Full Year 2023 Exscientia PLC Earnings Call
Hello, everyone. My name is Andre and I will be your conference operator today at this time I would like to welcome everyone to accentuate his business update call for the full year ended 2023.
All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you'd like to ask a question. During this time simply press the star key followed by the number one on your telephone keypad. If you would like to withdraw your question Press Star one again.
At this time I would like to introduce Chin, Okay Cool associate director of strategy and Investor Relations Chen you may begin.
Thank you operator.
Thank you operator, and welcome everyone takes Cynthia as full year 2023 financial results and business update conference call. The.
Press release, and 20-F shoot this morning, with our full year 2023 financial results and business update.
These documents can be found on our website at investors <unk> AI along with the presentation for today's webcast.
Before we begin I'd like to remind you that we may make forward looking statements on alcohol.
These may include statements about our projected growth revenue business models, preclinical and clinical results and business performance.
Actual results may differ materially from those indicated by these statements.
Unless required by law center does not undertake any obligation to update these statements regarding the future or to confirm these statements in relation to actual results.
On today's call I'm joined by Dave Hallett Inter.
Interim Chief Executive Officer, and Chief Scientific Officer.
And Ben Taylor, Chief Financial Officer, and Chief Strategy Officer.
So Mike <unk>, Chief Medical Officer will also be available for the Q&A session.
David Hallett: And with that I will now turn the call over to Dave.
Thank you Tim.
In 2023, we continue to make progress in expanding and integrating our technological capabilities.
Focus our internal pipeline efforts on the highest value oncology targets and steadily advance multiple new and existing programs without major pharma partners.
All of these elements position us for success as we work towards our goal of designing and developing better drugs faster.
Internally, we are progressing multiple highly differentiated small molecules for oncology, including GTA exa, six seven or $6 seven a CDK <unk> inhibitor currently in phase one two studies as well as our LSD one inhibitor exs seven full 539.
<unk> <unk> hundred nine.
Enabling studies.
Without partnerships, we're progressing multiple programs across an array of disease areas.
David Hallett: Recently announced an expansion of our collaboration with Sanofi to include a new discovery stage extensive originated program and achieved the first milestone from this partnership.
More broadly on in line with our business development strategy, we inked a new partnership with Merck <unk> Darmstadt, Germany.
David Hallett: This partnership, which we signed in September Leverages opposition design capabilities to design and discover novel small molecule drug candidates, but challenging oncology and immunology targets.
We have also significantly expanded our technological capabilities with the opening of our automation suite near Oxford and the launch of additional functional precision Medicine studies.
We're currently in the process of fully operationalized, our new automation facility unwanted has reached peak performance will be comprehensively integrating AI design capabilities, but it also makes it experimentation.
David Hallett: From this we expect to achieve material gains in speed efficiency and quality across our entire trip design and discovery value chain.
The launch of our functional precision medicine studies, <unk> and X sight to have the potential to support our pipeline with a greater use of human tumor samples and the preclinical development of new drug candidates and translational research.
We remain well capitalized with $463 million in cash at the end of the year, providing us with a runway well into 2026 to continue progressing our pipeline and investing in our long term growth.
Through all these updates on developments the mission of the company remains unchanged we.
We are focused on improving the lives of patients by creating highly differentiated medicines that saw significant unmet needs.
We have repeatedly demonstrated our ability to resolve previously unsolved design problems by efficiently translating scientific concepts into precision designed therapeutic candidates.
David Hallett: This can be seen in our pipeline and.
In addition to 617% and 539 that I have already mentioned also highlights key access for 318 peak ACC inhibitor.
4318 was previously in licensed by upon at Bristol Myers Squibb and is currently in phase one trials.
<unk> is a high value immunology targets has been extraordinary challenging for others to deliver small molecules against <unk>.
However, we're able to design a highly selective compounds with a low predicted human dose and retained significant economics for this program.
Our differentiated moat won't program precision designed with selectivity <unk> also continues to progress through IND, enabling studies and we look forward to providing an update for this program in the first half of this year.
