Exscientia Uses Generative AI to Reimagine Drug Discovery

Working backward from patient needs, Exscientia defines precise target product profiles (TPPs) that specify the complex combination of properties required by a well-tolerated and effective medicine. AI engineers design algorithms that generate panels of potential drug candidates to meet the TPPs. Active learning algorithms help expert designers to select a short list of drug candidates to synthesize in the lab, because they either move the TPPs forward or refine the models for future DMTL cycles.

Exscientia’s algorithms are trained on publicly available pharmacology data and proprietary in-house data generated from patient tissue samples, genomics, single-cell transcriptomics, and medical literature. By encoding data throughout the process and analyzing experimental results and previous design cycles, Exscientia can optimize upcoming design cycles and promote compound designs that are physically synthesizable. Using this synthesis-aware, iterative approach built on AWS, Exscientia makes 10 times fewer compounds than the industry average. “The idea is to iron out chemical liabilities to make safer and more effective drug candidates before we ever test them in patients,” says Hallett.

Exscientia has accelerated drug design by up to 70 percent while decreasing capital cost by 80 percent, compared with industry benchmarks. Using generative AI with other tools, Exscientia not only developed better drug candidates faster but also identified the right drug combinations to trial with patients in the clinic.

Exscientia has incorporated cutting-edge chemistry synthesis and biology assay lab equipment with automation robots to avoid manual handling of lab equipment. Thus, its lab—orchestrated by AWS microservices—can operate 24/7 with minimal human supervision, “When our designs are ready, we can push a button, and within a few days, the robots are making the drug,” says Hallett. Maintaining extremely high levels of security and comprehensive disaster recovery, Exscientia will use this automated robotic capability to reduce the make and test timelines resulting from traditional offshore research contracts used across the industry.

As the company closes the loop with its robotic automation lab, it expects further productivity improvements. Data generated in the lab improves algorithmic predictions and speeds up DMTL cycles.

Six molecules that Exscientia designed using AI have entered clinical trials. “Using AWS, we reduce bottlenecks and accelerate the pipeline,” says Hallett. “By switching on this highly integrated and automated DMTL loop, we can make drug candidates faster and more cost-effective.”