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Exscientia Highlights “The Future of AI-enabled Drug Discovery” at SLAS Europe

Exscientia plc announced two abstracts for oral and poster presentation, as well as the closing keynote to be delivered at the Society for Laboratory Automation and Screening (SLAS) Europe Conference and Exhibition being held May 22-26, 2023, in Brussels, Belgium. These presentations highlight Exscientia’s recent advancements in technology, including the development of computer-aided biology (CAB) approaches, encompassing design of experiments (DoE) and automation for biochemical assay optimisation. The SLAS Conference is Europe’s premiere venue for industry, government and academic researchers, developers and providers of laboratory automation technology innovation.

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“We believe automation has the potential to radically improve the timely creation of high-quality drug candidates, so that future patients may benefit from innovative science faster”

“At Exscientia, our mission is to encode and automate drug discovery. We believe that within this decade the design of all new drug candidates will be augmented with AI. I am delighted to showcase at SLAS Europe how our recent technology advances enable and drive our projects,” said Dr Garry Pairaudeau, Chief Technology Officer at Exscientia. Integrating laboratory automation of synthesis and screening with its AI-driven computational design platform is the logical next step for the company. “This will facilitate the next transformation in speed and productivity, as we aim to bring better medicines to patients faster. We’re looking forward to opening our purpose-built automation studio in Oxfordshire, U.K., later in 2023.”

The company’s presentation at SLAS will demonstrate how far along Exscientia is in automating the drug discovery processes. “We believe automation has the potential to radically improve the timely creation of high-quality drug candidates, so that future patients may benefit from innovative science faster,” Pairaudeau stated.

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This presentation will highlight Exscientia’s distinct approach to molecular design, using an end-to-end patient centric, AI-enabled platform, which has led the way in the precision design of AI-generated molecules resulting in the first AI-designed compounds to enter clinical trials. To reach Exscientia’s goal of fully encoding and automating drug discovery, the company is assembling an automated physical laboratory alongside its suite of sophisticated computational design tools, methods and scalable engineering platform. This presentation will highlight Exscientia’s current progress in the automation field and the critical steps needed in the implementation of automated, digital laboratories.

Exscientia is building an automated ecosystem of chemistry, biology and compound management, including developing a pipeline of fully automated biochemical assays from assay development onwards. This presentation highlights how CAB has been leveraged to achieve this pipeline, combining DoE (a statistical methodology) with automation to rapidly determine optimum conditions to run assays efficiently through analysis of multi-factorial experimental results. Utilising CAB also allows Exscientia to increase capacity and efficiency across FTEs, time and reagents required to run assays, increase assay robustness and investigate areas of biology not normally studied.

Exscientia researchers demonstrated the practical use of CAB to simultaneously improve an assay’s performance and reduce the reagent requirement, cost and labour for assay development. This presentation highlights the application of CAB to optimise the buffer of a nuclease assay, resulting in a 15-fold enhanced assay signal in low salt buffer conditions, alongside over a three-fold decrease in protein requirement​ and reduced assay development cycle times. The low salt requirement is a previously unforeseen result from CAB use which would have not been predicted from the literature. This highlights the potential for technology to improve assay performance in ways a human alone would not have been able to predict.

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