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Updated: Feb 28, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Binder2030: a quantitative membrane proteome binding dataset enabling AI-driven drug discovery
Naoki Tarui1, Masaharu Nakayama1, Thuy Duong Nguyen1
1SEEDSUPPLY INC., 26-1, Muraoka-Higashi 2-Chome, Fujisawa, Kanagawa 251-0012, Japan.
Abstract:
Membrane proteins represent more than half of therapeutic targets but remain underrepresented in quantitative ligand-binding datasets. Here we report Binder2030, a curated affinity selection-mass spectrometry (ASMS) dataset comprising 3384 small-molecule ligands across approximately 400 transmembrane proteins, including G protein-coupled receptors (GPCRs), solute carrier (SLC) transporters, and ion channels. Using Binder Selection Technology (BST) applied to membrane fractions, Binder2030 provides standardized dissociation constant (Kd) measurements with curated chemical identifiers and target annotations, enabling comparative analysis of affinity distributions and chemical space across target classes. To support external anchoring, we release a PubChem-overlap subset with matched activity annotations and Binder2030 Kd values. Finally, we demonstrate downstream integration in a structure-based modeling workflow by comparing Boltz-2 predicted potencies with experimental affinities for a GlyT-1 ligand set.
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