, which I've been working with for 20 or over 25 years now, right, and he uses these natural products where historically as use these natural products as starting points for new drugs, right. So in other words, take this phytochemical and make chemical synthetic modifications in order to achieve a potential drug but in the context of natural products, unlike the pharmaceutical realm, there is oftentimes a big knowledge gap between a disease and a plant. In other words, I have a plant that has activity, but how to connect those dots has been really laborious time consuming. So it took us probably 50 years to go from salicylic acid and willow bark to synthesize a pseudo salicylic acid or aspirin just it just doesn't work in today's environment so casting about trying to figure out how we expedite that process. That's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner Dr. Ron Tsukomar describing all the interesting things that he was doing in the area of artificial intelligence. And one of my favorite parts of this story is basically unannounced I arrived at his doorstep in Oak Ridge he was working at Oak Ridge National Labs at the time. I introduced myself to him didn't know I was coming didn't know who I was right and I said hey, you don't know me you don't know why I'm here. I said, but let me tell you what I want to do with your system, right. And so that kicked off a very fruitful collaboration and friendship. It's been the last four years using artificial intelligence and is culminated most recently in our COVID-19 project collaborative research between the NCMPR and HPE in this case. From what I can understand also as Chris has mentioned highly iterative as especially with these combination mixture of chemicals right in plants that could affect a disease. We need to put in effort to figure out what are the active components in that that affects it, the combination, and given the layman's way of understanding it. And therefore, iterative and highly data intensive. And I can see why Rangan can play a huge significant role here. Rangan. Thank you for joining us. So it's just nice segue to to bring you in here. You know, given given your work with Ryan over so many years now. I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things your work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that that history and and also the current work. So, so, Ryan, like you mentioned, walked into my office about four years ago and you'll say, Hey, I'm working on this omega three fatty acid. What can your system tell me about this omega three fatty acid. And I didn't even know how to spell omega three fatty acid. That's the disconnect between a technologist and the pharmacology person that helps your office right since then you've come a long way. He understands his terminologies now and he understands when I throw words like knowledge perhaps and page rank and then all kinds of weird stuff that that he's probably never heard in his life before right so. So it's been a month, my mouth of two different domains and terminologies and trying to accept each other's expertise and trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process. Right so I think about the discovery itself. And companies that are trying to accelerate the process to market right and average drug is taking 12 years to get to market, the process that Chris just mentioned right. And so companies are trying to adopt what's called in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro in silico in vivo environment right. So these silico techniques could include things like molecular docking could include artificial intelligence could include other data discovery method and so forth, and the essential component of all the things that that you know discovery workflows have is the ability to augment human to do the best by assisting them with what computers do really really well. So, so in terms of what we've done as examples is Ryan walks in and he's asked me a bunch of questions and few just come to mind immediately. The first few are, hey, you are an artificial intelligence expert. He's just threw a database of molecules, the 15,000 compounds that he's described to prioritize a few for that lab experiments. So that's question number one. And he's come back into my office and asked me about, hey, there's 30 million publications in PubMed and I don't have the time to read everything. Can you create an artificial intelligence system that once I've picked these few molecules will tell me everything about that molecule or everything about that virus, the unknown virus that shows up right. And what are some ways in which he can augment his expertise, right. And then the third question I think he described it better than I'm going to was, how can technology connect these stocks. And typically, it's not that the answer to a drug discovery problem sits in one database. Right, he probably has to think about unique product proteins has to think about pop cam for chemical informatics properties data and so forth. Then he talked about the phytochemical interaction there's probably another database. So when he's trying to answer a question, and specifically in the context of an unknown virus that showed up in late last year, we, we, the question was, hey, do we know what happened in this particular virus, compared to all previous viruses. Do we know of any substructure that was studied or a different disease that's part of this unknown virus. And can I use that information to go mind these databases to find out if these interactions can actually be used as a repurposing hook. Say this drug is known to interact with this subsequence of a known virus that also seems to be part of this new virus. Right. So to be able to connect that dot, I think the attraction that we are learning from working with some companies is that this drug discovery process is complex. It's iterative, and it's a sequence of nearly the his tax search problems. Right. And so so one day, Brian would be like hey I need to match genome I need to match putting sequences between two different viruses. And another day it would be like, you know, I need to shift to a database of potential compounds identified side effects and whatnot. Another day it could be, hey, I need to design a new molecule that never existed in the world before I figured out how to synthesize it later on, but I need to figure out I needed to complete a new molecule because of patentability reasons. So it goes through the entire spectrum. And I think where HP has demonstrated multiple times, even in the recent weeks, is that the technology infusion into drug discovery leads to several aha moments. And, and, and the aha moments typically happen the other few seconds, and not the hour states months that Ryan has deliberately worked through. And what you've learned is mama researchers love their aha moments. And it leads to a sound valid well founded hypothesis. That's all right. Absolutely. Absolutely. Yeah, at some point I would like to have a look at your peak and your the list of your hard moments. Yeah, something quite interesting in there for other industries to but we'll do it at another time. Chris, you know, with your regular in work with pharmaceutical companies, especially the big farmers right. Do you see botanicals coming being talked about more and more. Yeah, we do. Right. Looking at kind of