Generative AI for protein fitness optimization
This project explores generative modeling for optimizing protein fitness, such as stability, binding affinity, or enzymatic activity. A machine learning-based prior over protein sequences is combined with a property prediction model that guides generation toward high-fitness sequences. The resulting variational framework, VLGPO, can generate new candidate protein sequences predicted to exhibit higher fitness. (read more here: click here )
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