Researchers at the Icahn School of Medicine at Mount Sinai have identified a previously hidden druggable site in a cancer-related protein that could open the door toward the development of a new generation of more precise cancer drugs. The finding also reveals important limitations in today’s artificial intelligence tools for drug discovery. The study, published in the June 2 online issue of the Journal of the American Chemical Society [10.1021/jacs.6c05178], focused on PKMYT1, a type of protein known as a kinase that helps control how cells grow and divide. Because this process can go wrong in cancer, PKMYT1 has emerged as a promising target for new cancer drugs.

Drug delivery and diagnostic imaging often lack specificity, but a new “TRACE” method lets specially‑caged compounds stay inert until a target cell’s enzymes quickly remove the cage, potentially allowing for more precise drug delivery and sharper diagnostic imaging.

A new study in Science Bulletin presents DVSTP, a deep learning system that integrates pathology images with spatial transcriptomics and proteomics to map intra-tumor heterogeneity. DVSTP predicts molecular profiles from routine pathology slides, making spatial multi-omics more accessible. Whole–tumor 3D reconstruction reveals that SRSF6 drives immune exclusion and is associated with poor clinical outcomes.