David Hallett: In addition to this we also have multiple discovery programs ongoing.
Each of these molecules was designed by leveraging our industry, leading technology platform shapes with over a decade of experiments in the tech enabled drove design space.
We look forward to updating you throughout 2024 about these programs as they mature into the development phases.
Our platform was built with our overarching philosophy in mind.
<unk> discovery as a learning problem not a screening problem.
David Hallett: We use both proprietary as well as public datasets to drive information gain from target through the clinic.
Operating on the interface between technology and human ingenuity, we have designed our platform to overcome the key problems seen in drug design.
When starting a program it's important to confirm that the target has a strong association with the disease.
We used multi omics data from a patient tissue platform and other sources to find the link between target and disease.
Our technology stack that enables our tog analyst to properly unofficially interrogates target before we proceed to the experimental validation and trial design.
Our pipeline is a physical demonstration of our ability to design well balanced molecules.
A potential design platform means we have an advantage for targets.
Either historically not been tractable all had no design flaws.
We're able to achieve this by encoding our desired target product profile of SaaS or a program and then leveraging multi parameter optimization to find the appropriate design balance.
We have always integrated design was experimental data and this grounds and models that drive are designed to the truth.
We now have the capability to automate these experiments without new automation facility.
This will enable accelerated learning, which we will talk about more later.
We can further aim for an increased probability of success by selecting the right patients for each treatment option.
David Hallett: No two patients are the same and neither is that disease progression or response to drugs.
We use complex heterogeneous primary human tissue samples that get as close as possible to the best model the human patient.
This enables us to pick up the signals that would not be possible with current preclinical models.
This information is used broadly support drug design programs biomarker identification and feeds our target identification processes.
David Hallett: Operating this way enables us to accelerate the rate at which our systems Lynn and provides the framework to enable Joe design and a much more efficient manner and with the highest probability of success.
This increase in speed probability of success on cost efficiency.
Potential to do what we like to call shifting the curve.
If we are able to decrease the cost and time to bring new medicines to patients. We can significantly change the return on investment or ROI on each program that we'd start.
By ensuring that these programs result in better quality drug candidates. The demand is likely to increase further pushing the return on investment higher.
David Hallett: This clearly has great economic value, but it also means we can explore more areas from a scientific point of view.
Shifting the curve also means more patients will have more charges from the potential expansion of treatment options.
This is why I and my colleagues joined accented.
Speaker Change: We are on a mission to make a difference to the pharmaceutical industry at large and we remain committed to continuing our work here.
Speaker Change: We're always looking to improve our platform.
I will now highlight some of the key updates we have made throughout the year.
Internally, we have already started to incorporate large language models or <unk> for short into our target identification.
One of the applications of our LMS is the ranking of targets based on their association with disease.
Our target ranking LLM includes more than 1 billion parameters and he's built from both public and proprietary data sets.
The additional benefit of using <unk> is that we can now also ramp and then re rent targets based on additional context that we may be looking for.
The example here shows how we've applied this antiviral targets as part of our pandemic preparedness work.
We performed an initial ranking of targets associated with influenza.
Speaker Change: When performing a general sets for influenza target C is ranked first target is ranked third and target B is ranked 390 <unk>.
Speaker Change: However, when we add the additional context of targeting cell binding infusion.
Now re ranked with target <unk> ranked first in target be ranked seventh.
This is a useful inside a cell binding of fusion are essential steps in the influenza lifecycle and disrupting viral entry into the cells can prevent viral replication.
Target B has been previously identified as being part of this process and demonstrates that by using more precise prompts the rankings can be refined towards a specific biological process.
These models provide a productivity benefit as they allow us to determine the best targets to prosecute experimentally as target currently is.
It is important to note that these models do not just have applicability antivirals, but can also be used in oncology or any other disease indications.
This purpose built model has domain expertise and has outperformed externally available llm's in our internal benchmarking.
Target ranking as a great tool for hypothesis generation.