biosimilars and drugs that are that are already really in existence is kind of an important point in Dr. Yates and Ron Gunn with your work with databases. This is something important to bring up in much of the drug discovery in today's world isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right, all the different compounds that already exist insisting through those right. Of course, data is mind and it is gold essentially right so a lot of companies don't want to share their data. A lot of those botanicals, data sets are actually open to the public to use. In many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. Hmm. Well, and Ryan that's where NCNPR hosts much of those data sets here right and and my it's interesting to me right you know you were describing the traditional way of drug discovery where you have a target and a compound right that can affect that target very very specific. But from a botanical point of view, you really say for example I have an extract from a plant that has combination of chemicals and somehow you know it affects this disease, but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the the issue that the work that has to be put in for botanicals in this area. Yes, Dr. You hit it exactly not now I can understand why highly iterative intensive and data intensive and perhaps that's why Rangan you're highly valuable here right. So, tell us about the challenge right the many to many intersection to try and find what the targets are right given these botanicals that seem to affect the disease here. What what methods do you use right in AI to help with this. Fantastic question I'm going to go a little bit deeper and speak like Ryan and terminology, but here we go. So we're going back to that starting of a conversation right so let's say we have a database of molecules on one side, and then we've got the database of potential, you know targets in a particular could be a virus could be a disease target to be identified right. Oh, just for example on the virus you can have can have a number of targets on the virus itself. Some have the spike protein some of the other proteins on the surface so they are about three different targets and others on the virus itself. So a lot of people focus on the spike protein, right but there are other targets to on that virus. Correct. That's exactly right. So, for example, the word that we did with Ryan we realized that you know COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus. Not only that overlap with MERS that overlap with some bad coronaviruses it was telling before and so forth right so knowing that, and it's actually broken down into multiple and right I'm going to steal your words non structural proteins on low proteins as proteins there's a whole soft structure that you can associate and I mean as a sequence with right so on the one hand you have different targets and again since we did the work is 160 different targets even on the COVID-19 virus right and so you're trying to match 36, 37 million molecules that are potentially synthesizable. And try to figure out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for that will inhibit the symptoms on a human body right so that's the challenge there. And so I think the techniques that we can underroll go back to how much do we know about the target and how much do we know about the molecule. Right, and if you start off a problem with I don't know anything about the molecule and I don't know anything about the target. You go with the traditional approaches of docking and molecular band and simulations and whatnot right. But then you've done so much talking for on the same database for different targets you have learned some new things about the ligands the molecules that Ryan's talking about the potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets and some of the structure and so forth to build a model that will capture that essence of all we have learned from the docking before. So that's the second level of how do we infuse artificial intelligence. The third level is to say, Okay, I can do this for a database of molecules. But then what if the protein protein interactions are all over the literature study for millions of other viruses. How do I connect the dots across different mechanisms of actions to right. And so this is where the knowledge graph component that Ryan was talking about comes in. So we put together a database of about 150 billion medical facts from literature. That is able to collect the dots and say, Okay, I'm starting with this molecule. What interactions do I know about the molecule is there a pretty pretty interaction that affects the mechanism of pathway for the symptoms that a disease is causing. And then he can go and figure out which protein and protein in the virus could potentially be working with this drug, so that inhibiting certain activities would stop that progression of the disease from happening right so, like I said, your method of options. So the options you've got is going to be how much you know about the target. How much you know about the drug database that you have and how much can you leverage from previous research as you go down this pipeline right so So in that sense, I think we mix and match different methods and you've actually found that, you know, mixing and matching different methods produces better synergies for for for for people like Ryan so Well, well the synergies I think is really important concept wrong in additivity synergistic what it however you want to catch that right, but it goes back to your initial question Dr go which is this, this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active more than one network that's impacted. Okay. Remember, I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target like an interaction to the complete the intibital side of it right where now we all were focused on is an in a single molecule interacting with a target. And so where I'm going with this is interesting enough. I sort of migrate started to migrate back toward the middle and what I mean by that right is we have these in the concept of polypharmacology, we have this idea a regulatory pathway so called fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies, taking known approved drugs and putting them in different combinations to impact different diseases. Okay, and so I think there's a really unique opportunity here for artificial intelligence or is wrong and it's taught me augmented intelligence right to give you insight into how to combine those approved drugs to come up with unique indications or is that that patent ability right getting back to how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that almost the movement back toward that complex mixture fixed drug combination, as opposed to single drug entity single target approach. I think that opens up some really neat avenues for us. So as far as the expansion the the applicability of artificial intelligences, I'd like to talk to, to briefly about one other aspect right so what wrong and I've talked about is, how do we take this concept of an active fight a chemical, and work in other words, let's say you identify a fight a chemical from an in silico screen process, right, which was done for coven 19 one of the first publications out of a group, Dr Jeremy Smith group at Oak Ridge National Lab right identified a natural product is one