How AI Is Accelerating Drug Discovery | Smbat Rafayelyan (Bioneex CEO)

AI in Drug Discovery | #aidrugdiscovery #biotechinnovation #medicalinnovation

Amid a rapidly changing biotech landscape, AI is transforming how we discover and develop new medicines. Please visit our website to get more information: https://swangroup.net/ 

In this episode, I sit down with Smbat Rafayelyan, founder and CEO of Bioneex, a platform that connects early-stage biotech innovators with investors and pharma companies using AI-driven insights. He shares his journey from big pharma to entrepreneurship and how his team is reshaping drug discovery.

We explore how personalized large language models are being applied in biotech, the role of data integration in connecting biotech, pharma, and investors, and why China’s biotech ecosystem is fueling a surge of innovation.

✅ How personalized AI models improve drug discovery and evaluation
✅ The role of data integration in connecting biotech, pharma, and investors
✅ Global opportunities, including China’s emerging biotech sector

If you’ve ever wondered how AI is making sense of scientific data chaos, this episode is a must-watch.

Links from this episode:
✅ Get to know more about Smbat Rafayelyan: https://www.linkedin.com/in/dr-smbat-rafayelyan/?originalSubdomain=de 
✅ Learn more about Bioneex: https://bioneex.com

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https://www.youtube.com/@Biotech_Bytes/?sub_confirmation=1 

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#aidrugdiscovery #biotechinnovation #pharmainsights #aiinhealthcare #futureofmedicine #medicalinnovation

How AI Is Accelerating Drug Discovery | Smbat Rafayelyan (Bioneex CEO)

Chapters

• 0:00 Introduction
• 2:58 The gap that led to creating Bioneex
• 8:34 Using AI and large language models in biotech
• 15:06 Why super-personalized LLMs outperform general models
• 18:57 The growing influence of Chinese biotech innovation
• 22:48 Who uses Bioneex and how the platform works
• 26:52 A case study: Stanford biotech success story
• 30:46 Partnering with AI-driven drug prediction firms

Transcript

Steve Swan [00:00:00]:
Up next, join us for an exciting conversation with Simba Rafayelyan, founder and CEO of Bioneex. Bioneex is a platform which connects drug discovery with potential future partners and financial investors by way of AI and multiple data sources. Join me next. Hello and welcome to Biotech Bytes. I'm your host, Steve Swan. And today I have the pleasure being joined by Sinbad Rafaelian, the founder and CEO of Bioneex. Sinbad, welcome. Thanks for joining us.

Smbat Rafayelyan [00:00:40]:
Hi Steve, thank you. Thank you for hosting me today in your show.

Steve Swan [00:00:44]:
Sure thing. And we're going to discuss Sinbad's new platform that he founded. And as a reminder to all our listeners, if you enjoy what you see here today, don't forget to like us and follow us on Apple, Spotify, YouTube. Right, great. So Simba, tell me a little bit about. We'll get into your platform in a minute, Bioneex, but give me a little bit of your background to get our listeners familiar with you and who you are and how you got to today. Got to where you are today.

Smbat Rafayelyan [00:01:16]:
Yeah, absolutely. Steve, thank you very much again for hosting me today in your show. It's a great pleasure to talk to your audience and tell about myself and our platform particularly. So I'm coming actually from big pharma background, but I did my MD and PhD from my research times. I was already working with one of the blockbuster drugs of Pfizer, Etanrecept. Then after research I moved into industry. I was in Xenofi in oncology, worked with drug it calls absolute Beretsev or metastatic colorectal cancer space. Then after working a while in Zanofa, I worked for Pfizer.

Smbat Rafayelyan [00:02:01]:
Then I worked for iqvia. As a lot of people in our industry knows, it's a big company. I was there in their biotech helping to find the right partners, investors and optimize and prioritize their R and D pipeline. And I also was In Private Equity, MNA's pharma service provider focus at all. What I was doing, moving from science to business before started with bionics.

Steve Swan [00:02:30]:
Got it. Cool. So now Bioneex is a platform. Right. And it's going to bring, you know, really help investors to find, I think. Right. Help find some biotech ideas. But first, before we get into that, what gap did you see in the marketplace that you said I need to create this company?

