New AI model for drug design brings more physics to bear in predictions

New AI model for drug design brings more physics to bear in predictions

When machine learning is used to suggest new potential scientific insights or directions, algorithms sometimes offer solutions that are not physically sound.

To limit this type of unphysical result in the realm of drug design, Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences at Caltech, and her colleagues have introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm's performance.

"We see a lot of machine learning fail in coming up with accurate results on new examples that are different from training data, but by incorporating physics, we can make machine learning more trustworthy and also work much better," says Anandkumar.

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