DRUG DISCOVERY + HEALTHCARE
Drive innovation in biomedical science, transform new drug development and deliver treatments and care better tailored to patient needs with Graphcore-powered AI.
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SUPERCHARGE THE FUTURE OF HEALTHCARE WITH IPU-POWERED AI
In the pursuit of excellence in new drug development and patient care, IPU-based AI systems help leading pharmaceutical companies, biotechs and clinical research organisations make new biomedical breakthroughs to discover new drugs faster, repurpose medicines, model new drug combinations and advance personalised medicine to treat or cure disease and to improve patient outcomes. Researchers in genomics and protein therapeutics, and healthcare professionals around the globe are getting ahead of the curve with Graphcore machine intelligence to deliver a step change in healthcare with life changing consequences.
Make new breakthroughs in drug discovery with IPU systems. Take cellular and molecular biology techniques to the next level to validate novel targets for drug discovery. Explore new AI approaches to drug combination modelling, reducing costs and speeding time to identify new treatments.
LabGenius develops next-generation antibody therapeutics for cancer and inflammatory diseases, using a combination of AI, synthetic biology and laboratory automation. IPU systems halved the compute time needed to run crucial AI model training.
"Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there. With Graphcore, we reduced the turnaround time to about two weeks, so we can experiment much more rapidly, and we can see the results quicker.”
Dr Katya Putintseva, Machine Learning Advisor to LabGenius
Joint research from the University of Massachusetts Amherst & Facebook shows IPUs deliver a 7.5x speed-up for an Approximate Bayesian Computation (ABC) epidemiology model used in Covid-19 modelling.
“This hardware acceleration can give real tangible results in obtaining better quality results in much less time and could also be translated to many other scientific models which use simulation-based inference. We believe that this work would pave the way for more complex models and better quality inference in less time.”
Sourabh Kulkarni, University of Massachusetts Amherst
Accelerate genome analysis and predict protein structure to understand disease better and bring tailored therapeutics to market faster, bringing the dream of precision medicine that much closer.
“With IPUs we’ve seen impressive throughput in some of our key areas of research. We are excited to be testing Mk2 architecture and continue exploring the benefits of this innovative chip architecture.”
Chris Seymour, Director, Advanced Platform Development at Oxford Nanopore
Advanced computer vision models give medical staff deeper understanding of diagnostic imaging to make faster decisions for diagnosis and treatment and ongoing patient care.
Microsoft explored advanced computer vision model, EfficientNet, on the IPU for medical imaging. For pneumonia and Covid-19 X-ray imaging, researchers trained EfficientNet in just 40 minutes on IPU systems with higher accuracy than traditional vision models, compared to 3 hours on GPUs.
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