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Exscientia Launches AWS AI-powered Platform to Advance Drug Discovery

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Exscientia has announced an expansion of its collaboration with Amazon Web Services (AWS) to enhance its drug discovery platform using AI and machine learning (ML). This collaboration integrates generative AI and robotic lab automation to accelerate drug development and reduce costs.

Exscientia's platform leverages AWS technologies to design drug candidates that target specific diseases more effectively. The platform's DesignStudio uses generative AI for drug discovery, while the AutomationStudio synthesizes and tests these candidates, utilizing robotics for efficiency. The data from these tests is fed back into the AI models, improving their accuracy.

This enhancement aims to streamline the drug discovery process, benefiting both Exscientia's internal projects and its collaborations, such as those with Sanofi. The integration of AWS's scalable and flexible solutions is expected to transform the biopharma industry by increasing the quality and speed of drug development.

Positive
  • Exscientia's collaboration with AWS enhances drug discovery efficiency using AI and robotics.
  • The platform aims to accelerate early drug development at a lower cost.
  • Exscientia's generative AI models improve accuracy through iterative feedback loops.
  • This integration is expected to benefit internal projects and collaborations, including with Sanofi.
Negative
  • None.

The integration of AWS's AI and ML services by Exscientia represents a significant technological advancement in the field of drug discovery. The use of generative AI to identify potential drug candidates minimizes the need for traditional high-throughput screening, which is often slow and costly. By leveraging generative models, Exscientia can explore vast chemical spaces more efficiently, tailoring drug candidates to specific targets with a greater degree of precision.

Moreover, the addition of robotic automation for synthesis and testing, powered by AWS's flexible cloud infrastructure, enhances the speed and cost-effectiveness of drug development. This integration ensures a seamless transition from virtual design to physical testing, effectively 'closing the loop' between in silico and wet-lab processes.

For retail investors, this means that Exscientia is positioned to deliver new drugs to market more quickly and at a lower cost compared to traditional methods. This could potentially translate into faster revenue generation and a stronger competitive edge in the pharmaceutical industry.

Exscientia's strategic partnership with AWS signals a forward-thinking approach that capitalizes on the growing trend of digital transformation within the biopharma sector. The ability to harness AI and ML for drug discovery not only accelerates the process but also enhances the precision of drug targeting, which can improve clinical trial outcomes and reduce time-to-market.

In the broader market context, this partnership could serve as a competitive differentiator for Exscientia, setting it apart from peers reliant on more traditional drug discovery methods. Additionally, the collaboration with a tech giant like AWS may attract further partnerships with other pharmaceutical companies, expanding Exscientia's project pipeline and revenue streams.

Investors should note that while the technology promises substantial long-term benefits, the initial phases may involve significant investment in digital infrastructure and potential operational challenges. However, the long-term payoff in terms of cost savings and faster drug delivery could outweigh these initial expenses.

From a financial standpoint, Exscientia's move to enhance its drug discovery platform with AWS's AI and ML capabilities could lead to significant cost efficiencies. The reduction in time and resources needed for drug candidate identification and testing is likely to reflect positively on Exscientia's financials over time. This initiative aligns well with the company's objective of reducing costs and accelerating timelines, ultimately aiming to improve margins and profitability.

However, it is important for investors to monitor the implementation and integration of these new technologies closely. Any delays or technical issues could impact short-term financial performance. Additionally, the success of this initiative will depend on Exscientia's ability to attract collaborative programs and partnerships, which can bolster its revenue streams and offset initial costs.

Overall, the partnership with AWS presents a promising avenue for long-term growth, provided the company can navigate the complexities of integrating advanced AI and ML technologies into its operations seamlessly.

