Lot of the enthusiasm, which I think is also of note, has been around the concept of the HYFT and the uniqueness of the HYFTs, and as a distinguished and differentiator from our competitors. In fact, the two larger pharma companies that that we’ve been working with have gone into a pretty significant due diligence on that and really wanting to understand, how the HYFTs really drive that differentiation. And has garnered quite a bit of excitement around how the HYFTs drive the algorithm based exploration and their potential to be able to identify these relationships that the traditional AI and ML supported codes being used by other companies don’t have. And so I think that’s actually been where quite a bit of the interest and differentiation has come in, is around the uniqueness of the way that we have a real true discovery backing, the development of these codes and the fact that for them, that’s a concept of a HYFT and how that HYFT relates to what we understand about evolutionary biology is very tangible and very directly related to their experience in biology.
Let’s see. Will, I apologize I don’t know, if I answered all of your questions, so please
Will McHale: No, no. That’s super helpful. I guess then I also wanted to ask, just sort of looking back over the course of 2022 multiple members of the management team have bought stock in the open market and pretty consistently throughout the year. So I was curious to just hear from you guys what most excites you about the IPA investment case going forward.
Jennifer Bath: Yes, absolutely. So we’re, yes, you know that we are really excited and part of it really is because all along will we have had this really a magnificent vision for IPA. Our strategic goals every fiscal year have been aligned toward that vision. Many of the milestones that we have accomplished in the past couple of years were built specifically toward our vision of fully integrating digital €“ of being, I’m sorry, a fully integrated digital technology company that’s able to solve the most complex personalized medicine challenges. And I think also importantly on an unprecedented timeline and budget. And so, for years we have emphasized the increase in our discovery program throughput, and then accordingly also, the optimization of antibody sequence outputs from our lab, standardizing those workflows across all of our locations.
We focused on high-throughput NGS or next generation sequencing capabilities, but more specifically, we built a digital pipeline to mine large multi-species sequences and structures with amplified functional diversity. And then another, milestone we had built into that was, we coded algorithms to, and this is outside of BioStrand. We coded algorithms to augment our ability to mine and formalize and perform deep sequence analysis while building a repository for all of the data. So our ultimate aim was to apply this information to the right tool when that tool was identified with the ability to continuously learn from an AI or ML perspective, and then to apply the knowledge gained from these collection processes. And that tool is now our LENSai tool that we acquired through BioStrand.
So going back more specifically to your question, during the past three to four months, our excitement is definitely possible in particular amongst the, the management team because we’re starting to see very real world examples of how we’re able to process and interpret these informative models to solve real and complicated life science problems. And not only do we see our vision now starting to come to fulfillment, but it’s also being accomplished on a timeframe that accelerates some of our more lofty ambitions around the de novo and silico discovery and development, which is actually the foundation of the application supported by this BriaCell deal that we’ve been discussing, and then some other similar discussions. So in essence, really we see strong concrete signs that our vision is rapidly unfolding, that the pieces of the puzzle that we’ve been strategically constructing over the past couple of years are coming together, and the potential that €“ it generates is at the root of our enthusiasm and our excitement.
So I think going forward, we’re most excited to demonstrate the power of our algorithms to solve these challenges for our partners. And then of course along with the anticipated revenue generation that could result from these accomplishments.
Will McHale: Great. It seems like the scientific achievements are really on the verge of producing some pretty exciting financial performance as well. So thanks for that. I think everything else I had has been hit on, so I will drop. Thanks.
Jennifer Bath: Okay. Thank you, Will.
Operator: And at this time there appear to be no further questions. Dr. Bath, would you like to take over for closing remarks?