Smbat Rafayelyan [00:02:50]:
Yeah, great question, Steve. Thank you. When I was actually in Sanofi, I was in R and D one week and I talked to scientists. They're really doing early stage science and I Saw a lot of interesting discoveries which in pharma they would like to call programs. And then I saw some of the programs were like marked red. And then I asked what's happening guys? What is this? They said these are actually programs that basically the assets in early stage that management decided do not move forward. And then I said why? They said just because there is always budget scarcity, you can not develop every asset you have in the pipeline. You have to at the end of the day decide what to do and which one move forward which calls in our industry optimization of your pipeline and prioritization.

Smbat Rafayelyan [00:03:51]:
And then I was like curious what happens? What does they actually end up like not moving forward in the pipeline in the drug development? They told me at that time it was more than 10 years ago, I think it was 12 years ago, something like that. They said it end up in shelf, pretty much nowadays we call it shelf trucks. And then I thought that's interesting. We invested so much, I mean invested so much money the company in these assets. It might be interesting to build a platform where you can put it and then other companies will take it and then develop further. So I started to discover this idea a lot. But then I came across one article published, I believe it was Forbes from former R and D head of Pfizer, John Lamartina. So I reached out to John and I talked to him.

Smbat Rafayelyan [00:04:51]:
So he was very kind to answer all my questions. And then he told me that it's not really so much value into these shelf assets, although they are interesting, but mostly the discovery of novel assets, it comes from early stage biotechs. And then when you look into the data you see like in fact the big pharma companies and mid sized companies are not really contributing to the global novel drug discovery and development. It comes from the early stage biotechs. And then we thought we need to build a platform where we bring particularly early stage innovative drug development companies to put their assets on our platform and then help on the other side, the mid size and larger biopharmas and biotech focused venture capital firms to search and evaluate these assets on our platform and directly connect with the companies.

Steve Swan [00:05:51]:
That's very cool. So, so, so the assets that you, I'm, I'm not asking you how, but somehow you get in touch with the organizations that have decided to, for lack of a better term, leave them, right? Abandon them I guess because there's, they see too long of a Runway I assume for them to make some quick money on it. But they've, but the early discovery has already Been done. So the, the, the molecule or whatever if you will is there. They just didn't. They decided for whatever reason not to continue on after they've discovered it.

Smbat Rafayelyan [00:06:21]:
Yeah, so that's the case of so called discontinued assets, what you are describing. Basically they decided to discontinue. It could be like a strategic reason again, budget scarcity. It could be like the clinical trial has failed and some other company will take it and then run a new clinical trial. And nowadays it's becoming very trendy with AI in the industry where companies with their AI powered technology which they call platform can run like design better clinical trials for the failed assets and run it again. It could be like finding like a more specific patient population with a specific gene mutation that the drug is more responsive or any other way having more human and disease data and biological data behind that. But what is actually very interesting that those companies, early stage biotechs, their business model is not really bring a drug to the market because bringing a drug to the market, I work in big pharma, it's quite difficult, it's quite heavy lifting. You need to have huge marketing and commercialization market access like army and power across many countries.

Smbat Rafayelyan [00:07:44]:
And the biotech business model is pretty much discovered, develops and outlicense or partner co development partnerships with comparably bigger companies, mid sized pharma, bigger biopharmas. And then in that ecosystem of course also comes into play the biotech focused venture capital firms to provide a capital in the research and development of the drugs.

Steve Swan [00:08:09]:
Very cool. Well, so let me go back to something you mentioned, AI real quickly. You know, and in our last conversation you talked a lot about that. Right. So with your platform you're using, you know, large language models and AI. Can you expand on that a little bit for, for our listeners, for our audience?

Smbat Rafayelyan [00:08:26]:
Yeah, absolutely Steve. It's amazing like opportunity that tech companies in our space really leveraging a lot. So what's happening, you know, the AI is actually itself the large language model which everyone is almost familiar with. Chat gtp these are not really databases. What they are, they are reasoning and pattern recognition tools. That means you have to have the right database in order to use this tool, this smart tool to go into the unstructured data and find, find like patterns, find what you look for and then bring it to you in a way you like. So what we do, we have three main type of data. We leverage the number one, it's our marketplace data, a proprietary marketplace data that comes directly from drug development companies on our platform.

