Autoregressive protein language models (pLMs) have become transformative tools for designing functional proteins with remarkable diversity, demonstrating success in creating enzyme families like lysozymes and carbonic anhydrases. These models generate protein sequences by sampling from learned probability distributions, uncovering intrinsic patterns within training datasets. Despite their ability to explore high-quality subspaces of the sequence landscape, […]
The post Optimizing Protein Design with Reinforcement Learning-Enhanced pLMs: Introducing DPO_pLM for Efficient and Targeted Sequence Generation appeared first on MarkTechPost.
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