From enrichment to inference: a multi-target framework for scalable aptamer discovery

More critically, in conventional SELEX, sequencing abundance does not always correlate directly with binding properties due to amplification bias, matrix adsorption, and initial copy-number advantages. We establish an automated framework that converts enrichment readouts into affinity- and specificity-informed candidate prioritization.

The framework integrates two key innovations: a distribution-reset secondary screening and a relative abundance ratio for candidate selection. Furthermore, the 96-target workflow generates a systematically organized sequence-target dataset that can be utilized as a valuable resource for analyzing SELEX mechanisms and facilitating AI-assisted ap-tamer discovery. Here, we release the sequencing data of the enriched libraries.