Dimensions overall score 8.0

• HADES outperforms state-of-the-art baselines in in-silico evaluations across most metrics.result 90%
• HADES enables rapid transition of proposals toward promising areas by leveraging momentum and uncertainty in simulated physical movements.method 85%
• HADES uses a position discretization procedure to propose discrete protein sequences from a continuous state system.technical 90%
• HADES leverages mutual constraints between protein structure and sequence to design sequences with similar structures and optimized properties.method 85%
• HADES uses a two-stage encoder-decoder framework to determine structure and function relationships between mutant neighbors.technical 90%
• HADES's reliance on accurate structure prediction models is a limitation that could propagate errors affecting outcomes.limitation 85%
• HADES could replace traditional iterative protein engineering methods like directed evolution which are costly and time-intensive.market 75%
• HADES significantly outperformed existing methods on metrics including maximum fitness, mean fitness, and fitness diversity using benchmark datasets like GB1 and PhoQ.result 80%

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