用字符级分词提升RNA模型性能，实现更精准的生物分子预测。
Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models
- 通过可学习的字符级分词，捕捉RNA序列深层特征
- 在多个基准任务上达到当前最优表现，尤其在蛋白互作预测中领先
- 模型轻量高效，适合科研人员快速部署与研究
RNA在细胞中扮演关键角色，其治疗靶向研究近年备受关注。然而，由于结构与相互作用的复杂性，全面理解并预测RNA行为仍具挑战，现有RNA模型性能远未达到蛋白质领域的水平。本文提出ChaRNABERT系列轻量级、高效的RNA基础模型，通过可学习的字符级分词机制，在多个标准基准任务中取得当前最优表现，并拓展至RNA-蛋白及适配体-蛋白相互作用预测等下游任务。提供ChaRNABERT-8M的权重与推理代码供学术研究使用，其他模型可通过申请获取。
原文摘要 · Abstract (English)
RNA is a vital biomolecule with numerous roles and functions within cells, and interest in targeting it for therapeutic purposes has grown significantly in recent years. However, fully understanding and predicting RNA behavior, particularly for applications in drug discovery, remains a challenge due to the complexity of RNA structures and interactions. While foundational models in biology have demonstrated success in modeling several biomolecules, especially proteins, achieving similar breakthroughs for RNA has proven more difficult. Current RNA models have yet to match the performance observed in the protein domain, leaving an important gap in computational biology. In this work, we present ChaRNABERT, a suite of sample and parameter-efficient RNA foundational models, that through a learnable tokenization process, are able to reach state-of-the-art performance on several tasks in established benchmarks. We extend its testing in relevant downstream tasks such as RNA-protein and aptamer-protein interaction prediction. Weights and inference code for ChaRNABERT-8M will be provided for academic research use. The other models will be available upon request.
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