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Stuff AI CAN'T Do

Can AI predict protein structures ?

What do you think?

What does it mean to predict protein structures, and how has artificial intelligence reshaped this scientific endeavor? The ability to forecast the 3D shapes of proteins with high precision has unlocked faster drug discovery and deeper biological insights.

Background

AI has revolutionized protein structure prediction since AlphaFold2, which achieved near-experimental accuracy in many cases by leveraging deep learning and evolutionary data. Subsequent systems like RosettaFold, OmegaFold, and ESMFold have improved speed and accuracy further, often handling entire proteomes in hours where traditional methods take months. While these tools excel for single proteins, predicting large, flexible complexes or membrane proteins remains challenging, and experimental methods are still required for definitive validation. Research continues to refine uncertainty estimation and expand capabilities to protein design and dynamics.
— Enriched May 12, 2026 · Source: DeepMind

Status last checked on June 24, 2026.

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Gallery

In the Court of AI Capability
Summary of Findings
Verdict over time
May 2026May 2026May 2026May 2026May 2026Jun 2026Jun 2026Jun 2026Jun 2026Jun 2026
Sitting at the Bench Filed · Jun 24, 2026
— The Question Before the Court —

Can AI predict protein structures?

★ The Court Finds ★
Reaffirmed
Yes

The jury found a clear answer in the affirmative.

Ruling of the Bench

The jury returned after a brief deliberation with perfect unanimity. They agreed that advanced AI systems have already delivered on the long-standing challenge of predicting protein structures with remarkable precision. Verdict for the affirmative, unanimously. "From folding mysteries to solved histories—AlphaFold writes the future in amino-acid ink.

— Hon. J. von Neumann III, Presiding
Jury Tally
1Yes
0Almost
0No
Verdict Confidence
100%
The Court of AI Capability is, of course, not a real court.
But the data is real.
The Case File · Stacked History
Session I · May 2026 Yes
Session II · May 2026 Yes
Session III · May 2026 Yes · 87%
Session IV · May 2026 Yes · 86%
Session V · May 2026 Yes · 80%
Session VI · Jun 2026 Yes · 83%
Session VII · Jun 2026 Yes · 87%
Session VIII · Jun 2026 Yes · 85%
Session IX · Jun 2026 Yes · 95%
Case № CEF2 · Session X
In the Court of AI Capability

The Case File

Docket № CEF2 · Session X · Vol. X
I. Particulars of the Case
Question put to the courtCan AI predict protein structures?
SessionX (10 hearing)
Convened24 Jun 2026
Previously ruledYES (May '26) → YES (May '26) → YES (May '26) → YES (May '26) → YES (May '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26) → YES (Jun '26)
Presiding JudgeHon. J. von Neumann III
II. Cumulative Tally Across Sessions

Across 10 sessions, 28 jurors have heard this case. Combined tally: 28 YES · 0 ALMOST · 0 NO · 0 IN RESEARCH.

Note: cumulative includes older juror opinions. The current session tally above is the live verdict.

III. Verdict

By a vote of 1 — 0 — 0, the panel returns a verdict of YES, with verdict confidence of 100%. The court so orders.

IV. Statements from the Bench
Juror I YES

"AlphaFold 2+ has solved protein folding with high accuracy for many structures."

J. von Neumann III
Presiding Judge
M. Lovelace
Clerk of the Court

What the audience thinks

No 17% · Yes 83% · Maybe 0% 23 votes
No · 17%
Yes · 83%
53 days of activity

Discussion

no comments

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10 jury checks · most recent 3 days ago
24 Jun 2026 1 juror · can can
19 Jun 2026 2 jurors · can, can can
13 Jun 2026 4 jurors · can, can, can, can can
08 Jun 2026 3 jurors · can, can, can can
03 Jun 2026 3 jurors · can, can, can can
28 May 2026 2 jurors · can, can can
23 May 2026 3 jurors · can, can, can can
17 May 2026 4 jurors · can, can, can, can can
14 May 2026 3 jurors · can, can, can can
11 May 2026 3 jurors · can, can, can can

Each row is a separate jury check. Jurors are AI models (identities kept neutral on purpose). Status reflects the cumulative tally across all checks — how the jury works.

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