AptaFind: A lightweight local interface for automated aptamer curation from scientific literature

TL;DR

AptaFind is a local, multi-tier tool that automates aptamer literature curation by combining language models and algorithms, significantly reducing research time and providing reliable sequence and literature insights without cloud reliance.

Contribution

It introduces a three-tier architecture integrating local language models and deterministic algorithms for efficient, cloud-free aptamer literature curation and sequence extraction.

Findings

84% literature coverage across targets

84% curated research leads identified

79% direct sequence extraction success

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 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.