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DeepMind has released AlphaFold 4, pushing protein structure prediction into a new era.

The updated model handles:
• ~20,000 human proteins
• multi-chain complexes
• protein–protein interactions
• selected post-translational modifications

Reported accuracy reaches ~98% on benchmark datasets — approaching experimental resolution in many cases.

📄 Preprint (updated Feb 18, 2026):
https://arxiv.org/abs/2402.18567

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🧪 Why this matters

This is no longer just about predicting isolated protein folds.

AlphaFold 4 moves toward modeling biological systems — complexes, assemblies, interaction interfaces — the level where real drug discovery happens.

Targets long considered “undruggable,” such as:

• KRAS
• MYC

may become structurally tractable thanks to improved interface prediction.

Pharma companies are already integrating AI-generated structures into drug pipelines, potentially shortening early-stage discovery timelines dramatically. (Not “10 years → 2 years” overnight — but the structural bottleneck is shrinking fast.)

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🔬 Bigger picture

If AlphaFold 2 solved the protein folding problem,
AlphaFold 4 begins solving the interaction problem.

Structural biology is shifting from slow, expensive crystallography toward AI-assisted molecular design.

We are watching the transition from “map the molecule” to “engineer the molecule.”

The question now isn’t can we predict structure?
It’s how fast can we turn structure into therapy?

#AlphaFold #AI #DrugDiscovery #Biotech #ComputationalBiology