EXAI - Exscientia plc (EXAI) Q4 2022 Earnings Call Transcript
2023-03-23 10:45:04 ET
Exscientia plc (EXAI)
Q4 2022 Earnings Conference Call
March 23, 2023, 08:30 AM ET
Company Participants
Sara Sherman - VP-IR
Andrew Hopkins - CEO
Ben Taylor - CFO and Chief Strategy Officer
Dave Hallett - Chief Scientific Officer
Mike Krams - Chief Quantitative Medicine Officer
Garry Pairaudeau - Chief Technology Officer
Conference Call Participants
Vikram Purohit - Morgan Stanley
Peter Lawson - Barclays
Presentation
Operator
Hello, everyone. My name is Chris and I will be your conference operator today. At this time, I would like to welcome everyone to Exscientia's Business Update Call for the Full Year Ended 2022. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions]. Thank you.
At this time, I'd like to introduce Sara Sherman, Vice President of Investor Relations. Sara, you may begin.
Sara Sherman
Thank you, operator. A press release and our 20-F were issued this morning with our full year 2022 financial results and business updates. These documents can be found on our website at www.investors.exscientia.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 our call. 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 Exscientia 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 Andrew Hopkins, Chief Executive Officer; and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Scientific Officer; Garry Pairaudeau, Chief Technology Officer; and Mike Krams, Chief Quantitative Medicine Officer will also be available for the Q&A session.
And with that, I will now turn the call over to Andrew.
Andrew Hopkins
Thank you Sara.
2022 was another transformational year of Exscientia. We continue to validate our AI driven precision medicine platform and strengthen our business. Exscientia's goal is to fundamentally transform the way our industry designs and develops drugs. We believe our unique pattern of excellent science with advanced computational experimental capabilities at every step of the R&D process, differentiates our patient first precision medicine approach. Our approach of model driven adaptive learning as an overarching technological principle enables us to innovate from discovery and into development.
To that end, our remarkable progress to-date is a testament to the strength for our company. We are well capitalized with $611 million in cash at the end of the year. This provides us with several years' runway to advance our near term programs deepening our pipeline whilst also investing for long term growth.
To that end, we marked several significant milestones throughout the year across our internal and partner programs, which we will provide more insight on today. We will also highlight data that illustrates how we're working on designing better drugs and the right patient by combining precision engineering with personalized medicine.
At a high level, last year was significant for a trajectory of our business. We started here by signing the collaboration with Sanofi for up to 15 targets and ended the year showcasing the value of our multimodal gene signature data for immuno-oncology patient selection at ESMO I-O.
Importantly, we've advanced our pipeline. We highlight five programs across oncology and immunology and inflammation, either in clinical stage or IND enabling studies. We have presented important data from our design and translational platforms across these programs and we are pleased we can now share these targets and how we combine precision design with personalized medicine.
Our A2A candidate EXS-21546 or 546, last year we reported top line healthy volunteer data in June. The data confirmed our target product profile design, including potency, high receptor selectivity and expected low brain exposure with no CNS adverse events reported. This data provided support to move into our patient trial.
Late last year, we also received CTA approval to initiate our Phase 1/2 trial. IGNITE trial will examine the safety, pharmacokinetics, pharmacodynamics and efficacy of 546 when used in combination with anti PD-1 therapy in relapsed/refractory renal cell carcinoma and non-small cell lung cancer.
During the trial, we will be observationally validating our patient selection biomarker aimed at enriching patients more likely to respond to 546 discovered preclinically. The trial will enroll up to 110 patients and we continue to expect the first patient to be dosed in the first half of this year.
A vital component of our A2A program is a robust biomarker strategy as we believe the key to a successful A2A inhibitor is enriching for the right patients with high adenosine in the tumor microenvironment. We presented relevant data at ESMO Immuno-Oncology Annual Congress in December identifying a novel patient selection, multi-gene transcript signature, the Adenosine Burden Score, or ABS.
