A kész LLM-ek hatékony fehérjeszekvencia-optimalizálónak bizonyultak
New research demonstrates that standard large language models, specifically Llama-3.1-8B-Instruct, can perform complex protein engineering tasks without any special modifications. The researchers showed the model is capable of performing protein engineering through Pareto and budget-constrained optimization, demonstrating success on both synthetic and experimental fitness landscapes.
- The setup uses random sampling to find protein candidates with high fitness and low editing distance.
- LLMs are encouraged to generate new candidates through mutation or crossover operations.
- The approach outperformed standard evolutionary baselines in several distinct tasks.
- The method was successful in multi-objective and budget-constrained optimization.
- Integration into real-world pipelines could accelerate directed evolution experiments.
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This highlights a 'capability overhang' where existing AI systems possess latent scientific abilities that are only now being discovered, suggesting massive untapped utility in current technology even without further training.
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