ProteinOPD uses token-level on-policy distillation from multiple preference-specific teacher models into a shared student to balance competing objectives in protein design, delivering gains on targets without losing designability and an 8x speedup over RL baselines.
Rita: a study on scaling up generative protein sequence models
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A domain-adaptive fine-tuning stage followed by reward-weighted RL fine-tuning produces protein sequences whose amino-acid composition matches a specified target while preserving sequence statistics and diversity.
Review of generative sequence models and Direct Coupling Analysis for simulating protein evolutionary dynamics from extant data.
citing papers explorer
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ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
ProteinOPD uses token-level on-policy distillation from multiple preference-specific teacher models into a shared student to balance competing objectives in protein design, delivering gains on targets without losing designability and an 8x speedup over RL baselines.
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Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition
A domain-adaptive fine-tuning stage followed by reward-weighted RL fine-tuning produces protein sequences whose amino-acid composition matches a specified target while preserving sequence statistics and diversity.
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Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics
Review of generative sequence models and Direct Coupling Analysis for simulating protein evolutionary dynamics from extant data.