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This work introduces antiGen, a machine learning model that forecasts the evolutionary future of viruses by learning from their evolutionary past, and achieves state-of-the-art performance for predicting mutations to the SARS-CoV-2 spike protein.
This work demonstrates how to provide task-specific information without losing the general knowledge learned during pretraining by using direct preference optimization to align a structure-conditioned protein language model to preferentially generate stable protein sequences.
The utility of ML-assisted evolution for engineering Rubisco with improved carboxylation efficiency and potential for enhancing crop productivity is demonstrated.
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