For the past few years, protein-structure AI has been on a one-way sprint from “amazing science demo” to “drug discovery workhorse.” AlphaFold 2 cracked the big public milestone—predicting protein structures at scale. AlphaFold 3 pushed the frontier into interactions, showing how proteins might bind to other molecules, including potential drugs.
Now comes the next jump—except it’s happening behind closed doors.
Google DeepMind’s biopharma spin-off Isomorphic Labs has revealed a new proprietary system it calls IsoDDE (its “drug-discovery engine”). Researchers who work on open tools describe it as the kind of leap they’d expect from a hypothetical “AlphaFold 4.” The catch: Isomorphic isn’t releasing the model, and the accompanying report offers only limited clues about how it achieves its results.
The result is a strange moment in science: a clear advance that many researchers can’t reproduce, audit, or build on—at least not directly.
What Is IsoDDE, and why are people calling it “AlphaFold 4”?
Isomorphic Labs is based in London and positions itself as an AI-first drug company. Its new system, IsoDDE, was described in a 27-page technical report released on February 10.
The headline capabilities are exactly what pharma wants from modern biology AI:
- More precise predictions of how proteins interact with drug-like molecules
- High-end antibody modeling, including how antibodies interact with their targets
That matters because antibodies underpin a massive chunk of modern therapeutics—and because predicting “does this molecule bind?” is only the beginning. The hard part is how strongly, how reliably, and whether the model generalizes beyond the kinds of molecules it saw during training.
Researchers in the field say IsoDDE appears to do unusually well on those harder questions.
The feature everyone is watching: binding affinity, but faster—and (claimed) better
One of the most valuable numbers in drug discovery is binding affinity: how tightly a potential drug “sticks” to its protein target. Traditionally, estimating this well can be computationally expensive, using physics-heavy methods that are slow and costly at scale.
The open-source world has been catching up here. A model called Boltz-2 (from researchers at MIT and released last year) added the ability to predict binding affinity—pushing beyond “shape prediction” into “drug-likeness reality.”
Isomorphic claims IsoDDE goes further: outperforming Boltz-2 and outperforming physics-based approaches at predicting binding affinity.
If that holds up broadly, it’s a big deal—because it means faster iteration, fewer dead-end molecules, and better prioritization before wet-lab work begins.
The “wow” detail: it may generalize to molecules it didn’t train on
One scientist’s reaction captures why people are impressed: IsoDDE reportedly performs well even when asked to predict drug–protein interactions for molecules that look very different from its training examples.
That kind of generalization is the difference between:
- a model that’s “great on benchmarks,” and
- a model that’s genuinely useful in messy, real-world discovery.
And it’s also the clue that something novel is happening under the hood—because generalization is where many models break.
The problem: the best hints are… basically “compute, data, algorithms”
Isomorphic’s leadership says the models behind IsoDDE are “profoundly different” from other efforts. But the company has been clear: it does not plan to reveal the secret sauce.
What it does say sounds familiar to anyone who watches frontier AI:
- lots of compute
- lots of data
- better algorithms
- and a pipeline that mixes public data with synthetic data and licensed sources
That’s not a critique—it’s just not enough detail for independent researchers trying to replicate the results.
And that gap matters because open science runs on three things:
- reproducibility
- peer scrutiny
- iteration by the community
A closed model can still be real. It can still be great. But it changes the ecosystem: instead of “everyone builds,” you get “a few labs race.”
Did proprietary pharma data make the difference?
One of the most pointed reactions comes from within industry: if a model has access to large volumes of private structural data—through partnerships or deals—its performance could be boosted in ways outsiders can’t match.
Isomorphic’s report arrives after significant efforts to partner with pharma companies and potentially access private data. Critics note that makes it hard to judge how much of the gain comes from:
- new modeling breakthroughs
vs. - privileged data access
Isomorphic says it has built different versions of IsoDDE for different contexts (partners vs. internal), incorporating different data sources.
Meanwhile, leaders in the open-model community argue they’re still seeing big gains using mostly public data—and that Isomorphic’s results should be treated as a baseline to catch and surpass, not an unreachable wall.
The business angle: the model is private because the company is building drugs
Isomorphic isn’t acting like a university lab. It’s operating like a biotech company with a serious commercial strategy.
It has already signed major drug-development collaborations with Johnson & Johnson, Eli Lilly, and Novartis, and it says it has an internal pipeline with clinical trials on the horizon.
In that world, a proprietary model isn’t just research—it’s competitive advantage.
So the real tension is structural:
- Science wants openness to accelerate discovery.
- Companies want exclusivity to capture value.
Both are rational. Together, they create friction.
What this means for the next phase of “AI-first drug discovery”
Whether IsoDDE becomes the dominant engine or simply the first in a new wave, the shift is clear:
1) Drug discovery AI is moving from “structure” to “interaction and strength”
Not just “what does the protein look like,” but “what binds, how well, and in what ways.”
2) The field is splitting into open tools and closed super-tools
Open communities will keep improving—and they’ll likely innovate in ways closed labs can’t predict. But closed labs may stay ahead if they can combine scale, partnerships, and private data.
3) Reproducibility becomes the battleground
If the biggest jumps happen behind walls, independent validation becomes harder—and confidence becomes more dependent on trust, reputation, and downstream results (like actual drug candidates).
Bottom line
IsoDDE looks like a real leap in AI-driven drug discovery—strong enough that experts are comparing it to the next generation of AlphaFold. But it also highlights a new reality: some of the most powerful biology models may never be public.
