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NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

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Integrating Functional Response and Target Binding for Mechanism-Centered Drug Screening by High-Mass MALDI-MS.

Congrui Tan¹, Yu Gao², Marcus Buggert²

¹Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), CH-8093 Zürich, Switzerland.

ACS Central Science

|March 30, 2026

Summary

This study introduces a new mass spectrometry platform for drug discovery. It integrates functional and binding assays to better identify effective drug candidates for challenging targets like protein-protein interactions.

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Area of Science:

Biochemistry
Pharmacology
Mass Spectrometry

Background:

Early drug discovery faces high failure rates due to separate functional and binding assays.
Distinguishing drug mechanisms for protein-protein interactions is challenging.
Existing methods create an information gap, hindering effective hit advancement.

Purpose of the Study:

To develop an integrated assay for simultaneous measurement of biochemical function and target binding.
To generate multidimensional pharmacological profiles for drug candidates.
To enable rational selection of high-quality leads for challenging drug targets.

Main Methods:

Developed a cross-linking matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) platform.
Integrated biochemical functional response and target binding into a single assay.
Utilized the SARS-CoV-2 RBD-ACE2 interaction and 17 drug candidates for proof-of-concept.

Main Results:

The platform distinguished between two otherwise indistinguishable inhibitors of the SARS-CoV-2 RBD-ACE2 interaction.
One inhibitor demonstrated stronger affinity and preferential ACE2 binding compared to the other.
Mechanistic differences correlated with improved cell viability in cellular antiviral assays.

Conclusions:

The developed MALDI-MS platform offers a mechanism-centered, rapid screening strategy.
Provides early multiparameter insight for drug lead selection.
Compatible with high-throughput formats for efficient drug discovery.