Exscientia plc 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.
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“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.”
Source: Businesswire