The biomarker was discovered preclinically used in our patient tissue platform and multi-omics data set integration from complex disease relevant model systems. The IGNITE trial will evaluate this gene signature to identify patients most likely to respond to 546. We look forward present an additional data on biological validation of the ABS and our 546 program at the upcoming AACR meeting next month in Orlando.
With our CDK7 inhibitor, GTAEXS617 developed in partnership with GT Apeiron. We remain on track to enroll the first patient in our planned Phase 1/2 study in the first half of this year. This program also showcase what truly makes Exscientia drug unique precision design aiming to transform patient benefit and patient selection strategies.
We highlighted some miss patient selection data at the EMA Congress in October to maximize understanding of the effect of 617 using primarily patient material. For the first time, we showed how we integrate machine learning, data from primary human tumor samples and multi-omics sequencing capabilities to predict tumor efficacy of 617.
Using our deep learning AI and high content imaging platform, we've previously confirmed 617 activity in primary human samples. Data presented at AACR led us to generally define two groups of patient samples, effectively high and low responder groups when focusing on ovarian cancer. We believe that leveraging this information will enable us to identify responders and non-responders to 617 across tumor types. This is a key component of how we designed our Phase 1/2 study. We look forward to sharing more detail on the tumor types we've investigated shortly.
With both our CDK7 and A2A programs, we can now begin to see the hallmark of what an Exscientia drug looks like. AI machine learning is applied not just in the process of how we design the drug, but also how we identify the right patients for that drug. Exscientia invented the first AI design drug to ever enter the clinic. Since then, we have made significant advancements in our technology and AI capabilities. We have developed a comprehensive physics-based platform, encompassing molecular dynamics and quantum mechanics, which is combined with our AI generative and active learning capabilities. We have also significantly progressed our engineering and data platforms, enabling scalability and robustness.
As you may have seen, over the past several weeks, we have highlighted three new targets that are progressing. EXS4318, our clinical-stage PKC feature compound that was in-licensed by BMS. EXS74539, our LSD1 inhibitor, and EXS73565, our MALT1 protease inhibitor. These embody what Exscientia can do in terms of AI generator molecular design where we truly lead the field.
These molecules are great examples of how our drug centers using our AI can solve complex problems such as kinase cell activity in the case of PKC beta; brain penetration, coupled with reversibility in the case of LSD1; and aloseRic inhibition in the case of MALT1. I'll now spend a few minutes on each of these important programs, showing you how we design these compounds and what we have seen to date.
In February of this year, Bristol-Myers Squibb initiated a first-in-human study of EXS4318, a potential first-in-class selective PKC-theta inhibitor. BMS will oversee clinical and commercial development and Exscientia is eligible for milestone payments. And if approved, tiered royalties on net product sales.
PKC-theta is an attractive immune modulating target, which plays a critical role in the control of T cell function and is a key driver of several highly common autoimmune diseases, but has proved challenging to dose. The target product profile was particularly challenging due to the need to balance sustain, high levels of target inhibition to drive efficacy with low daily dosing in-humans. PKC-theta is structurally similar to several related kinases, makes it difficult to achieve high levels of selectivity required to avoid off-target effects.
Our team of experts, leveraging our AI design platform, delivered a balanced candidate with potent on-target activity, while maintaining high selectivity and a favorable therapeutic index as demonstrated in the IND-enabling studies.
As you can see here, previous molecules have failed that were challenged to design a candidate with required potency as well as selectivity against ever closely related kinases. We believe our well-balanced molecule meets required properties to potentially provide benefit in patients. These molecules are first immunology and inflammation candidate to enter into a clinic. This is a significant milestone, illustrating Exscientia's strength, efficiency and flexibility to precision design high-quality therapeutic candidates.
