Aptamer researchers face a literature landscape scattered across publications, supplements, and databases, with each search consuming hours that could be spent at the bench. AptaFind transforms this navigation problem through a three-tier intelligence architecture that recognizes research mining is a spectrum, not a binary success or failure. The system delivers direct sequence extraction when possible, curated research leads when extraction fails, and exhaustive literature discovery for additional confidence. By combining local language models for semantic understanding with deterministic algorithms for reliability, AptaFind operates without cloud dependencies or subscription barriers. Validation across 300 University of Texas Aptamer Database targets demonstrates 84 % with some literature found, 84 % with curated research leads, and 79 % with a direct sequence extraction, at a laptop-compute rate of over 900 targets an hour. The platform proves that even when direct sequence extraction fails, automation can still deliver the actionable intelligence researchers need by rapidly narrowing the search to high quality references.

翻译：适配体研究者面临的文献资料分散在各类出版物、补充材料和数据库中，每次检索都会耗费本可用于实验台工作的数小时。AptaFind通过三层智能架构改变了这一检索困境，其核心理念在于：研究挖掘是一个连续谱系，而非简单的成功或失败二分。该系统在可能时直接提取序列，在提取失败时提供精选研究线索，并通过穷尽式文献发现提供额外置信度。通过将用于语义理解的本地语言模型与确保可靠性的确定性算法相结合，AptaFind无需依赖云端服务或订阅壁垒即可运行。在德克萨斯大学适配体数据库300个靶标上的验证表明：84%的靶标能找到相关文献，84%获得精选研究线索，79%实现直接序列提取，且在笔记本电脑上每小时可处理超过900个靶标。该平台证明，即使直接序列提取失败，自动化仍能通过快速将检索范围缩小至高质量参考文献，为研究者提供所需的可操作情报。