Every target decision is, at bottom, a choice between two bets. Be first-in-class on a target nobody has reached, or best-in-class on one the whole field is already chasing. A quarter of the industry pipeline now sits on a few dozen molecular targets, so most teams are, knowingly or not, choosing the second. Both bets can win. But you cannot choose well between them without seeing the whole field: where the crowd is, and where the open space is. Closing that gap, seeing the open space as clearly as the crowd, is becoming one of the defining problems in early research.
The crowd is easy to picture. More than 5,600 clinical trials have tested PD-1 or PD-L1 inhibitors. One target class, thousands of programmes, enormous overlap. Pick almost any validated pathway and the pattern repeats. None of this is irrational. A clinically validated target offers a clearer development path, lower scientific uncertainty, and a higher base rate of success. Every team that piles onto it is making a defensible individual choice.
The trouble is what those individually sensible choices add up to. Effort concentrates, differentiation compresses, and biology that could matter for patients with few options goes unexplored because no early signal is pulling anyone toward it. The field becomes highly optimised around what is already known and under-invested in what is still to be found.
Crowding is usually treated as a strategy problem: be braver, look further, take on harder biology. That framing is incomplete, because it skips the mechanism that creates the crowd in the first place.
Most target ranking rewards how much a gene has already been studied. The more a target is worked on, the more papers accumulate, the more associations are reported, and the safer it looks on any view that weights evidence by volume. That safety then attracts more work. It is a feedback loop, and its quiet consequence is that the targets which look least risky on a literature-weighted view are, almost by construction, the most crowded. Ranking by how well-studied a gene is will always point back into the crowd.
So the teams that want open space are not short on ambition. They are short on a way to see the open space as clearly as they can see the crowd. Counting trials on a known target is straightforward. Pointing, across roughly 34,000 diseases and the whole protein-coding genome, at the disease-driving biology that nobody is on yet is the hard part. That is a measurement problem, and it has to be solved before the strategy conversation can even start.
The way out is to rank candidates on causal evidence drawn from primary biological data, independent of how often a gene appears in the literature. Genetic causal estimates from Mendelian randomisation on GWAS effects. Perturbation responses from CRISPR and compound screens. Single-cell expression in the tissue where the disease acts. When candidates are scored this way, they surface because the biology converges, not because the citation count is high.
This is also where open space and quality stop being a trade-off. Targets with human genetic support are roughly twice as likely to reach approval, so the causal signal that points away from the crowd is the same signal that raises the odds of success. Going where others are not does not mean lowering the evidence bar. Done on primary data, it can mean raising it.
Cell-type resolution sharpens the picture further. Crowding is almost always discussed at the level of the gene. Disease biology is finer than that. The same target can be a bystander in one cell type and a driver in another, and the open space often sits one layer down, in which cell type is carrying the disease. A ranking that resolves to cell type can separate the crowded gene from the uncrowded mechanism inside it.
A useful shift is already underway in how leaders talk about this: crowding risk and differentiation risk are being named as their own categories, separate from scientific and clinical uncertainty. That language only helps if the numbers behind it stay separate too.
The instinct in most scoring systems is to blend everything into one priority figure. A single number is easy to sort. It also hides the decision. How strong the causal biology is, how tractable the target is with today’s modalities, and how crowded it already is are three different questions with three different answers. Collapse them into one score and you have thrown away the exact trade-off a portfolio team is there to weigh. Keep them as separate, calibrated axes and the trade-off stays on the table, where a researcher can look at a candidate with strong biology in an uncrowded space and defend the choice to advance it.
That is the difference between a tool that hands you a ranking and a tool that helps you make, and defend, a decision.
We ran this across the genome. Of 43,084 scored genes, 95% carry no drug programme at all, and every drug programme in the dataset sits within the most-crowded 4.5% of targets. The top 1% of targets, 431 genes, hold three-quarters of all drug activity. That is the crowd, measured.
The open space is not empty of biology. 1,333 genes are a top causal driver in 25 or more diseases, score as tractable, and carry low predicted safety risk, while no drug programme has touched them. Strong biology, no crowd, and a credible path to a molecule. These are not picked from a literature search. They fall out of ranking the whole genome on primary data across 204 million gene-disease pairs. The two charts with this piece show both halves: how drug programmes pile onto a sliver of targets, and the reservoir of strong-biology, uncrowded genes sitting behind it.
This is where the choice gets made on evidence rather than instinct. The same Atlas that surfaces the uncrowded, strong-biology candidates also tells you, for a target you are already weighing, exactly how crowded it is and whether the causal biology is strong enough to justify a best-in-class run into a busy field. First-in-class is the open space. Best-in-class is a crowded space you enter with your eyes open, because the biology is worth the fight. Both are defensible bets. Choosing between them blind is not, and that is what most teams are still doing.
Disease Atlas by Euretos was built around this idea: map disease biology from organ to cell type, rank the full genome on causal and molecular evidence rather than on literature volume, and keep the evidence axes separate so the researcher can see where the open space is and stand behind the move into it. Every score traces back to its source.
The crowd will always be visible. It advertises itself in trial counts and press releases. The open space does not advertise. It has to be measured, on the primary biology, across every disease, down to the cell type. The teams that can do that with confidence are the ones who will keep finding the next target before it becomes the next crowd. First-in-class or best-in-class, the choice starts the same way: by mapping the whole field.
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