Smbat Rafayelyan [00:09:26]:
That means that's the most high quality data from primary source. Then we also generate in our platform internal intelligence coming from the user behavior. And it comes also like who is looking what. For example, our platform helps to understand what is actually hot at the moment in the industry, whether it's a small molecules, biologics, gene therapies, this disease area or other modalities, mechanism of actions, et cetera. And then on plus of our proprietary marketplace data, we also connect to the major databases such as patent databases, clinical trial databases, scientific publications and regulatory filings. I'll give you example. Everyone knows PubMed, it's a scientific publication. It's however in the US or clinicaltrials.gov right? Or Edgar database for like filings, like regrettable filings.

Smbat Rafayelyan [00:10:26]:
But there are other major regions. Nowadays everyone talks in our industry, biotech industry, China. So what are these databases that you have to connect to really access to the information. That's what our platform also does. It connects to Chinese, Japanese, South Korean and other major regions as well. And it translates into English. So there is another layer in our platform, nlp, natural language processing, which translate into English. And also it brings into one scientific common language because different databases using different scientific terms about the mechanism of action or I don't know any drug classes, et cetera.

Smbat Rafayelyan [00:11:07]:
And it translates into one language. So that's the secondary package of the database on our platform that LLM goes and looks into. And then it comes the first database, it comes directly from that specific client we work with. So for example, we work now with a major biopharma company. They have huge internal data typically coming from their search and evaluation activities, business development, licensing activities, or from their due diligence or from their research and development R and D. So they bring on top of our data, global patent scientific data, also their internal data. But it's only accessible and usable for them. No one has access to the data, so no one can go into that data.

Smbat Rafayelyan [00:11:55]:
And then what we do, we have this large language model which is then underlying reasoning and pattern recognition tool that goes in, read this type of buckets data and then try to find really what you're looking for. Whether you look for novel target or you have a novel target, you look for an asset to apply to the target. So I can talk about LLM more, but I'll stop here and be happy to. Yeah, hear. What would be your next question?

Steve Swan [00:12:28]:
Well, so. So what you're saying is the investor goes in, right? And does this search, right? Yes, correct. And the LLM and AI is working on their behalf to curate and put all this data together and then feed it back to them, you know, based on all the different parameters that you just mentioned there. And I'm sure that they have many different search criteria. Right. That they can put in store for in there. Okay, okay. Now you seem real excited about the LLM and the AI.

Steve Swan [00:12:57]:
Is there something deeper that I'm missing there and not understanding? Like is there more to it than what you just described?

Smbat Rafayelyan [00:13:03]:
Yeah, absolutely. So I want to tell you that, I mean LLM is quite new, but if you go and talk more people already built some expertise in that space, most of them will tell you that by applying generative AI like GPT5, for example, in specific industry domains, in 95% cases, it fails. The reason why it fails because these LLMs is in fact general AIs. That means there is a ton of bunch of like a site data which is not relevant for your specific domain. For example, we are in a search and evaluation of therapeutic assets. It is very, very specific area. Right? But then if you have in this LLM data, I don't know, coming from football, coming from anything else in the world, then in that case they call it it hallucinating. So it gives you something that is not really relevant.

Smbat Rafayelyan [00:14:02]:
And that's one of the reason that some of the big pharmaceutical companies now took a step back, not using this general, General LLMs. For example, Pfizer is not using. We talking with Pfizer we know because they started this early days and then they saw it's not really giving good results. So what our solution is, it's highly personalized, so called super personalized LLM where typically what you do, you took like an, like a mediocre LLM e.g. gPT4 or some level of Gemini. You know, it's quite good, but it doesn't have so much data yet inside like GPT5. And then you train it on your local GPUs. As everyone talks about Nvidia, it becomes really hot.