— State-of-the-art platform, built using Amazon Web Services technologies, integrates generative AI drug design and robotic lab automation to further accelerate Exscientia’s ability to deliver high quality drug candidates at faster speed and lower cost —

OXFORD, England--(BUSINESS WIRE)-- Exscientia plc (Nasdaq: EXAI) today announced it will be expanding its work with Amazon Web Services (AWS) to use the cloud provider’s artificial intelligence (AI) and machine learning (ML) services to power its platform for end-to-end drug discovery and automation.

Exscientia’s platform uses generative AI models and the scalability and flexibility of AWS to securely, quickly and efficiently design drug candidates that aim to better target specific diseases and patients, with the goal of accelerating early drug development at a lower cost. The industry-standard pace of drug discovery is hampered by conventional high-throughput screening (HTS) approaches, which involves outsourcing large-scale chemical synthesis and biological testing to contract research organisations. Exscientia deploys generative AI in its DesignStudio to ‘learn’ rather than to ‘screen’ for discoveries in vast chemical space. The company’s UK based AutomationStudio then synthesises and tests drug candidates that were identified by its DesignStudio as having high potential, making extensive use of state-of-the-art robotics to drive efficiency in the molecular synthesis process. Data from the testing completed at the AutomationStudio is then fed back to its DesignStudio to further improve its generative AI algorithms. By impacting both the screening stage and the synthesis and testing stage of drug discovery, Exscientia aims to accelerate the pace of drug development, closing the loop between in silico design and wet-lab synthesis.

Exscientia’s platform, which is built using AWS technologies, supports its Design-Make-Test-Learn (DMTL) loops and spans generative AI, active learning, ML, physics-based systems and many other predictive methods. It also draws on large language models via Amazon Bedrock, a fully managed service that makes high-performing foundation models from leading AI startups and Amazon, available through a unified application programming interface (API). Exscientia expects that ‘closing the loop’ of virtual design and physical experimentation on AWS will benefit its internal discovery projects as well as its collaborative programmes, such as with Sanofi, and potential future partnerships.

“Extending our collaboration with AWS beyond our DesignStudio to include the robotic automation of synthesis and testing of our molecular designs was the logical next step for Exscientia,” said John Overington, Ph.D., Chief Technology Officer of Exscientia. “We were seeking a solution that had flexibility and scalability, combined with high performance and generative AI capabilities. We also wanted a collaborator that is creative and passionate about life sciences. AWS ticks all of these boxes.”

“Our mission is to transform the way the biopharma industry invents impactful medicines, by pairing the best available human science, ingenuity and AI/ML tech expertise with innovative experimental automation technologies to increase the quality and capacity of drug design, discovery and development,” said David Hallett, Ph.D., interim Chief Executive Officer and Chief Scientific Officer of Exscientia. “By encoding, automating and integrating the loop of design, synthesis and testing with our generative design workflows in our new AutomationStudio, we have taken the next step to increase the speed of learning, and to reduce the time and cost of exploring new therapeutic alternatives. We believe that working with AWS will accelerate the achievement of our goal to deliver higher-quality, precision designed future therapies to patients in need, faster.”

“The cloud is transforming the life sciences industry and helping to accelerate the pace of innovation. We’re excited that Exscientia is expanding its use of AWS’ generative AI and ML solutions to further speed up the discovery of new treatments for patients,” said Patrick Lamplé, Principal Healthcare & Life Sciences Tech Strategist – Worldwide, at Amazon Web Services. “The use of AI is also enabling Exscientia to automate their labs and processes, which will help to lower the costs associated with drug development.”

About Exscientia

Exscientia is a technology-driven drug design and development company, committed to creating more effective medicines for patients, faster. Exscientia combines precision design with integrated experimentation, aiming to invent and develop the best possible drugs in the most efficient manner. Operating at the interfaces of human ingenuity, artificial intelligence (AI), automation and physical engineering, we pioneered the use of AI in drug discovery as the first company to progress AI-designed small molecules into a clinical setting. We have developed an internal pipeline focused on oncology, while our partnered pipeline extends to many other therapeutic areas. By leading this new approach to drug creation, we believe we can change the underlying economics of drug discovery and rapidly advance the best scientific ideas into medicines for patients.

