Amazon’s new AI Bio Discovery tool can provide ‘every researcher’ with ‘lab-in-the-loop drug discovery’ – 40+ AI biology models can filter 300,000 novel antibody candidates down to the top results for testing in just weeks | Daily Reports Online
- AWS reveals new AI drug discovery tool
- Amazon Bio Discovery removes the technical barriers to high computational AI experiments
- The tool can cut drug testing times significantly
A new AI powered drug discovery tool has been launched by Amazon Web Services (AWS).
The Amazon Bio Discovery tool helps researchers speed up the discovery of new drugs by providing scientists with the ability to run complex computational loads without the need for technical expertise.
Amazon’s cloud platform touts the tools as being capable of reducing the timescale for an antibody design workflow from 12 months to just weeks.
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AI speeds up drug discovery
Amazon Bio Discovery provides a catalog of foundational models specialized for drug discovery, with the option for scientists to upload models from third parties. Of course, the tool wouldn’t be complete without an AI agent, which can guide users through selecting the right models and parameters for their research.
When the experiment is ready to start, the AI agent begins searching through data sources and foundational biological factors – and it even provides references and scientific rationale for its predictions and suggestions.
The tool then filters down the results to the top selection of results which can then be sent to one of Amazon’s integrated lab partners for synthesis and testing without the need for a manual handover that can cause delays. The results from lab testing are then automatically fed back into Amazon Bio Discovery for additional analysis.
The continuous back and forth feedback between the integrated labs and researchers allows for rapid fine-tuning of results, speeding up the time between design, testing, and synthesis.
In collaborative testing with Memorial Sloan Kettering Cancer Center, Amazon Bio Discovery helped narrow down a selection of 300,000 antibody candidates to the top 100,000 and sent them for testing “in weeks versus up to a year using traditional design methods.”
AWS also collaborated with Gray Lab at Johns Hopkins Whiting School of Engineering to produce the ‘Antibody Developability Benchmark’ – the “largest and most diverse” antibody dataset designed to help evaluate AI-guided antibody design.
Luca Giancardo, an applied scientist with Amazon Web Services said, “This dataset will allow researchers to confidently be able to answer ‘Which model is better suited for our purposes?’. Today there are many computational models coming out that are mostly evaluated on either proprietary data or public datasets, which are not representative of antibody heterogeneity. That means deciding what is better or worse is very, very hard — if not impossible.”
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