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This study conducts extensive evaluations across a wide range of settings and demonstrates that ProFiT is a plug-and-play tokenizer adaptable to diverse downstream tasks and further reveals the significant potential of the flow matching tokenizer paradigm.
The evaluation reveals that model performance shifts systematically with the availability of target-specific experimental evidence: PLMs demonstrate proficiency in open-ended single-mutant generation by leveraging protein-specific priors, whereas LLMs and agents perform strongly in multi-mutant ranking, particularly wh...
This work proposes the first Bayesian flow formulation for protein backbone orientations by recasting orientation modeling as an equivalent hyperspherical generation problem with antipodal symmetry and delivers consistently exceptional performance in both peptide and antibody design tasks.
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