Smbat Rafayelyan [00:14:58]:
So Nvidia is one of the biggest producer of GPUs. So companies like ours, we build this in house infrastructure with GPUs and we're continuing to do this. So then we can take and train the LLM and create it like really, really super personalized, tailored to that client's specific needs. And in that case the outcome, the result is better. And then after it's trained, you can deploy it either on your cloud, like on our cloud as a service provider or some big pharmaceutical companies. They have high security like clearance, they are required to deploy on their own clouds. So that's the difference. Super personalized LLM using trained on specific data sets.

Steve Swan [00:15:48]:
So let's say I get one of these super personalized LLMs and I'm XYZ company right now it's inside my firewall. It's gotta keep. Don't we have to keep tweaking that and teaching it and it has to keep learning or is it learning on the data that I'm pushing through it at that point?

Smbat Rafayelyan [00:16:01]:
So yeah, it keeps learning. First it learns from your usage. So every time when you use adapts to your requirements, to your needs. It's imagine like you hired some PhD guy after Harvard graduation and getting a PhD degree but this guy never worked in the industry. So it's really learning everything fast. Whatever you tell every, every day that you tell give these tasks, it does better job the next day. So that's one of the things they learn and adapt. And then of course continuously feeding with the data and training.

Smbat Rafayelyan [00:16:42]:
That's what our job is, working with the client very closely. So it's not like we deployed, we customize and then we go away. But really like client provides constantly new data. We continue to, we call it fine tuning. So we do that constant fine tuning of that LLM and it's pretty much like imagine you have a nice suit, like you're growing, right? So you are a child, you're growing and the suit constantly getting like tailored to your body all the time. That's the, you know the, the beauty of, and the magic of the, the, the super personalized LLM.

Steve Swan [00:17:25]:
But once I implement the super personalized LLM and I put it inside behind my firewall, you're out of the picture. Or do you get involved still?

Smbat Rafayelyan [00:17:33]:
No, we get still involved. We are there, we are there. We will be always like continuously training this. Yeah, it's like how you do this Typically in a software world nowadays you do the work and then you deploy it in a cloud. You do the work and deploy. So every time when needs to be again fine tuned, we do it in our local GPUs and then again it will be deployed and updated. So the software constantly gets updated the same way like people talk about Tesla, right. So it's not a car company as they like to tell.

Smbat Rafayelyan [00:18:11]:
It's a software company. That's the thing. Right. So software. Exactly. That gets updated all the time. That's not how the AI works. It should be all the time like getting updated, right?

Steve Swan [00:18:25]:
Absolutely. So now you had mentioned to me, you know, you mentioned you know Chinese a minute ago and that was something that you, you know, we're getting pretty excited about in the past. Can you explain, you know, what's going on there? Is there something in particular we should know about from, from, from your perspective as far as you know, rolling out your product to some Chinese firms?

Smbat Rafayelyan [00:18:49]:
Yeah. So thank you, Steve. Absolutely. That's a great point. And in the industry nowadays as data is also showing, there is a significant jump in Chinese biotech space. A lot of like a public funding and now American companies are hunting, we work with large biotech companies, large pharmas, they all are hunting for Chinese innovation. And one of the strength what we have, it's our in person capabilities. We have on our company board former president of Pfizer China who spent a lot of time in his life in China and you can imagine the understanding and network he has.

Smbat Rafayelyan [00:19:37]:
So he helped us a lot to open a lot of doors in a Chinese biotech space. Plus we actually did from early days quite good already relationship building with Chinese biotech landscape. And we now have that access plus of course the Chinese databases which is in their local language. And we always like try to understand better and better what are these public databases that we can connect and identify the novel IPs in China space. So then we go using our network quickly connecting with this company. So if anyone is interested to work to identify Chinese landscape, we can do great job there for both technology perspectives as well as from the business development and human relationship we have built.

Steve Swan [00:20:28]:
The question I would have right about the Chinese is, you know, the data, right? Is it coming from them or is it public? Like meaning is it coming specifically from the company or is it already out there public information that you're pulling?

Smbat Rafayelyan [00:20:41]:
So we have companies on our platform, Chinese companies, they put already their data on our platform and then of course the public domain that comes from their scientific publications because they had their own PubMed, they have their own IP database, they have their own clinical trials database. So we also leverage that public data to learn more about the landscape.

