GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE336nnn/GSE336208/primaryOK200Othersynthetic constructOtherhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE336208GEOGSEfalseFrom enrichment to inference: a multi-target framework for scalable aptamer discoverySELEX has greatly advanced aptamer discovery; however, traditional workflows face considerable challenges in scaling due to the labor-intensive screening and validation processes. 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. Within each sequence-protein matrix, the dataset integrates raw read counts, relative abundance ratios, sequence ranks, and target identities into a unified format. This structure preserves both successful and unsuccessful screening results, which are crucial for com-prehending target screenability and distinguishing genuine target-specific enrichment from general back-ground retention.2026/06/23GSE336208GSM9830041GSM9830042GSM9830043GSM9830044GSM9830045GSM9830046GSM9830039GSM98300403713326526336208synthetic construct