Drug Discovery Has No Magic Wands - by Daphne Koller

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Drug Discovery Has No Magic Wands<br>On AI, human biology, and what it will actually take to discover transformative new medicines

Daphne Koller<br>Aug 03, 2026

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Welcome to Deep Phenotype, insitro’s new publication exploring the ideas, evidence, and provocations shaping the future of artificial intelligence, biology and drug discovery. We’re launching with a two-part manifesto from insitro founder and CEO Daphne Koller on why the path to transformative medicines begins with causal human biology. A special thank you to our friends at a16z for their partnership and for cross-posting our debut article. Subscribe to follow along.

The tech world has latched onto an intoxicating promise: build a superintelligence, and it will cure cancer, and every other disease as well. The logic is seductive. The human body is a system we can already read from and write to, so like other knowledge problems, a powerful enough AI should be able to solve disease.<br>I fully believe that AI will eventually transform human health. It is why I’ve spent close to 30 years working at the intersection of AI and biology, and the last decade in drug discovery. The need is staggering: by most counts only a quarter of diseases — and by some estimates a few percent — have an approved therapy, and most of those merely slow a disease rather than stop it. For the majority of human illness, medicine still has little to offer.<br>But the magic-wand promise rests on an assumption that turns out to be false: that we already understand human biology well enough for a clever enough reasoner to find the cures hidden in what we know. We don’t. Hundreds of years into modern medicine, our understanding of most human disease, and much of healthy physiology, is best captured by the parable of the blind men and the elephant; in this case, a really huge elephant. AI is undoubtedly extraordinary, but aimed at a biology we have only begun to measure and barely understand, it will mostly help us generate failures faster.<br>This essay is about what that actually takes. The path to real cures starts at the root of the problem: measuring biology in the right way and using AI to derive novel insights from those measurements. Below, I describe some of the most prevalent AI Magic Wand narratives, explain the key pitfalls, and offer my perspective on what we actually need to deliver on this important goal.<br>Three Problems, One Bottleneck

To understand where AI fits, it helps to decompose drug discovery into its three essential stages:<br>Disease-to-mechanism: Identifying a biological mechanism — a pathway, a target, a molecular interaction — where therapeutic intervention will alter the course of disease in humans.

Mechanism-to-drug: Creating a molecular intervention in the right therapeutic modality — a small molecule, antibody, siRNA, gene therapy — that achieves the desired mechanistic effect with acceptable safety and pharmacological properties.

Drug-to-patient: Designing a clinical development program that identifies the right patients and assesses the molecule’s effects — beneficial as well as adverse.

The vast majority of AI work in drug discovery has focused on stage 2. This is understandable: the origin of the AI Magic Wand exuberance is the incredible achievement of AlphaFold, a field-defining tour de force. From this starting point, we have seen an explosion of AI tools capable of designing novel proteins, small molecules, RNA therapies, and even gene therapies. Given a biological mechanism we want to hit, it seems that AI can now design a molecule to hit it faster and better than ever before.<br>AI will certainly generate new and better molecules at an unprecedented rate, but will it generate drugs that unlock diseases for which there is currently no meaningful treatment? There are “undruggable targets” — high-confidence mechanisms that historically we have been unable to hit. The success against KRAS, the quintessential undruggable target, shows that this journey is possible. Notably, this success emerged from decades of structural biology and medicinal chemistry, not AI; as of now, I don’t know of a single example of an AI-derived insight that has led to “drugging the undruggable.” More broadly, the biggest step functions in our ability to drug the undruggable have historically come not from better molecular design tools, but from expanding our repertoire of therapeutic modalities: first biologics, then siRNA and antisense oligonucleotides, then gene editing. Each new modality opened a class of targets that was simply inaccessible before.

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But an even more critical point: validated yet undruggable targets are a tiny handful in the landscape of unmet need. For the vast majority of diseases without effective treatments, we simply have no idea what the right mechanism is. More than 90% of drugs that enter clinical trials fail — a dismal statistic...