Steve Swan [00:21:03]:
Got it, got it. So now talking about landscape, you, you, you went right into where I was going to go next. In the space that you're in, are there a lot of competitors and if so, you know, how, how many are in your space or, or, or is this novel you the first one doing this?

Smbat Rafayelyan [00:21:20]:
Yeah. So you know, because the AI is so new, there are not so many companies in our specific domain. They are doing it in terms of the marketplace concept. There are some companies doing this, but we are the Most advanced one. But in terms of applying a personalized LLM on our marketplace data plus public data and the client data, we are in that case in our knowledge unique. There is only one company we know so far that they do pretty much the same for asset scouting and due diligence in the industry. However, they do not have a marketplace data. They do not have their own proprietary marketplace data.

Smbat Rafayelyan [00:22:07]:
What they do, they pretty much take the public data and then client data, which is in a way everyone who has the AI knowledge and some experience in the industry can try to do. But to have these three main type of data sets, your own proprietary data, public data and client data, and applying personalized LLM as an underlying reasoning and pattern recognition tool. In our knowledge we are the only one at the moment in the industry.

Steve Swan [00:22:40]:
Very cool. And obviously you've run into with the personalized data. Right? With my own company's personalized data you've run into every single security and privacy question you could think of and I'm sure you absolutely overcame every single one of those from a technology perspective. So I don't even need to get into that. Right.

Smbat Rafayelyan [00:23:01]:
Yeah. So we do have actually very good, like a technical team, very strong, experienced CTO who addressed all these questions and actually we have now very good experience working a large Australian based biotech company where pretty much their requirements are connecting through API to their in house internal AI like search and evaluation and due diligence tool. Because there are a few companies in our industry, they already start to build their in house and pretty much they want to use through one single interface. They don't want to come and login into our platform but they through API can connect to our platform. That's also what we provide and of course it's all in a secure environment with all the requirements that big companies as we know in our industry will need it.

Steve Swan [00:23:59]:
Got it. So I guess. So the main customers for you would be the investors, right? The PE firms and the large pharma companies that would want to buy assets or programs or whatever you want from the smaller research based companies. Correct.

Smbat Rafayelyan [00:24:16]:
So the typical customers are in fact as big pharma companies and comparably like mid sized companies, they actively looking to assets in license to do the due diligence work. However there are also smaller biotechs, I would say, okay, funded, not super well funded but still funded biotechs, they do have their in house asset. They're looking to out license what they need to do. They need to do like a competitive analysis to understand like the competitive Landscape and that's another reason why they use our platform. They're not particularly looking like twin license but they are interested to see what is out there and then really use the AI to understand where the assets stand compared to those they are in that specific, very specific domain. And then of course you talk about the investors actually our clients are not the PEs. Most of the cases are VCs. The venture capital because PEs are goes more in a late stage investment whereas in our industry it's still like a biotech consider as a risk risk investment.

Smbat Rafayelyan [00:25:29]:
This is more VC game.

Steve Swan [00:25:32]:
Got it. Cool. That's good. That's good stuff. So I don't know what other questions I really have of this. Is there anything more from you know from your platform or your customer base or you know where you've come from and where you're heading to that I think that you think you know my listeners would want to hear about.

Smbat Rafayelyan [00:25:54]:
Yeah, thank you Steve. Yeah, I want to talk about the recent like study that we did a press release a case study that's really interesting. You asked about the customer base. In fact this is a an emerging biotech coming from Stanford. It's a very prominent professor in neurology. She identified a novel target for mega neurodegenerative diseases. And then for applying to the novel target they were their team were looking an asset basically a drug candidate that they can apply that target they have identified. So they use our platform, our AI capabilities and they identified quite quickly an asset for that for that novel target.

Smbat Rafayelyan [00:26:44]:
And now they are in a late stage discussion for an in licensing. This is really a success story that I can proudly talk about how AI can help a company our our tool very quickly to identify an asset that you really looking for a specific target. That's yeah, everyone who is interested the personal is out there can read about that now.

Steve Swan [00:27:11]:
So do you guys charge a monthly fee for what you do or do you get involved with the actual deal?