Speaker Change: We have also begun to pair this with the large language model enhanced search that integrates a general purpose LLM with our proprietary data sources.
This enhances allows our teams to drill down into the biological rationale for a target that has been selected.
Speaker Change: We have focused on the elimination of the looser nations that have been seen with other <unk> and includes solstice grew five additional background for our tog analysts.
In this example, we have after model how does CDK seven promote cancer types.
The first thing the tool does is identify the key words all entities in the model.
This is important because these words may have synonyms such as tumor instead of cancer that we will also launch including our search.
The model that performs a search of the entities <unk> and tabulate the results alongside the source material.
This generates a first answers the question and generates follow up verification statements and questions.
Speaker Change: These statements and questions then go through the search again.
This is important because this additional search acts as a form of self interrogation.
Speaker Change: Pressing hallucinations with additional source material.
Once the verification questions have been answered the model then formulate a final consistent answer.
It is important to note that the enhanced search has enabled for conversation.
Follow up questions can be asked based on previous queries on.
And our teams have already started to use this while the early interrogation of proposed targets.
In mid 2023, we announced that we opened our automation facility.
Prior to this we had already started to bring experimentation capabilities in house.
Generating exponential data ourselves and shows these quality and high quality reproducible data is what drives our machine learning models.
With 45000 square feet of studio space. This facility have capabilities in compound management automated chemical synthesis automated logical screening and in time, we expect that it will enable us to produce proteins and develop <unk> PK assays.
Speaker Change: The use of automation for chemical synthesis and experimentation creates the potential to further reduce cycle times and lowered the cost to generate high quality experimental data.
With the fidelity sensitivity and reproducibility that comes with automation. We are finding that we're able to develop assays that were not accessible to us just using a manual approach.
We believe that we are the first company to have built an ultimatum facility that has the <unk>.
Potential to close the loop between AI, let Joe design an experimentation.
We have integrated software modules that Cuba, AI generative design active learning and AI synthetic roof design with our hardware.
Speaker Change: We also believe that we are the first company to develop software that can orchestrate synthesis unexpected station with the competition will position designed compounds.
Diving the integration of the virtual world with the real World.
The experimental hardware is combined with an engineering robotics platform that can physically move samples through the suites.
We have developed our own bespoke software to enable this and integrated this without lab informatics suites.
Collection of data is deliberately performed in a format to best enable our machine learning models.
Speaker Change: These changes also mean behavioral changes in the way outside his work.
Speaker Change: Our teams have always been data led and this approach further focuses the need for this.
With lepton required for optimizing and executing experiments. The teams can now be free to spend more at that time on the strategic questions and setting the parameters of the experiments being run.
The productivity gains from the virtual World are often limited by the reality of the physical world.
Speaker Change: The logistics of manual work required for the current approach to drug discovery can be a bottleneck.
The reason we have made this investment into an automation facility is that while we strongly believe that the use of AI and other computational techniques have incredible value. This alone will not drive productivity through the limitations of the physical world.
The maximal impact of AI is achieved when it is deployed in a continuous learning system.
This will require the tight integration of AI, driven generative design with high performance May contest.
The speed and quality of our loops is what will determine success.
Speaker Change: This means integrating learned within the design make test loop and automation has the potential to enable this.
Speaker Change: We look forward to providing future updates on this platform.
Speaker Change: In addition to these platform updates. We also wanted to provide some key updates from our pipeline and how these have the potential to shape. The year ahead, starting with CDK seven.
Due to the dual targeting of cell cycle, and transcriptional mechanisms inhibiting CDK seven could be used for multiple tumor types.
The majority of cancers are transcriptional addicted with semic overexpression on the impacts of cell cycle inhibitors has been demonstrated by CDK <unk> inhibition.
The three major CDK, four and six inhibitors generated nearly $9 billion between them in 2022 alone. However, a significant portion of patients ultimately develop resistance.
467, our CDK <unk> inhibitor. We are currently in the dose escalation phase of elucidate and adaptive model driven phase one two trial.
