I companies promise that the tech's applications in sciences are practically boundless. New research from Google DeepMind may start to live up to that promise.
On Wednesday, the company's AI lab unveiled AlphaGenome 2, the second generation of its DNA sequence model, aimed to help scientists research the cellular causes of disease. The model represents a "step-change improvement" in predicting genetic variations compared to its predecessors, allowing researchers to predict genetic mutations in specific cells, including those the model is unfamiliar with.
The model's previous iteration, AlphaGenome, was released last year and was able to predict how variants of DNA could impact molecular activity across broad tissues. AlphaGenome Atlas, released in September, used this to create predictions for every possible single-letter change in the human genome, totaling 9 billion, making it the most comprehensive catalogue yet of how genetic mutations impact molecular biology and allowing biologists to potentially identify harmful mutations.
AlphaGenome 2 builds on this work, improving in a number of areas:
- The model can identify nearly twice as many genetic variants that change gene expression in cells compared to the previous model, scoring a 30% in identifying causal variants compared to the original AlphaGenome's 16%.
- It's also able to make more precise predictions, as it can investigate specific cell types rather than just broad tissue categories, and can make predictions about genetic behaviors based only on a small sample of cells.
- AlphaGenome 2 is also trained on a massive well of data, learning from 107 million individual cell profiles across 68 different types of tissues.
Though the model is only available publicly for non-commercial research and in private preview for commercial use cases, it could aid researchers in discovering which genetic mutations are linked to disease at a cellular level, helping speed up research and develop new drugs. It's also one of several efforts from Google to embed AI within science, with its previous track record including disease research on breast cancer and tuberculosis and extreme weather mapping.
Our Deeper View #
Science, particularly life sciences and health, are commonly used as an arguing point by AI industry leaders to justify the investment of so many resources into AI. For instance, Anthropic CEO Dario Amodei said in a recent podcast that AI could make things like cancer, Alzheimer's, heart disease and mental illness "relics of the past," as the tech could generate cures for a broad swathe of diseases. However, when faced with this reasoning over and over again, it could be argued that using health as a bargaining chip to vindicate creating increasingly powerful AI is manipulative, playing into humanity's drive to live longer, healthier lives. While it's undeniable that this tech can, and has, facilitated innovative research in health, as shown by Google DeepMind's latest delivery, it shouldn't be treated as a panacea for all of humanity's ailments, nor should these innovations be used to justify unchecked growth by frontier labs.