- AWS Unveils New AI Drug Discovery Tool
- Amazon Bio Discovery Removes Technical Barriers to Highly Computational AI Experiments
- Tool can significantly reduce drug testing times
A new AI-powered drug discovery tool has been launched by Amazon Web Services (AWS).
The Amazon Bio Discovery tool helps researchers accelerate the discovery of new drugs by giving scientists the ability to run complex computational workloads without the need for technical expertise.
Amazon’s cloud platform touts these tools as being able to reduce the time of an antibody design workflow from 12 months to just weeks.
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AI accelerates drug discovery
Amazon Bio Discovery provides a catalog of specialized fundamental models for drug discovery, with the ability for scientists to download third-party models. Of course, the tool would not be complete without an AI agent, which can guide users in selecting the appropriate models and parameters for their search.
When the experiment is ready to start, the AI agent begins searching through data sources and fundamental biological factors – and it even provides scientific references and rationales for its predictions and suggestions.
The tool then filters the results down to the first 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 transfer that can cause delays. Lab test results are then automatically fed back into Amazon Bio Discovery for additional analysis.
Continuous exchanges between integrated laboratories and researchers allow results to be quickly refined, thereby 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 first 100,000 and sent them for testing “in a matter of weeks versus up to a year using traditional design methods.”
AWS also collaborated with Gray Lab at the Johns Hopkins Whiting School of Engineering to produce the “Antibody Development Benchmark” – the “largest and most diverse” antibody dataset designed to help evaluate AI-guided antibody design.
Luca Giancardo, an applied scientist at Amazon Web Services, said: “This dataset will allow researchers to confidently answer ‘Which model is best suited for our purposes?’. Today, many computational models are published and are mainly evaluated on proprietary data or public datasets, which are not representative of the heterogeneity of antibodies. This means that it is very, very difficult, if not impossible, to decide which is better or worse.”
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