Taghon, G.
(2026),
AptaFind: A Lightweight Local Interface for Automated Aptamer Curation from Scientific Literature, ACS Synthetic Biology
(Accessed January 22, 2026)
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AptaFind: A Lightweight Local Interface for Automated Aptamer Curation from Scientific Literature
Published
Author(s)
Abstract
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 value exists along a spectrum, not as 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 failed extractions need not be failures at all, but opportunities to deliver the intelligence researchers need, faster.Citation
ACS Synthetic Biology
Pub Type
Journals
Keywords
aptamer, literature mining, agentic, language model, biocuration, automation
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Created January 21, 2026