Colorectal and head and neck pancreatic non small cell lung.
Mone receptor positive.
Two negative breath carcinoma on ovarian cancer are all included and elucidate.
Speaker Change: As you can see on this slide that an estimated 75000 patients diagnosed with these tumor types in the U S alone each year.
Speaker Change: Enrollment for the trial is progressing well and we expect to move to the dose expansion phase of the trial in the second half of this year.
This will be in monotherapy and we expect to follow this up with combination dose escalation.
Speaker Change: The point of transition, we expect to be able to provide data on both the safety and pharmacokinetics of 607.
We believe that many families in the clinic can be predicted based on the sub optimal design of compounds.
Speaker Change: The table on this slide is color coded to show how close a profile compound eggs to an optimized target product profile.
Green represents no deviation from the property's ideal range orange money deviation and read a major deviation.
Speaker Change: The first to collect COVID-19 columns up from two CDK <unk> inhibitors designed by other companies.
On the right hand side, we have $6 seven.
You can see that $6 seven was deliberately designed with a short half life.
This in combination with its non covalent binding enables better control of the duration of inhibition.
This is important because extended exposure could lead to systemic toxicity.
$6 seven was also designed with reduced efflux and transporter issues.
This is important because issues with transporters will likely contribute to variable absorption and gastrointestinal issues from compounding accumulation in the Gi tract.
This is key because in this table you can see that both the compounds on the left appear to have transport issues. In fact, the phase two compound has reported some safety data with adverse events linked to the Gi tract, such as diarrhea, nausea and vomiting.
Speaker Change: These adverse events in turn May put restrictions on dosing meeting suboptimal dosing is used.
With suboptimal dosing that drove full efficacy potential may not be achieved.
The stresses the importance of both pharmacokinetics and safety data from our early CDK seven clinical trials.
Many of you may remember from our last earnings call. The mechanistic rationale for inhibiting LSD, one to treat both AML and small cell lung cancer.
At a high level LSD won't promote cell differentiation for these cancer types.
Speaker Change: <unk> either slows the expansion of tumor cells authentic causes them to cytotoxic agents.
We have generated in vivo animal and ex vivo human data that demonstrates efficacy for <unk> hundred nine our highly differentiated LSD one inhibitor.
Speaker Change: We now plan on entering into human studies in the second half of 2024 and are undertaking work to identify the optimal patient group to start with.
Speaker Change: Both small cell lung cancer, and AML remain our focus indications for 539 and.
And we are currently performing analysis to find the optimal study sequence.
As with all our clinical programs to date, we continue to look to leverage modeling formed drug development by incorporating this trial data until our ongoing simulations.
This will allow us to assess the benefits and risks of the program to earlier informed decision making.
Speaker Change: We remain excited about our lead pipeline programs, 617% and $5 39.
We look forward to providing updates not just on these but also 565, almost one inhibitors as well as the other discovery programs in our pipeline.
I'll now hand over to Ben Taylor, our CFO and Chief strategy Officer to walk us through our partnerships on financials.
Thank you Dave.
In 2023, we signed deals with Merck PGA and Sanofi that continue to move our partner pipeline forward in.
In addition to these new deals we achieved our first discovery milestone with Sanofi and BMS initiated a phase one clinical trial for our lead program in their partnership.
Casey theater inhibitor called <unk>.
In total we have received roughly $230 million from our three major partners, which has been used to expand both our pipeline value and platform capabilities.
This number will continue to grow as we hit anticipated milestones over the coming months and years ahead.
We intend to be active in business development. During 2020 for Accenture is fundamental value proposition is efficiently, creating drugs that have a greater probability of success than would be possible using traditional methods.
That gives us broad flexibility and the types of business development that we can pursue.
Our recently announced deal with Sanofi moved one of our internal discovery programs into the Sanofi collaboration with enhanced economics.
Even prior to starting clinical development, we have potential payments on that program of $45 million, followed by over $300 million in milestones and high single to mid double digit royalties.
