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Personalized cancer medicine has advanced from a distant hope to a clinical reality. Oncologists regularly individualize treatments to target a tumor's unique genetic weaknesses. But because these personalized medicines reach healthy tissues and tumors alike, even the most targeted treatments can cause unwanted side-effects. A new approach devised by nanotechnology experts at the Sloan Kettering Institute (SKI) at Memorial Sloan Kettering Cancer Center may improve the precision of personalized medicines by helping them avoid collateral damage. "We found a way to use machine-learning algorithms to design powerful nanomedicines that can deliver a stronger, safer, more personalized punch," says Daniel Heller, PhD, a chemist in the molecular pharmacology program at SKI and an assistant professor at the Weill Cornell Graduate School of Medical Sciences.

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