DeepMind published AlphaGenome, a long‑sequence DNA model that predicts regulatory activity and variant effects across up to one million base pairs, tackling the functional interpretation of noncoding 'dark' genome regions. AlphaGenome produces cell‑type specific predictions—such as transcription start sites, splicing, and RNA output—enabling rapid scoring of variant impacts for research and therapeutic target discovery. Developed as a successor to protein‑structure AI work, AlphaGenome integrates diverse experimental datasets to infer thousands of molecular properties and scores variants in about one second. The model is positioned for applications in rare variant interpretation, target identification, and synthetic regulatory design. Early reviewers recommend benchmarking AlphaGenome against independent datasets and existing variant‑effect tools; DeepMind has released versions for community use and encouraged cross‑validation. The publication signals another major AI resource aimed at enabling genomic interpretation at scales relevant to drug discovery and functional genomics. Translational teams will evaluate AlphaGenome for target deconvolution, CRISPR guide selection, and noncoding therapeutic designs; regulatory and clinical translation will require orthogonal experimental validation of model predictions.