We were able to drive this level of economics. Despite the programs early stage, because we were able to show how our technology platform may have shelved in major limitation for an existing class of drugs.
This is a good example of how an investment into proof of principle can translate into a major economic gain.
In the end our business development is not formulaic, rather just focus on driving the return on investment from the areas, where our platform can have the most impact.
Now I'll take a minute to close with highlights from our financial results full results are detailed in our press release and form 20-F.
I'll review the results in U S dollars using the December 29, 2023 constant currency exchange rate of one point to seven.
We ended the year with $463 million in cash equivalents and fixed term deposits.
Over the course of 2023, we saved over $60 million versus our original budget by substantial gains in operational efficiency and prioritizing the programs with the greatest potential opportunity.
This has put us in a strong financial position with a cash runway, including anticipated milestones well into 2026.
This allows us to further advance our differentiated clinical programs deliver on our partnerships and expand and integrate our technological capabilities.
Speaker Change: Our full year net operating cash burn was $150 million.
Speaker Change: We are not providing specific guidance, we expect operating cash burn to be less in 2024 than it was in 2023.
With that I will turn the call back to Dave.
Dave: Thank you Ben.
Let me summarize 2023 as a year of steady progress.
Dave: Rigorous pipeline focus and critically expanded technology capabilities.
We expect Thats, our focused internal pipeline may eventually lead to drug candidates of high value and differentiation with a high probability of success that.
Dave: That makes a great difference to patients our investors partners and also to our employees.
Aside from the opening about new automation facility, enabling the even faster or more efficient internal synthesizing and testing of new AI design molecules. In 2023, we also steadily progressed, new and existing programs without large pharma partners.
In 2024, we will continue to advance our internal programs through clinical development.
In addition, we look forward to progressing our partner programs to meet agreed deliverables, while striking new partnerships and expanding our technological capabilities to cement our leadership in AI powered <unk> design discovery and development.
With that we'll open up the call for questions and answers.
Thank you at this time I would like to remind everyone in order to ask a question Press Star then the number one on your telephone keypad.
We'll go to our first question from Alec Stranahan of Bank of America.
Hey, guys. Thanks for taking our questions.
Just a couple from us actually just on the the.
The automation capabilities that have come online.
Trying to put maybe a better.
Better earmark around the the dollar value.
We could see this realized in the near term or medium term I guess, how do you see the facility playing out from a business perspective does that have a benefit to cogs or G&A.
And the immediate impact or is that maybe a longer term and how.
How about time from discovery to bringing new assets to the pipeline.
And then maybe as a follow up.
How do your current partners view these capabilities and could this be a good selling point for you to leverage and future partnership conversations. Thanks.
So that's a fantastic set of questions you can probably tell.
But collectively really excited about.
This automation platform and bringing it online. So we opened the facility back in the summer of 2003.
And started running biological assays.
Dave: During the second half of last year.
We're already demonstrating the operational impact so.
We expect to see kind of.
Savings in <unk> and.
In many aspects.
The onshoring of work that we would just kind of a typically for the last few years has been been taking up to contract research organizations.
We are bringing chemistry on line literally as we speak and again, we would expect to see an impact of that incentive.
Dave: One aspect of it and we've always outsource biological experimentation as it has been something we've been kind of growing steadily over the last four years, what chemistry has always been something we have singularly outsourced.
We've already started multiple programs on the platform both internally and with partners. So our partners are already starting to see the benefits of our investment into automation.
I think it's important to remember that.
It's not just cost is clearly important.
Dave: But it's about data quality and data quality for machine learning.
I think we've tried to punch out on the slides because we're an organization that fundamentally think that drug discovery is a learning problem.
The speed of learning is really important to us and so.
We expect to see a kind of an acceleration both in the rate at which we learned that in turn should have a knock on effect in terms of maintaining quality and design IC accelerating the speed with which we can bring high quality candidates forward.
I'll shift them also had any additional color to that yes, sure and we're not providing specific guidance on how much of a financial impact it could have but I will say.