For more information visit us on www.exscientia.com or follow us on LinkedIn @ex-scientia and X @exscientiaAI.

Forward-Looking Statements

This press release contains “forward-looking statements” within the meaning of the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995. Words such as “anticipates,” “believes,” “expects,” “intends,” “may,” “plan,” “projects,” and “future” or similar expressions (as well as other words or expressions referencing future events or circumstances) are intended to identify forward-looking statements. All statements, other than statements of historical facts, included in this press release are forward-looking statements. These statements include, but are not limited to, statements regarding the advantages of the company’s technology platform and its drug discovery programmes; the company’s belief that using generative AI will accelerate drug development; the anticipated benefits of the company’s expanded collaboration with AWS including the ability to accelerate discovery projects; the benefits of the use of robotic automation in drug design; the potential for entry into and the expansion of current and future partnerships; the potential for experimental automation technologies to increase the quality and capacity of drug design, discovery and development including to design new and more efficient treatments for patients; the company’s belief that its capabilities will enable it to invent the best drugs in an efficient manner; the company’s belief that it can change the economics of drug design and advance ideas into medicines; and the company’s business strategies, goals and approach to drug design. Any forward-looking statements are based on management’s current expectations and beliefs of future events and are subject to a number of risks and uncertainties that could cause actual events or results to differ materially and adversely from those set forth in or implied by such forward-looking statements, many of which are beyond the company’s control. These risks and uncertainties include, but are not limited to, the risk that the company’s platform technology may fail to discover and design molecules with therapeutic potential or may not result in the discovery and development of commercially viable products for the company or its collaborators; the company may be unable to advance its drug candidates through clinical development, regulatory approval or commercialisation; the impacts of macroeconomic conditions, including the conflict in Ukraine and the conflict in the Middle East, heightened inflation and uncertain credit and financial markets, on the Company’s business, clinical trials and financial position; the company’s ability to realise the benefits of its collaborations; changes in expected or existing competition; changes in the regulatory environment; the uncertainties and timing of the regulatory approval process; and unexpected litigation or other disputes. These and other risks and uncertainties are described in the “Risk Factors” section of Exscientia’s Annual Report on Form 20-F for the year ended December 31, 2023, filed with the Securities and Exchange Commission (SEC) on March 21, 2024, and well as discussions of potential risks, uncertainties and other factors in Exscientia’s subsequent filings with the SEC. All information in this press release is as of the date of the release, and the Company undertakes no duty to update this information, except as required by law.

Investor Relations:

Sara Sherman / Chinedu Okeke

investors@exscientia.ai



Media:

David Keown

media@exscientia.ai

Source: Exscientia plc

FAQ

What is Exscientia's new platform with AWS?

Exscientia has expanded its collaboration with AWS to enhance its drug discovery platform using AI and ML, integrating generative AI and robotic lab automation to accelerate drug development and reduce costs.

How does Exscientia's platform improve drug discovery?

Exscientia's platform uses AWS technologies to design drug candidates targeting specific diseases more effectively, leveraging generative AI for discovery and robotics for synthesis and testing, thereby accelerating development and reducing costs.

What are the benefits of Exscientia's collaboration with AWS?

The collaboration aims to enhance drug discovery efficiency, improve accuracy through AI-driven feedback loops, and benefit both internal projects and collaborations, including those with Sanofi.

How does Exscientia's AutomationStudio work?

Exscientia's AutomationStudio synthesizes and tests drug candidates identified by its DesignStudio, using robotics to drive efficiency in the molecular synthesis process.

What is the role of generative AI in Exscientia's platform?

Generative AI in Exscientia's platform is used for drug discovery, designing drug candidates by learning from vast chemical space and improving through iterative feedback loops from synthesized and tested data.

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