AptaNexus: An AI-Driven Platform for Aptamer Data Discovery and Intelligent Interaction

Abstract

Aptamers are widely used in biosensing and targeted therapeutics, yet reported data remain fragmented across unstructured text, tables, and figures. Existing databases are limited in coverage and diversity, which constrains computational modeling. Here, we present AptaNexus (https://www.aptanexus.com/), a multitier aptamer database containing over 12,000 sequences targeting 1900 molecular entities, curated from literature published between 2005 and 2025. Its extraction pipeline, Dual-LLM Extraction with Reverse Grounding (Dual-LLMs+RG), achieves an F1 score of 0.970 at a fraction of the cost of the state-of-the-art model. Records are stratified into four quality tiers, supporting both experimental selection and machine learning applications. Beyond conventional keyword search, AptaNexus incorporates the Model Context Protocol (MCP) and an embedded conversational agent, Chat Aptamer, enabling natural-language queries that return structured, source-linked, and application-oriented recommendations. For example, users can request detection of a target in blood to receive a ranked list of aptamers with validated sensor platforms or query drug delivery targets to obtain functionally annotated candidates. By combining large-scale, evidence-grounded data with agent-accessible interfaces, AptaNexus makes aptamer information readily accessible across disciplines and, for the first time, provides native AI-agent interoperability.