Smbat Rafayelyan [00:27:19]:
Yeah, so it depends from which side it is for the those guys the major biopharmas and VCs they're looking assets to like invest or to in license. We work with them on contractor base. Basically we have long term contract where firstly deploy it the platform fully customized. We do pilot setup for them. They can test up to three months because it's something they really need to test give us feedback and then we tune it our like human activity plus our technology and then constant work during three months and if they really satisfied it meets all their success criteria. We go into like A long term contract, typically it's a three year contract. This is how we work with biopharmas and VCs. But for biotechs who are actually out there looking to partner their asset, to out license their asset, we have an opportunity now on our platform where they can put their information and if that crystallize into a deal someone in license their assets from our platform then they only need to pay us a success fee.

Smbat Rafayelyan [00:28:38]:
In that case there is no, any subscription fee, no upfront payment fee for the early stage biotechs which puts us in a huge like in a step ahead in a, in a competitive world. Because nowadays you know the, the industry is really in a tough situation. A lot of biotechs out there looking for capital and there are a lot of investment bankers, consultants charging like a retainer upfront fee and then trying to find investors or strategic partners for them. And oftentimes biotechs, you know they cannot afford it. Every dollar they counting. So working with us they don't need to pay any upfront fee, any retainer. It's only success fee based now on success fee.

Steve Swan [00:29:24]:
When it's done, when the deal gets done, you get some of it and that's you know.

Smbat Rafayelyan [00:29:29]:
Exactly.

Steve Swan [00:29:30]:
Yeah, it's already, it's already, money's already changing hands. It's not like, it's like you said nothing up front. So that's good.

Smbat Rafayelyan [00:29:36]:
Cool.

Steve Swan [00:29:37]:
Good. Awesome. Like that. Okay. All right.

Smbat Rafayelyan [00:29:40]:
I want to highlight once again another part of AI collaboration we just built. It's a company based in California, GATC Health. Really awesome team, a very experienced team. It fits into the concept of our platform. Search, evaluation and due diligence. What they provide, they provide a drug prediction, risk prediction which you know it's a huge topic for investor. Right. So as we know early stage assets in pre clinical stage they have a like very high failure rate, 80 to 90% failure rate where they pretty much this, they can reduce this risk up to 10 to 20% through their huge like data, biological data, disease data, et cetera to predict whether this preclinical asset can succeed in a clinical stage or not.

Smbat Rafayelyan [00:30:38]:
So we are the first platform in the world that offers that risk predicted assets pretty much really it gives a huge advantage to investors and strategics and if anyone interested we can yeah entertain and talk about that more. I'm happy to also yeah. Connect with jtc.

Steve Swan [00:31:02]:
That's really cool. I like that, that's nice. I'm sure folks will like that and they'll want to see that. Right. So good, good Good.

Smbat Rafayelyan [00:31:09]:
Okay.

Steve Swan [00:31:10]:
Well, thank you very much for your time.

Smbat Rafayelyan [00:31:12]:
Thank you, Steve.

Steve Swan [00:31:13]:
Great.

Smbat Rafayelyan [00:31:13]:
Thank you.

Steve Swan [00:31:14]:
I do have one final question that I ask of folks and has nothing to do with, with what we just talked about. But before I do that, just want to make sure that you know that there's nothing else you want to hit on about your company, about your market space.

Smbat Rafayelyan [00:31:28]:
Yeah, I think I covered everything. I want to say the only thing, why pharmaceutical big pharma biopharma companies should work with us, because it takes a lot of like, capital in house to build such a AI power search and evaluation platform. One of the reasons, simple reason, that, you know, people working in big companies, they are not really entrepreneur in a way that they can implement things quickly and it's something very new and we are very experienced. We do it faster and far more cheaper. And I can tell you one of our current client, they were working nine months and they didn't even have a minimum viable product to do a demo where we jumped in and within just two months, we build and we deployed and we give them like a really great opportunity.

Steve Swan [00:32:29]:
I talked to my podcast. I have all these CIOs on from the small and large biotechs, right? Technology is only part of it. They, they, you can, you can create the mousetrap, right? You can create the technology, but if you don't have the data, you're in trouble. You solve the data problem too. You got three different sources of data coming into.