Last week, we shared an update on our next-generation LSD1 and MALT1 inhibitors. I'll highlight a few details, but I encourage everyone to visit our website and watch the video, which detail these candidates forever. 539 is a differentiated lysine demethylase 1, or LSD1 inhibitor, precision designed to improve patient benefit and solve challenging design objectives. It promises strong potential in both hematology and oncology.
To date, other LSD1 inhibitors in development elsewhere have failed to achieve the combination of appropriate pharmacokinetics, good brain penetrants and reversible mechanism of action. Our candidate has been designed to achieve suitable CNS penetration to target brain metastases common in certain cancer subtypes.
In vivo studies have shown favorable activity also in small cell lung cancer xenograft models, with dose-dependent inhibition of tumor growth. In vivo studies have also shown a favorable absorption, distribution, metabolism and excretion profile with shorter predicted human half-life and some LSD1 inhibitors currently in clinical trials. We believe this may benefit on-target tox management, allowing for platelets to recover following dosing, given the reversible nature of 539.
We believe the exquisite control of LSD1 inhibition and with superior management of platelets will be a critical differentiator for 539 in the clinic, particularly in combination with the standard of care that often has negative effects on platelets. The flexibility to genuinely explore intermittent dosing regimens in the clinic and thus, maximize our therapeutic window is another reason we believe that 539 is differentiated from other compounds in development.
Here, you can see the properties of 539 against two other LSD1 candidates, specifically looking at factors such as CNS penetration, mechanism of action and predicted, as in the case of 539, or published clinical dosing regimens. As you can see, only 539 achieves a unique combination of a reversible mechanism, suitable CNS penetration to target brain metastases and the predicted human half-life aligned with once daily dosing.
Our molecule also met a long list of other criteria, such as selectivity against related enzymes, high bioavailability in preclinical species, and in vivo efficacy in relevant models of SCLC, a potential indication where 539 may have benefit.
Importantly, we were able to use AI to find this highly differentiated molecule with its specific target product profile, ultimately exploring new chemical space. Using our 3D mapping algorithms, we identify targetable features of each region of a protein. We then used our free degenerative AI design algorithms to produce prioritized populations and molecules, meeting specific optimization criteria.
Machine learning models efficiently scored the compound of CNS penetrants alongside optimizing multiple parameters, including potency and admin properties. By then applying active learning methods, we were able to select the most information-rich molecules to make and test at each design cycle, usually around 10 to 20 compounds. This allowed us to find novel molecules outside the established domain of applicability that were counterintuitive, which enabled us to find a new starting point for design and covering this chemotype ultimately led to 539.
The result was that we were able to create molecules which achieved a target product profile that no competitor molecule have exhibited. We believe 539 has the potential to become the first potent, selective, reversible and brain penetrant LSD1 inhibitor to meet significant unmet need in a range of oncological and hematological indications. We look forward to highlighting the precision design of this compound as well as the latest in vivo data at the AACR next month.
I'll now highlight another candidate we've recently availed, our MALT1 inhibitor, EXS73565, or 565. MALT1 or mucosa-associated lymphoid tissue lymphoma translocation protein 1, is an important proteus target with potential applications in hematology. It aims to inhibit the uncontrolled proliferation of malignant T and B cells in hematological cancers. Exscientia's AI-driven precision design approach was able to optimize the safety profile of agents targeting MALT1, was also generating potency and selectivity.
When considering an optimal target product profile, the team took into account the likely use of a MALT1 inhibitor in combination Fabry such as BTK inhibitors. Therefore, in addition to potency, selectivity in a balanced set of properties, we were mindful of potential drug-drug interactions. In these two charts here, you can see that in vivo studies of 565 have shown antitumor activity in most models and favorable pharmacokinetics both as monotherapy and in combination with ibrutinib.
Our toxicology studies have also shown an acceptable Fabry index, with the ability to maintain high levels of potency, selectivity and safety benchmarks, whilst avoiding meaningful inhibition of UGT1A1, which can lead to excessive levels of bilirubin and is a known cause of drug-drug interactions.
