Discovery on Target

Poster sessions

Poster 1 Title: : Large-scale Characterization of Drug Candidates Against Transmembrane Receptors using HT-SPR

Abstract: Membrane targets make up a substantial part of the overall “undruggable” therapeutic space that has recently garnered widespread interest. Despite encouraging improvements in the tools to screen for therapeutics against membrane-bound targets, there are still many practical limitations owing to the challenges of working with proteins that are not highly stable outside of the cell membrane environment. High-throughput surface plasmon resonance (HT-SPR) is a powerful technique that is transforming characterization workflows and enabling a greater breadth and depth of information for drug candidates. Here we demonstrate the ability to quantitatively assess binding kinetics for panels of antibodies against membrane receptors in several formats. This workflow highlights opportunities to perform detailed binding characterization for up to thousands of drug candidates in parallel.

Poster 2 Title: High-Throughput SPR Screening and Kinetic Characterization Using Carterra Vega™ with AI Binding Prediction Comparison

Abstract: Carterra Vega™ is a high-throughput surface plasmon resonance (HT-SPR) platform that enables 48-analyte parallel analysis through its multi-channel flow cell, supporting high-quality binding data collection for up to two targets plus an internal reference in each channel. In this study, small-molecule compounds from the Maybridge fragment library were screened against human carbonic anhydrase II (hCAII) and XII (hCAXII) using Vega, allowing a 384-well plate to be screened in approximately 35 minutes while providing kinetic context. With fast cycle times and an optional integrated plate-loading robot, Vega can screen more than 20,000 compounds per day. SPR screening results were compared with binding predictions generated using Boltz-2, and selected hits were subsequently characterized by titration experiments to determine kinetic and affinity parameters. The comparison showed agreement between predicted and experimentally observed binders for a subset of compounds, while also revealing additional binders that were not strongly predicted by the model. Further analysis suggests that AI-based binding predictions align well with known chemical patterns represented in available data, while SPR screening can reveal additional binders beyond those learned patterns. Together, these results demonstrate how HT-SPR can efficiently identify and characterize small-molecule binders while generating experimental datasets that complement and inform computational prediction approaches in early drug discovery.