Can computational biology cure cancer?
AI models such as DeepMind's C2S-Scale and Tufts' MultiXVERSE are demonstrating the ability to uncover novel biological insights and drug candidates, although regulatory approval remains elusive due to the inherent complexity of human biology.
Joel Miller
Google DeepMind announced this week that a specialised model, C2S-Scale, discovered a novel cancer therapy, validated through laboratory experiments on human cells. The 27 billion parameter model, built on the familiar open-source Gemma architecture, identified that silmitasertib could amplify immune responses in specific cellular contexts, something no researcher had previously considered.
C2S-Scale converted each cell’s gene expression data into ordered lists, where position indicates expression level. After processing 57 million cells and over a billion tokens, it learned that certain gene reordering only occurs in specific immune contexts. When asked to find drugs that boost antigen presentation, it recognised a pattern humans had missed: silmitasertib plus low-dose interferon creates a synergistic effect that makes tumours visible to the immune system. Researchers at Yale are now exploring the mechanism and testing additional AI predictions. What’s striking is how quickly this moved from computational prediction to laboratory validation, suggesting these models are generating genuinely testable hypotheses rather than statistical noise. Given this is a small model, one wonders if scaling these kinds of solutions up to current frontier sized trillion parameter models will bring more progress. For now it’s possible to download the code and explore this discovery on your laptop.
This pattern-finding capability isn’t limited to single-domain models. Earlier in June, researchers at Tufts University published MultiXVERSE, a universal network embedding method that discovered an unexpected causal link between GABA neurotransmitters and cancer formation. The system analysed molecular, drug, and disease interactions across multiple network layers simultaneously, generating a hypothesis that researchers then validated experimentally using Xenopus laevis tadpoles. What makes MultiXVERSE particularly interesting is its ability to handle any type of multilayer network, from molecular pathways to social systems. The GABA-cancer connection it identified had been hiding in plain sight across thousands of published datasets; it took AI’s ability to traverse these complex network spaces to spot the relationship.
And yet whilst promising these pattern-finding breakthroughs do not guarantee large scale outcomes. Despite a decade of progress, not a single AI-discovered drug has received regulatory approval. The industry’s 90% clinical trial failure rate persists, with AI candidates showing early promise in Phase I trials only to stumble in Phase II. The problem isn’t necessarily the AI; it’s that human biology remains stubbornly mysterious. As one industry veteran puts it, drug discovery is “probably the hardest thing mankind tries to do”. Even with perfect pattern recognition, toxicity and side effects remain nearly impossible to predict. The successful companies appear to be those with patient capital and massive computing power, like Google’s Isomorphic Labs, which has raised $600 million this year and can afford to wait decades for results whilst building what they call a “generalisable drug design engine”.
The true endpoint of this pattern-finding capability may be complete cellular simulation. The Chan Zuckerberg Initiative launched rBio in August, an AI model that learns from virtual cells rather than laboratory experiments, whilst the Arc Institute announced a $100,000 prize for models that predict genetic perturbations. Google’s Demis Hassabis has declared building a virtual cell as a primary goal. These aren’t traditional mathematical models with manually coded equations; they’re AI systems learning directly from billions of real cellular observations. Stephen Quake at CZI describes the ambition starkly: flip biology from 90% experimental to 90% computational. Early results suggest this isn’t fantasy. CZI’s models can already predict cellular responses across species they’ve never encountered, whilst Stanford’s virtual yeast cells predict metabolic disruptions with high accuracy. If successful, scientists could test thousands of drug candidates computationally before touching a pipette, fundamentally changing how we approach biological research.
Recent months have seen an increase in signals that AI can help drive scientific progress. LLMs are universal translators for complexity, finding patterns in spaces too vast for human cognition. The combination of widespread access to reasoning models and available compute for scaled experiments is enabling this progress. And language, it seems, encodes more than human communication; it captures patterns of reality itself. The immediate future will likely bring more such discoveries as researchers apply similar approaches to other scientific domains and exploit ever advancing techniques. The computational infrastructure is now in place: open models aplenty, massive computational resources, and growing acceptance of AI-generated hypotheses. This feels like a mini-breakthrough moment, hopefully the first of many.
Takeaways: AI has started to cross the threshold from assistant to discoverer, not through reasoning but through pattern recognition at scales humans cannot achieve. Progress suggests we we’re entering an era where major scientific advances come from AI spotting signals in noise we didn’t know existed and transitioned scientific discovery from experimental to computational. The bottleneck isn’t the AI anymore; it’s having enough experts to test what the models discover and the courage to pursue counterintuitive findings.
