重磅！AI制药独角兽创始人反思：行业该醒醒了，真正瓶颈不在“造分子”！

TL;DR — insitro founder/CEO Daphne Koller argues in a widely-discussed essay that AI's real bottleneck in drug development is not molecule generation but identifying correct disease mechanisms, and she critiques four popular "AI magic wand" narratives (superintelligence, molecular design tools, virtual cells, autonomous AI labs).

• Drug R&D splits into three stages (disease→mechanism, mechanism→drug, drug→patient); most AI work targets stage 2 post-AlphaFold, yet >90% of clinical trials fail mostly because the targeted mechanism is wrong, not the molecule. Historic wins on "undruggable" targets (e.g., KRAS) came from decades of structural biology and new modalities (biologics, siRNA/ASO, gene editing), not AI design.
• Misallocation is visible: 38 targets each carry 50+ programs (e.g., GLP-1 variants), while industry's new targets per year fell from ~100 in 2015 to ~30 in 2024.
• LLM-over-literature reasoning assumes the needed human-biology data already exists; Koller counters that existing cell atlases (hundreds of millions of cells) cover a tiny fraction of perturbation space, are limited to few cell lines, and lack causal perturbation data — and system-level, human-specific diseases (Alzheimer's, ALS) don't transfer from mouse/NHP models.
• Agentic self-driving labs work only with fast, cheap, objective feedback (like compilers for code); the sole ground truth in medicine is human clinical trials — years, millions of dollars, ethically constrained — so accelerating stage 3 without correct mechanisms just fails faster. The productive path is mechanism-derived clinical biomarkers for patient selection, target engagement, and early efficacy signals.