Kan AI overgå mennesker i at forudsige protein-protein-interaktioner ?
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AlphaFold-Multimer og efterfølgere tog dette benchmark i 2024.
Background
Since 2021, deep-learning models have steadily improved PPI prediction by learning co-evolutionary signals and structural constraints from large protein sequence alignments. AlphaFold-Multimer (2021) and RosettaFold2 (2022) demonstrated top-1 accuracy near 70% on high-confidence heterodimers, surpassing template-based and physics-only baselines in head-to-head blind tests. By late 2023, newer pipelines such as ESM3-MSA and ProteinMPNN-CI combined large language models with geometric sampling to reach approximately 75–80% precision on human-vetted interactomes, though on smaller benchmark sets. At the same time, rare quaternary complexes and transient, disordered interactions remain problematic, with model precision dropping below 50% for certain immune synapse components. Community-wide assessments like CAMEO and EVfold continue to flag systematic failures where AI confidently predicts non-existent contacts or misses known binding modes, underscoring domain-specific limitations.
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Status senest tjekket July 2, 2026.
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Kan AI overgå mennesker i at forudsige protein-protein-interaktioner?
Snævre demoer findes — men panelet var ikke enigt.
Juryen var enige om, at AI har gjort bemærkelsesværdige fremskridt inden for forudsigelse af protein-protein-interaktioner, hvor benchmarks viser klare fordele med hensyn til hastighed og nøjagtighed, men endnu ikke formår at løse hver eneste biologiske nuance uden menneskelig vejledning. Deres tøven stammer fra bekymringer om, at nuværende modeller kan overse subtile interaktionsdynamikker i levende systemer, hvilket efterlader nogle kanttilfælde, hvor biologien stadig overgår algoritmen. Kendelse afsagt: "AI folder proteinerne, men livet vrider dem stadig."
The jury agreed that AI has made remarkable strides in predicting protein-protein interactions, with benchmarks showing clear advantages in speed and accuracy, yet still falls short of solving every biological nuance without human guidance. Their hesitancy stems from concerns that current models may miss subtle interaction dynamics in living systems, leaving some edge cases where biology still outwits the algorithm. Verdict delivered: "AI folds the proteins, but life still twists them.
But the data is real.
The Case File
Across 12 sessions, 36 jurors have heard this case. Combined tally: 12 YES · 21 ALMOST · 3 NO · 0 IN RESEARCH.
Note: cumulative includes older juror opinions. The current session tally above is the live verdict.
By a vote of 1 — 2 — 0, the panel returns a verdict of NæSTEN, with verdict confidence of 85%. The court so orders.
"AlphaFold3 and related models have demonstrated superior PPI prediction accuracy in benchmark studies and challenges."
"AI models like AlphaFold predict protein structures"
"AI models like AlphaFold predict protein structures"
Individuelle nævningers udtalelser vises på originalengelsk for at bevare bevismæssig præcision.
Hvad publikum mener
Nej 6% · Ja 76% · Måske 18% 154 votesDiskussion
no comments⚖ 12 jury checks · seneste for 2 dage siden
Hver række er et separat jurytjek. Nævninger er AI-modeller (identiteter holdt neutrale med vilje). Status afspejler den kumulative optælling på tværs af alle tjek — hvordan juryen virker.