AI for Bio has a Fuzzy API problem

Ankit Gupta

Blog

Contact

Github

AI for Bio has a Fuzzy API problem

Monday. May 04, 2026

“AI for bio” is getting hot again. Given the excitement in the current moment, I thought I’d share a bit about what actually makes biology uniquely hard as an application domain for machine learning. The reason is not simply that biology is complicated, though it obviously is. ML is good at many things that are complicated. The deeper reason is that drug discovery does not have the kind of clean feedback loops and clean interfaces that made modern ML so powerful elsewhere.

In software, we are used to clean APIs. One team can build a backend service, expose an endpoint, and another team can build on top of it. The interface is typed. The object either satisfies the contract or it does not. If something breaks, you can usually trace the failure to a bug, fix the code, rerun the test, and ship again. This is so much the case that billion dollar companies are regularly built satisfying exactly one interface (e.g. Supabase for databases, Exa for search, NVIDIA for GPU compute).

It is tempting to imagine drug discovery the same way:

target = target_discovery(disease)<br>drug = drug_design(target)<br>medicine = clinical_trial(drug)

Target discovery gives you a target. Drug design gives you a molecule. Clinical trials tell you whether it works. One company satisfies each interface.

Unfortunately biology does not expose clean APIs. The output of target discovery is not really a target. It is a probabilistic hypothesis that modulating some biological process, in some direction, in some tissue, in some patient subset, at some disease stage, will produce a useful clinical effect without unacceptable toxicity. The output of drug design is not really a drug. It is a candidate intervention whose value depends on whether the target hypothesis was right, whether the modality is appropriate, whether the molecule reaches the right tissue, whether it has enough selectivity, whether the safety margin is acceptable, whether it can be manufactured, whether it has a defensible IP position, whether the unknown competitive landscape materializes, and whether it fits a viable clinical strategy. The output of a clinical trial is not simply a “cure”. It is an outcome filtered through patient selection, endpoint choice, dosing regimen, site execution, statistical power, standard of care, and regulatory interpretation.

So the API is fuzzy. A target can look validated until the molecule hits it in the wrong tissue. A molecule can look great until it fails because the disease biology was wrong. An animal model can look convincing until the human disease is meaningfully different. A trial can look negative even though the drug might have worked in a narrower patient population. The downstream or upstream stages encode specific assumptions in other stages. To me, that is the core problem: AI for bio has a fuzzy API problem. In software, good APIs hide complexity. In biology, the hidden complexity inadvertently kills programs. This essay is about where that fuzziness shows up: target discovery, drug design, and clinical development, and where that poses both challenges and opportunities to use machine learning to revolutionize the field.

The discovery process

The first order, drug discovery involves designing an intervention that stops some sort of deleterious process occurring inside the body. In practice, modern therapeutic development usually looks something like this:

Determine the causal biology driving a disease in a particular patient population.

Design a chemical or biological intervention that modulates that biology.

Test that intervention in model systems (cell, animals, etc) to build evidence that it is safe and plausibly effective in humans.

Run clinical trials to determine whether it works in humans.

That clean list hides a lot. The intervention itself can take many forms, called modalities. They can involve small molecules that block a particular molecule, antibodies that deliver toxins to certain molecules, pieces of RNA that block protein design, in-vivo edits that permanently delete bad processes, methods for reprogramming the immune system to recognize a foreign body, and several others. Clinical development then generally proceeds through three phrases, testing various aspects of safety and efficacy in humans.

For the purposes of this post, I’ll simplify the whole process into three stages:

Target discovery : What biology should we modulate?

Preclinical Design and Translation design : What intervention can modulate it? Does the intervention look safe and effective enough in model systems to justify trying it in humans?

Clinical development : Does it actually help the intended human population?

Machine learning can matter in all three stages. But the type of ML that matters, and the difficulty of the problem, varies a lot by stage.

Target Discovery

When I talk about target...