This chart here compares 565 directly with published and patented MALT1 scaffolds from various groups. 565 compares very favorably across all parameters, examining importancy, cellular activity and drug-like properties. 565 has very little activity at UGT1A1 and is highly differentiated in this respect. We would predict that many of the other compounds will likely inhibit UGT1A1 to a meaningful degree and thus present challenges in clinical development.
It's important to note, in designing this compound, it was the first time that we merged molecular dynamics with AI at Exscientia. Molecular dynamics, a physics-based method, is just one of the tools that we have in our tech stack today. In fact, molecular dynamic simulations provided additional insights into critical binding interactions within the allosteric site.
Using Hotspot analysis allowed us to understand the allosteric binding pocket, highlighting key interactions needed for design. Molecular dynamics then enabled us to understand the dynamic motion of the binding pocket and developed a design strategy to improve potency and broader properties of our compounds.
Using data and knowledge of other allosteric MALT1 inhibitors, our generative design algorithm, Gambit, was used to evolve novel molecules. This resulted in a suite of promising compounds. We believe that 565 can be developed to meet a significant medical need that exists today, through potent and selective MALT1 protease inhibition, with the potential for a meaningful safety differentiation.
In summary, these compounds demonstrate the potential of the Exscientia platform to efficiently deliver precision design compounds that may provide substantial benefit to patients. Better design, we believe, improves the probability of success of reaching patients.
And while the average industry time line to discover a development candidate takes around 4.5 years, and synthesizing between 2,500 to 5,000 compounds, it is remarkable that these two candidates were designed in 15 to 20 months, respectively. And from synthesizing, 344 and 414 precision design compounds. These stats underscore how our AI-driven approach is not only differentiated in terms of precision design, but also faster and more efficient than conventional methods.
IND-enabling studies are underway for both these inhibitors, and we expect to provide an update on clinical development plans leveraging Exscientia's personalized medicine platform in the second half of 2023. These compounds have potential broad application in oncology and hematology.
Overall, we are thrilled with our recent advancements and look forward to sharing more details on our progress.
I'll hand over now to Ben to walk through our financials.
Ben Taylor
Thanks, Andrew.
I'll now take a minute to close with highlights from our financial results. Full results are detailed in our press release in 20-F. I'll review the results in U.S. dollars using the December 31, 2022, constant currency rate of $1.2077 to the pound.
We ended the year with $611 million in cash, equivalents and bank deposits. We believe this provides us with several years of cash runway and the resources to continue investing in our growth. At the same time, we believe that the recent macroeconomic factors, including bank defaults, political trends and large pharma announcements will cause 2023 to be a year of economic conservatism in the biopharma industry. As a side note, Exscientia does not have any banking exposure to SVB or Credit Suisse.
From a business model perspective, we are well positioned to respond to the current market environment. We have now repeatedly demonstrated that we can achieve better drug discovery outcomes faster and with less cost than traditional methods.
In order for the pharmaceutical industry to improve ROI in the face of growing price and competitive pressures, it needs the quality and efficiency that we bring. We also believe that our personalized medicine platform will help improve the probability of success in the clinic, which, in turn, will further improve return on investment.
At Exscientia, our partners continue to invest substantial resources in our projects. Our existing partnerships alone could contribute several hundred million in milestones over the next three years. We expect a number of earlier milestones during 2023, with the majority of the milestones occurring in 2024 and 2025 as we achieve development candidate goals.
We are also seeing an active interest in new business development and are reiterating our guidance of at least two deals this year. We have seen a focus on innovative technologies and specific pipeline candidates from potential pharma partners, and as a result, we have begun to adjust some of our operations to focus on areas that we believe will have the highest near-term impact and return.
In addition, we believe it is important to respond to the macroeconomic environment by keeping our own operations as efficient as possible. Over the last six months, we have reduced costs with several of our CRO relationships and believe there is additional room for improvement without a loss of quality.