It could be pretty substantial.
We expect the payback period, just from a cost perspective alone just to be.
In a in a handful of years not this.
Dave: Isn't something that needs to run for a decade to pay itself back.
So that that can give you a sense assuming the cost savings I would say the other part to it is on the time savings.
This will be.
In some ways as dramatic of a step.
As we took with the early AI design work and being able to cut out.
A massive part of the time requirements for it so I think what we look at it and saying what can we take down from months to being weeks or.
Dave: Short periods of time, and how quickly can we learned to Dave's point, because if we can get faster learning cycles, we can actually ask more questions and learn more quickly. So you actually do get to a better result, not just that efficiency.
Great. Thanks, guys congrats on the progress.
We'll move next to Chris should future ships, Johnny at Goldman Sachs.
Hi, good morning. Thank you so much for taking our question. This is Chris Schmidt on for Chris.
So we were wondering in regards to the Santa Fe collaboration if you had any color or guidance on the cadence of milestones expected in the year ahead. Thank you.
So thank you and thanks for that question.
Dave: So.
Very excited about the kind of progress we've been able to make within the Sanofi collaboration.
Speaker Change: We achieved the first milestone.
Speaker Change: Late in 2023.
And then under the program that was Accenture originated.
Speaker Change: That collaboration we've enhanced economics around that particular project.
That speaks to the progress and the debt we have that.
Well I won't give specific guidance in terms of kind of.
Speaker Change: Actual numbers that we're expecting that the milestones to kind of ramp up over the next kind of.
Kind of 18 to 36 months.
Speaker Change: At a higher kind of cadence in that we see and that's been so far given that the first two years about our sheet and preparing the ground identifying targets and progression of them wanting to lead developments.
Thank you so much.
We'll move next to Peter Lawson at Barclays.
Great. Thanks, so much thanks for the update just a couple of questions around data disclosures for <unk>.
Peter Richard Lawson: CDK seven.
When would when should we expect to see clinical data.
And to the dose escalation side of things, where we what should we be focused on.
And then the question around $5 3 million.
Very good it's Mike speaking.
The.
Program is currently in its.
The monotherapy dose escalation phase.
A continuous reassessment method.
<unk> will initially establish a maximum tolerated dose at which point, we will morph into a monotherapy dose expansion.
At that time point.
You can look at efficacy data.
That will be an evolving picture.
Within this year.
We will look but not expect conclusive readouts on.
On the efficacy.
Forcing you to 70.
When we get to see the efficacy data in the second half.
So I believe that.
<unk>.
Bill.
Hefty accumulating data being assessed.
But there will be.
Peter Richard Lawson: No.
Peter Richard Lawson: Not sufficient data to make.
A concrete statements about efficacy in 'twenty four.
Okay.
Peter Richard Lawson: Great.
<unk> data.
Yeah, Peter I'll just jump in on there really quick so.
You definitely will get PK PD safety data.
As you would expect from a phase one trial and there may be efficacy signals that we could talk about but if you think about the time period. It wouldn't have been in an effective dose long enough to have durable responses that we'd really want to be able to talk about for efficacy anyway. So.
I wouldn't expect durable efficacy data in the second half of this year.
Especially because of how dose escalation works.
But I do want to emphasize as well remember that the.
The goal on CDK seven is really.
Design goal here. This is a mechanism where we've seen CDK four six.
Really well CDK seven could potentially build off of that but it's managing that therapeutic window and so that was where a lot of our design work in our platform differentiation come from so you will actually get to see that out of the phase one data because you'd be able to see that in the PK PD safety data.
Perfect. Thank you so much.
And then on three alright, sorry, 539, LSD, one inhibitor where are you for the DCT Cta.
Speaker Change: So we're currently.
Peter Richard Lawson: Preparing.
For a first in patient study.
We're interested in.
Peter Richard Lawson: <unk>, but also small cell lung cancer.
Peter Richard Lawson: And.
Finally, the key.
