E2P projects pre-computed user embeddings into a single soft prefix token for frozen LLMs, reporting gains on four personalization tasks, though its reproduction scripts write zero embeddings.
Free energy analyses of cell-penetrating peptides using the weighted ensemble method
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abstract
Cell-penetrating peptides (CPPs) have been widely used for drug-delivery agents; however, it has not been fully understood how they translocate across cell membranes. The Weighted Ensemble (WE) method, one of powerful and flexible path sampling techniques, can be helpful to reveal translocation paths and free energy barriers along those paths. Within the WE approach we show how Arg9 (nona-arginine) and Tat interact with a DOPC/DOPG (4:1) model membrane, and we present free energy (or potential mean of forces, PMFs) profiles of penetration, although a translocation across the membrane has not been observed in the current simulations. Two different compositions of lipid molecules were also tried and compared. Our approach can be applied to any CPPs interacting with various model membranes, and it will provide useful information regarding the transport mechanisms of CPPs.
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cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
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Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models
E2P projects pre-computed user embeddings into a single soft prefix token for frozen LLMs, reporting gains on four personalization tasks, though its reproduction scripts write zero embeddings.