In addition, we are evaluating multiple ways to apply technology or streamline our internal operations in order to drive greater efficiency. We estimate the combination of these efforts will save tens of millions of dollars in operating costs over the course of 2023.
I also wanted to be clear that our guidance of several years cash runway allows us to take all four of our disclosed later-stage internal programs, including A2A, CDK7, LSD1 and MALT1, through initial proof-of-concept clinical trials if and when the clinical evidence and strategic business rationale support that decision.
With that, I will turn the call back over to Andrew.
Andrew Hopkins
Thank you, Ben.
Today, we'll walk you through several examples of how we are working to produce better drugs faster by innovating in both discovery and in development. We believe that our differentiated approach and our advancements this year further validates our end-to-end platform and distinguish our company as leaders in the field of AI-based drug discovery. As you can see, we have another important year ahead to best position us for the future.
With that, we'll open up the call for questions and answers.
Question-and-Answer Session
Operator
Thank you. [Operator Instructions] The first question is from Vikram Purohit with Morgan Stanley. Your line is open.
Vikram Purohit
Hi, good morning. Thanks for taking our questions. Two from our side. First, for 539 and 565. Your slides mentioned that the molecules were developed in I believe, 15 to 20 months versus a much longer industry average development time line. And I was wondering if you could just walk us through which components of early stage development you believe your platform helped to cut time lines for the most?
And then secondly, on the topic of partnerships and business development. I mean going forward, what would you be looking for in your next set of partnerships? What kind of capabilities would be most additive to seek out? And conversely, what also do you think you could be bringing your partners with the next set of partnerships that you weren't able to with the earlier set of partnerships? Thanks.
Andrew Hopkins
Vikram good to speak to you again. This is Andrew. Vikram, actually for that first question, I want to introduce Garry Pairaudeau, CTO, actually, because I think Garry's unique background of being a drug hunter and a technologist actually will highlight and understand where actually Exscientia's platform really brings value to the drug discovery and design process.
Garry Pairaudeau
Sure. Thanks, Andrew, and thanks, Vikram. So the time lines we put out the time from starting the project with a hit molecule to identifying the candidate molecule, so that's the molecule we take forward into development. Typically, industry average times are around about four years for that period. The reason why our platform is so much more effective is because we're harnessing the full power of using artificial intelligence, using multiparameter optimization and using generative design to explore chemical space much more widely.
What that means in a nutshell is every optimization cycle, we're exploring more possibilities and we're effectively taking larger jumps at each step, which is what's allowing us to cut the time down so dramatically. And we've seen this across all of our programs.
Andrew Hopkins
Excellent. Thanks, Garry. And Dave, do you want to add a bit more about a broader set of capabilities we have now?
Dave Hallett
Yes. Thank you, Andrew. I think it's important to realize that Exscientia operates at that interface between kind of experiment and artificial intelligence. And I think it's the components of each that when they're brought together that actually make a significant kind of contribution to above time line.
So we -- it's very much in our interest to actually generate high-quality data that can actually be then utilized in the machine learning kind of environment. And so that explains why we've created and continue to grow our experimental footprint. But it is ultimately kind of placing experts with the right tools they need and with the right technology, and that's kind of where you see the acceleration of the time lines.
Ben Taylor
Vikram, I'll take your second part of the question as well around partnerships. I think what's exciting about Exscientia development, particularly over the past couple of years, is how our end-to-end platform has really expanded. We've gone upstream, thinking about now how we think about target selection, particularly incorporating patient tissue, patient to life approaches in target selection and combine that then with data science approaches and deep learning approaches that really think about how to integrate the wealth of external knowledge into these experiments.
And going all the way downstream now to thinking about how we design precision medicine biomarkers for patient selection using a whole range of machine and approaches to multimodal omics, you can see that with adenosine burden score and the work we do in our CDK7 and expect to see more of that coming forward each year.
We're starting to see now of actually the hallmark of Exscientia is one way we combine both precision design with personalized medicine going forward. It's not just a process of how we design the drug, but also how we think about embodying that kind of technology into the kind of labels we're also thinking about.