Al.
Peter Richard Lawson: Exploration of these indications in conjunction with.
Peter Richard Lawson: Key opinion leaders, but also in discussions with health authorities.
Speaker Change: Got you.
When do you think.
<unk> or Cta will be filed later.
We are aiming for.
Third quarter.
2024, and also aiming to have post patient dosed before the end of this year.
Perfect. Thank you so much I appreciate it.
Our next question comes from Vikram pure hit at Morgan Stanley.
Vikram: Hi, good morning, Thanks for taking our questions. So we had to.
One following up on 539.
Our release mentioned that.
X sight to study that you initiated earlier this year it could be used to help progress that program. I was wondering if you could just speak a bit more about specifically, how <unk> could be woven into the.
The development for that molecule and then secondly on.
Your appetite for partnerships broadly.
What do you think.
Our partnership could look like either.
Vikram: Sure.
Biopharma or for the tech space in the coming years, what do you think would be interesting for <unk>.
Form a partnership around in either or both of those spaces.
So I will start working on the question regarding the studies.
Studies.
Excellent one initially looked at you mentioned luxury consumers.
And.
We have now gone also looking into solid tumors and excite to looking at you mentioned electrical tumors.
We are building.
Building out our ability to look at data from.
The.
Tumor samples to see to what degree and these observational studies we can.
Predict.
Sure.
And we'll.
Vikram: We'll use this in.
Later stages of the program too.
Further establish which combination therapies to explore.
Okay.
Regarding <unk>, maybe I missed your question.
Yes, yes, I can I can take that one.
We absolutely have appetite for more partnerships and I think you'll see us continue to be very active on the BD I think one of the things thats been.
Speaker Change: Really helpful for US is as our pipeline has advanced and we've started to see some of our own internal programs come up.
It actually gives us more flexibility on what we do with some of those programs like what you saw with Sanofi towards the end of last year, where we we took it to a point, where we could validate that we were doing differentiated work.
Speaker Change: We could get enhanced value for that and so we went in for a partnership of that it wasn't the same type of partnership that we have done historically, where it was literally starting with a target and then progressing on it was us taking it forward a little further.
And the economics on that are terrific as a result of it. So I think there's part of it we're looking at our pipeline, we're looking at our capabilities and saying.
Where can we enhance even beyond the value that we've had historically and built off of that so I think that's a core focus for us.
And you brought up the tech side, its actually interesting we've got great.
Great dialogue with a lot of the Big Tech names, we're doing sort of the high end of.
Of what they'd like to see the entire industry doing.
And so theres a lot of great collaboration around that.
But it's also interesting because we're not a big data screening company right and there is there is absolutely nothing wrong with the big data screening company. It's just something very different from what we do we ask very precise questions and we generate proprietary data that is informed to solve those very.
And so our actual compute needs.
While they are high and they are not.
Speaker Change: Big is a big data company would need and so it's a different type of partnership than a lot of people are looking for.
We are not in search of a question, we actually know what the questions are that we go into a project with and we are on a learning curve.
To solve that and so.
We use a lot of different types of AI, we use a lot of different types of technology and oftentimes. They are sort of the cutting edge of where things are going so theres a lot of ties to the tech community, but it is not.
Speaker Change: Maybe the same sort of thing that you've seen out there with other companies.
Understood. Thank you.
And there are no further questions at this time I would like to turn the conference over to Dave for closing remarks.
Thank you operator and.
Thank you to everyone on the call today for your continued support of <unk>.
We remain committed to our mission to fundamentally change how the world designs and develop drugs.
The design platform, we have been using an evolving for over a decade now the investments we've been making into automation and a fantastic group of people, we have brought together positions us well to achieve this.
We're all really excited about 2024 and the milestones ahead of us.
Operator, you may now disconnect.
Thank you and this concludes today's conference call. Thank you for your participation you may now disconnect.
Please wait the conference will begin shortly.
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Speaker Change: Okay.
Sure.
Speaker Change: [music].
Yes.
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