Louise von Stechow

With DeepMind’s release of AlphaGenome—a deep learning model that can predict the relevance of variants in both coding and non-coding DNA—the so-called “dark genome” has taken center stage in biotech news. AlphaGenome, an “all-in-one” genome exploration model, is a unified deep learning model that can predict multimodal properties and variant effects from up to 1 million base pairs of DNA sequence. In the preprint first published on June 25, 2025, the authors showed that AlphaGenome outperforms current state-of-the-art models in the majority of tasks—and holds great potential to improve the interpretation of non-coding variants.

Rare diseases, which affect more than 300 million people worldwide, are often difficult to diagnose. Many patients wait five years or more for a correct diagnosis and are misdiagnosed at least once along this diagnostic odyssey. In addition to being a significant burden to patients’ well-being, low diagnostic rates also impact research and development in the rare disease space and challenge the success of marketed orphan drugs.

Drug development for rare (also referred to as orphan) diseases is notoriously challenging. Legislative initiatives, such as the 1983 US Orphan Drug Act and 1999 EU Orphan Regulation, provide regulatory and economic incentives to drug developers. Despite an increase in the number of annually FDA-approved drugs with an orphan designation from 2 in 1983 to 33 in 2020, more than 90% of rare diseases still lack effective therapies.