Artificial intelligence models may be doing a lot more memorizing and a lot less reasoning when it comes to predicting biology, results from a new competition suggest.
Shortly after launching in April, a tiny startup in Utah called Leash Bio kicked off a challenge to test how accurately AI models predict molecules binding to specific protein targets. The results are in, Leash CEO Ian Quigley exclusively tells Endpoints News — and they aren’t good.
“No one did well,” said Quigley, summing up the results of about 2,000 teams that competed over three months.
For the competition, which ran on the data science competition platform Kaggle, Leash provided its training data, which consisted of lab results showing how millions of molecules bind — or don’t bind — to target proteins. Leash’s dataset is roughly 1,000 times larger than the largest publicly available database focused on protein-small molecule interactions, to Leash’s knowledge.
