The computational biologist Bruno Correia used to have a rule in his lab: No machine learning allowed. He didn’t consider it real science. Now Correia has used it to detect potential interactions between proteins — the complex folded molecules responsible for many biological processes — 40,000 times faster than conventional methods. The journal Nature Methods featured his system on its cover in February 2020. Correia said of his early reluctance to embrace machine learning, “I was wrong, and I’m glad I was wrong.”
What changed his mind? Geometric deep learning: an emerging subfield of artificial intelligence that can learn patterns on curved surfaces.
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