🔬 Training Transformers to solve 95% failure rate of Cancer Trials — Ron Alfa & Daniel Bear, Noetik
Apr 20, 2026 · 1:25:22
Noetik founders Ron Alfa and Daniel Bear argue that 95% of cancer drugs fail in clinical trials not because of pharmacology but because of poor patient selection; their thesis is that the right patients exist but aren't identified. To solve this, Noetik generates its own multimodal data—pathology H&E, spatial transcriptomics with up to 20,000 genes, and protein stains—from human tumor samples, building self-supervised foundation models like OctoVC and the autoregressive Tario. They claim a data moat: over a hundred million spatially resolved cells, an order of magnitude more than any public dataset, which drives better generalization across cancer types. The platform validates predictions via PerturbMap, an in-vivo mouse model with multiplexed CRISPR knockouts, and an ‘in silico humanization’ that reads mouse H&E in human gene space. A $50M deal with GSK licenses OctoVC for therapeutic discovery and fine-tuning on GSK's own data, marking a rare software-focused licensing deal in biotech. Noetik bets that patient-level tissue modeling, not subcellular simulation, will first deliver clinically actionable insights.