But that means saying we now have a much broader offering. And I think actually, as we think about the developments and advances now, we're thinking about in clinical development and precision medicine, I think we're in a very strong position actually to offer a broad range of solutions to the pharmaceutical industry who are themselves face a number of challenges right now.
So we are very excited right now, with a lot of discussions we have in which regard how we think about applying precision medicine with partners, how we think about applying actually the model-informed model-driven adaptive learning processes that actually we've also created in discovery, how they also now apply in development in the design of our trials. So we're really excited actually.
As Exscientia grows, I think we're in a very strong position actually to continue to develop and strengthen the offering of solutions we can bring to our pharma partners.
Operator
The next question is from Michael Ryskin with Bank of America. Your line is open.
Unidentified Analyst
This is Wolf on for Mike. Thanks for taking the questions. So starting off the announcement of 539 and 565 are obviously quite exciting. Just wondering how to think about the incremental spend associated with these and other IND-enabling programs given the continued refinement and scale of your platform, and kind of building off of Vikram's question.
And then as a follow-up, how are you thinking about your current capacity for parallel IND-enabling studies? What do you see as the primary limiting factor to the number of studies that you can have ongoing, once is it just a headcount thing or the technical issues as well?
Andrew Hopkins
Excellent. Good to speak to you again, Wolf. I'm going to introduce Ben Taylor actually to answer this question.
Ben Taylor
Wolf, nice to talk to you again. So a couple of things on just thinking about our budget and going ahead and operating expenses. So as we think about 2022 versus 2023, 2022 was really the year of scaling and putting a lot of the infrastructure in place that we would need to be able to execute on a broader pipeline, both in the discovery phase as well as in development.
So when we look ahead at 2023, even though we are initiating a number of clinical trials, we actually don't see that scaling of cost continuing and would expect 2023 to be much more level with what we saw in the fourth quarter.
So I think we've actually achieved a lot of that scale and infrastructure to be able to execute. And I think that goes into your question of how we can handle clinical programs moving forward. The actual additional expense internally that we would need to do that is not substantial.
I think what we would look at is as those programs continue to get into later-stage development, that's where you really see the scaling in the expense. And so I think both on the discovery and the development side, we're at a pretty good place right now.
I also mentioned we are finding a number of good efficiencies with our CRO relationships. That's been a real change for us as well because we're now of a scale in doing enough projects where we can actually get economies of scale out of our CRO relationships. We can push that pricing dialogue without losing quality. And so that's something that has been very powerful for us recently, and I think you'll see that impact in 2023.
Unidentified Analyst
Got it. Much appreciated.
Andrew Hopkins
Thank you, Wolf.
Operator
The next question is from Peter Lawson with Barclays. Your line is open.
Peter Lawson
Thanks for taking the questions. I guess a question for Ben, just on the back of your comment about the potential area of conservativeness around pharma. And just how does that hinder collaborations? Kind of what's your analysis around that?
Ben Taylor
Yes. So to be clear, we still see a lot of interest out of pharma partners. I think what we've seen is a bit of change in the focus of the pharma partners. So back in 2020, a lot of the dialogue was around, how can I scale my pipeline? How can I do the really large pipeline deals? I think what we're seeing in this sort of environment is a real focus on specific technologies and specific identified programs. So from our perspective, we can actually handle either. And in fact, sometimes the specific partnerships can be more profitable for us than the broad pipeline deals because there's less infrastructure required to execute on them.
I think also we are in a different position than we were two years ago because we've advanced a number of programs and technologies and platforms that we didn't have then. And we've seen a lot of the pharma partners have interest in those later-stage programs as well.
So I think with this economically conservative environment, the fact that we're still seeing a lot of interest out of pharma partners. And hopefully, this is an economic cycle. So if we're seeing this level of interest right now, we feel pretty good if the economic cycle improved.
