LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. The firm combines AI, lab automation, and synthetic biology for its potentially life-saving work.

Until now, the company has been using traditional GPUs for its workloads. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks.

“Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there,” said Dr Katya Putintseva, a Machine Learning Advisor to LabGenius.

“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.”

Specifically, LabGenius is using IPUs from Bristol, UK-based Graphcore to train a BERT Transformer model on a large data set of known proteins to predict masked amino acids. This, the company says, enables the model to effectively learn the basic biophysics of proteins.

“[The system] is looking across different features we could change about the molecule — from point mutations of simpler constructs to the overall composition and topology of multi-module proteins,” explained Tom Ashworth, Head of Technology at LabGenius.

“It’s making suggestions about what to design next… to learn about a change in the input and how that maps to a change in the output.”

One in two people now develop cancer in their lifetime. Current treatments often cause much suffering themselves and, while survival rates for most forms are increasing, only around 50 percent survive for ten years or more.

AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. However, while discovering new cancer treatments is the current focus of LabGenius, the company notes how the principles can be applied more widely to find new treatments for other horrible diseases that plague mankind.

“Graphcore has changed what we’re able to do, accelerating our model training time from weeks to days,” adds Ashworth.

“For our data scientists, that’s really transformative. They can move much more at the speed they think.”

(Photo by National Cancer Institute on Unsplash)

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The candidate was identified by scientists who used BenevolentAI’s target ID tools and machine learning models to identify and experimentally validate a novel biological target. Impressively, the achievement was made without any prior reference in published alliteration or patents linking the gene to ulcerative colitis.

Anne Phelan, Chief Scientific Officer at BenevolentAI, said:

“Ulcerative colitis is a chronic, lifelong disease that affects 0.2% of the US population alone and 1.6 million patients in the seven major markets, yet it is poorly served by the standard of care therapies.

Our novel preclinical candidate addresses the high unmet need for an oral, safe and efficacious therapy and has demonstrated improved safety and tolerability profile compared with other leading IBD treatments.

We are actively using patient-derived molecular descriptors to target patient subgroups that will optimise trial design and further increase our probability of success.”

Following the identification of the candidate, BenevolentAI’s molecular design capabilities were used to generate a potential oral and peripherally-restricted candidate drug. The preclinical candidate has been experimentally validated in ex-vivo ulcerative colitis colon samples from patients who didn’t respond to current treatments.

Joanna Shields, Chief Executive Officer of BenevolentAI, commented: “Nominating a drug candidate for a novel ulcerative colitis target identified by our AI-drug discovery platform represents a milestone for BenevolentAI but, more importantly, advances a new potential treatment for this debilitating disease.” 

BenevolentAI plans to advance the asset into clinical trials in early 2023.

(Photo by National Cancer Institute on Unsplash)

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