Nuñez-Andrade et al. propose “eOHE,” compressing chemical tokens into fewer real values. It slashes memory costs in deep learning while preserving model performance, helping large-scale molecule generation remain efficient and robust. https://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00230j
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