Peter Lawson
Got you. And then just a question on timing for data. I know both of your adenosine and CDK7 patients kind of in trials enrolled the first patients in the first half. Just your expectations for timing around those data sets?
Andrew Hopkins
Thanks, Peter. First I'm going to introduce Mike Krams, who leads our development efforts. Mike?
Mike Krams
Thank you for the question. First of all, we are using model-informed drug development, simulation guided clinical trial design and experiments where we accrue in real-time all the data and look at the data at all times. However, the time at which we will actually announce major findings, but probably coincide with the movement from the dose escalation phase to the dose expansion phase in the Phase 1/2 trials.
We haven't given guidance as to the exact timing of that. However, it will be similar to any of the Phase 1/2 trials. That's running in this field. But importantly, the trials that we're running are based on model-informed approaches where we accrue and analyze the data at all times.
Peter Lawson
Got you. And is -- could we expect data in second half? Or is that too tighter time line?
Mike Krams
We haven't given guidance on the exact timing. But I would think that the second half of '23 is particularly early given that the first patients will come in the first half. You can just go through the time.
Peter Lawson
Okay. Got you. Thank you so much.
Andrew Hopkins
Thank you, Peter.
Operator
[Operator Instructions] The next question is from Chris Shibutani with Goldman Sachs. Your line is open.
Unidentified Analyst
This is Roger on for Chris. So you noted that you plan on moving towards antibiotics as the company pivots towards using action learning techniques to develop on biologics. Just wanted to kind of understand since that's such an area that's challenging from both development and economics. What are the variables that make you believe this kind of investment going forward is worthwhile? Is it the plethora of data that kind of feeds into the close model? Lower competitive dynamics? Just kind of want to understand the rationale there. Thanks.
Andrew Hopkins
No, we have a relatively small effort in antivirals and panda preparedness. We have no plans all to move into antibiotics as a field right now. We have a great collaboration in antivirals with the Gates Foundation. That right now is focused on small molecules, and we move forward. That's led by Professor Ian Goodfellow of the University of Cambridge.
But an important we bought up actually is Exscientia's development of a biologics platform. We are, as we announced last year, developing e-biologics design engine. That currently, we are testing a proof of concept as we speak. We're also building out a new automated biologics lab actually to really speed up sort of a make and test cycle or generation of data.
So one of the key areas that we see here actually is the ability actually to generate high-quality data to drive machine lender models. And I'll give you an example of that. We currently have a collaboration with Oxford University, where we're looking to generate a lot of pair sequence data. We believe actually we can create some of the large databases in the world and understanding the observable human antibody space.
And that actually can give us sort of the priors and defined in our models. So when evolving and designing molecules and by AI, we can then design into where human antibody space actually is. We're incredibly excited by that actually. It's led by Professor Charlotte Deane, Exscientia, also holds a Chair in Oxford University. And later this year, we look forward to bringing to you sort of more news and information about how our biologics platform is developing.
But importantly, I think we have a key advantage here as well. We've already shown that our patient-centric precision medicine platform works equally well for antibodies as it does with small molecules. So we there can also think about downstream, how we can start to think about applying precision medicine also to biologics and bringing those two fields together. And I think that's going to be a unique set of attributes actually in that field.
Unidentified Analyst
Got it. Thank you.
Andrew Hopkins
Thanks Roger.
Operator
There are no further questions at this time. I'll turn it over to Andrew Hopkins for any closing remarks.
Andrew Hopkins
Thank you, operator, and thank you to everyone for the call today and for your continued support of Exscientia. Over coming months and into 2024, we look forward to advancing multiple programs going forward, bringing new molecules into the clinic, unveiling incredibly important new projects and programs and building up our clinical development innovations to bring truly personalized medicine to patients.
I want to thank you again for joining us today, and have a good day. Thank you, all.
Operator
Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now disconnect.
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Exscientia plc (EXAI) Q4 2022 Earnings Call Transcript