Smbat Rafayelyan [00:32:47]:
Yes, exactly.

Steve Swan [00:32:49]:
That's huge. I mean, that's, that's where the rubber meets the road. I mean, I can build whatever I want. I can build the Ferrari, I can build the Camaro, whatever I'm building, right? But if I don't have anything to put through the, you know, the, the engine, I don't have any gasoline. It's going to sit. It's going to look great. It's going to sit there and not do anything for me, right? So you solve that problem too, which is awesome, you know, so, so my final question that I ask of every, every guest and if you watch any of my podcasts all the way to the end, you would have seen this, but maybe you haven't. I'm a mu.

Steve Swan [00:33:21]:
I'm a fan of music, especially live music. So I go to see concerts and things like that. So what I like to ask folks is from, if, if they've gone to concerts, which I don't know that most folks haven't gone to concerts, what would you say in your lifetime was your favorite concert or live band that you've ever seen. If, if you've seen some. Any thoughts there?

Smbat Rafayelyan [00:33:45]:
Yeah, yeah, yeah. Very interesting question.

Steve Swan [00:33:49]:
Right?

Smbat Rafayelyan [00:33:52]:
Yeah, I can, I can tell you like, I like all types of music. I love music from 80s, I'm a big fan of Elvis, I like 90s, big fan of Michael Jackson and Tina Turn. But I also living in Berlin, as everyone knows, Berlin is the hub of the techno music. It's the best techno music. So I have been in some like, like clubs in Berlin, live techno music created like really world famous DJs. And I can tell you that was, that's one of the best. And then another best music that I enjoyed most very randomly. Two years ago I was in New York, which is by the way after Berlin, my next favorite city.

Smbat Rafayelyan [00:34:45]:
I love New York. It's such amazing culture and vibe. And I was walking in Brooklyn and came across during the day an amazing techno music. As I appreciate that as art. That was probably one of the best I enjoy so far. Techno music created by a New York dj.

Steve Swan [00:35:06]:
Yeah, yeah. I both my, my daughters, whenever they travel, whenever they go anywhere, they, they, they were in Japan earlier this year. They were in Spain two years ago and they go to the, the electronic EDM concerts. You know, they love the stuff, you know, on New York City. They live in New York City, so they go to those things and they just love them, love them, love them, you know. And you know, for me, I don't have a discerning ear for that. Right. You know, but all the different DJs do all this different stuff and they go and see some of these famous DJs, you know, the, the big ones and I would know who they are.

Steve Swan [00:35:39]:
But yeah, they, they love it and they, they record it, they take some pictures and show me. I'm like, oh, that's cool, you know. Yeah, it seems pretty fun.

Smbat Rafayelyan [00:35:46]:
Yeah, yeah, yeah. I mean if you work with the technology and the generation of technology, you also appreciate the music generated, created by technology plus human. That's what I think. Always from professionals. Also from the personal perspectives that what makes us nowadays really powerful and interesting, the technology plus human factor, whether it's in biotech industry or in music industry.

Steve Swan [00:36:21]:
Right, agreed. Yeah, exactly. Yeah. The, the intersection of humans and technology, that's what this is all about. Right? So, yeah. Well, listen, yeah, I appreciate your time, Simba. This was great.

Smbat Rafayelyan [00:36:34]:
Thank you. Thank you, Steve.

Steve Swan [00:36:36]:
And, and we'll have your contact information on here. So if anybody wants to reach out to you and your website Bioneex, they can, they can reach out to you there as well.

Smbat Rafayelyan [00:36:45]:
Thank you. Thank you, Steve and you, you do great shows. I, I want to tell everyone, you know, to, to watch Steve's interviews and if anyone is interested to talk about their stuff, reach out to Steve. Really, really exciting interviews and shows that Steve doing. Thank you very much.

Steve Swan [00:37:07]:
Well, I try and hit every angle I can. Right. When. When you and I first spoke, I thought that it was going to be a great thing because it's a whole new approach to something, to a problem that's out there. So it's all good. So I appreciate your time. Thank you very much.