of the, the interesting actives, right, and so it raises the question to a botanical guys is okay, where in nature, do we find that fight a chemical. What plants do I go after to try and source botanical drugs to achieve that particular endpoint right and so what wrong and system allows us to do is to say okay, let's take this fight a chemical in this case, fight a chemical and say where else in nature is this found right that's a trivial question for an artificial intelligence system, but for a guy like me left him own devices without a I spend weeks combing the literature. Wow. So, so this is this is brilliant this I've learned something here today right. If you find a chemical that actually, you know, affects and addresses a disease. You can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. Well, well there's that and there's the other almost still wrong and thunder here right he always teach me Ryan don't forget everything we talk about has properties plants have properties chemicals have properties etc. It's really understanding those properties and using those properties to make those connections those edges those sort of interfaces right. So, yes, we can take something like an area dictate all right that example I gave before and say okay now based upon the properties area dictate all tell me other fight chemicals, other flavonoids in this case such a fight a chemical class at area is now tell me how what other fight chemicals match that profile have the same properties, it might be more economically viable right in other words, this particular fight a chemical is found in a unique Himalayan plant that will never be able to source but can we find something similar the same thing growing in, you know, a bush found all throughout the southeast region. So, so Chris on the pharmaceutical companies right are they looking at this approach of getting building drugs developing drugs. Yeah, absolutely Dr. really what Dr. Yates is talking about right it doesn't help us if we find a plant and that plant lives on one mountain only on the north side and the Himalayan, we're never going to be able to create enough of a drug to manufacture and to provide to the substance right assuming that the disease is widespread or affects a large enough portion of the population. Right so understanding, you know, not only where is that mechanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspects affects the binding site which aspects of the compound actually does the work, if you will, and then being able to to make that at scale. Yeah, if you go to these pharmaceutical companies today. Many of them look like breweries to be honest with you it's large still it's large back everybody's clean room. And it's there they're making the microbes do the work for them or they have these, you know, unique processes right. So they're not brewing beer. Although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa. So we should, we should visit one of those yeah. Right so so what's next right so we've we've you've described to us the process and and how you develop your relationship with Dr. Yates Ryan over over the years right five years was it. And culminating in today's many to many fast screening methods. Yeah, what what do you think would be the next exciting things you would do other than let letting me peek at your aha moments right what what would be what you say are the next exciting steps, you're hoping to take. Thinking long term again this is where I am working on this long term project about we don't know enough about botanicals as much as we know about the synthetic molecules right and so this is a story that's inspired from Simon Sinek's infinite game book trying to figure out if human population has to survive for a long time, which we've done so far with natural products we're going to need natural products right. So what can we do to help organizations like NC and PR to stage genomes of natural products to stage and understand the evolution as we go through understand the evolution to map the drugs and so forth. So the vision is huge right so it's not it's not something that you're going to do on a one off project and go away, but in the process just like you're learning today to go I'm going to be learning quite a bit having fun with Ryan Ryan what do you think. Ryan we're learning from you. So my paternal grandfather lived to be 104 years of age. I've got a few years to get there but back to the infinite game concept that that wrong and mentioned he and I discussed that quite frequently. I'd like to throw out a vision for you that's this well beyond that sort of time horizon that that we have as humans right and that's this right is our current strategy and it's understandable is really treatment centric. In other words we have a disease we develop a treatment for that disease, but we all recognize whether your health care practitioner whether you're a scientist, whether you're a business person, right, or whatever occupation you realize that prevention right the old ounce prevention worth a pound of cure right is how can we use something like artificial intelligence to develop preventive sorts of strategies that we are able to predict with time right that's why we don't have preventive treatment approach right we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old, well, we can't do that on a time scale. That is reasonable. Okay, and then the other part of that is why focus on botanicals is because for the most part and there are exceptions I want to be very clear I don't want to paint the picture that botanicals are all safe you should just take botanicals dietary supplements and you'll be safe right there are exceptions, but for the part botanicals natural products are in fact safe and have undergone testing human testing for thousands of years right. So how do we connect those dots a preventive strategy with existing extent botanicals to really develop a health care system that becomes preventive as opposed to treatment centric if I could wave a magic wand that's the vision that I would figure out how we can achieve right and I do think with guys like rongan and Chris and folks like yourself, England that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age but you never know right. Two really good points there, Ryan, it's really a systems approach, right understanding that things aren't just linear, right and as you go through that there's no impact anything else right, taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream really we've been discussing the drug discovery process a lot, and kind of the kind of pre clinical and vitro studies and in vivo models. But once you get to the clinical trial, there are many drugs that just fail just failed miserably, and the botanicals right known to be safe right and many instances you can have a much higher success rate. And that would be really interesting to see, you know, more of these growing in the market. Well, these are very visionary statements from each of you, especially Dr. Yates right prevention better than cure right being proactive, better than being reactive, reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much right. This has been a brilliant panel with brilliant panelist, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah, offer the future of drug discovery that includes botanicals. Thank you very much. Thank you.