Oak Brook, IL – Volume 39 of SLAS Discovery includes three original research articles and one short communication that aim to accelerate drug discovery through novel imaging workflows, fragment identification for challenging targets and datasets that bridge ligand-binding data gaps to enable AI-driven drug discovery.
Original Research
- AI-Based Analysis of Label-Free Live Cell Imaging of T-Cell Mediated Tumor Killing Assay Enables Competitive and Robust Hit Calling
This article presents a newly developed AI-powered live brightfield microscopy analysis workflow that eliminates the need for fluorescent dyes or nuclear labels to assess functional activity of immune cell therapeutics in T-cell mediated killing assays. The hands-free approach simplifies these assays while maintaining the consistency of traditional methods and avoiding artifacts such as phototoxicity and segmentation errors.
- Application of a MALDI Mass Spectrometry Assay to Identify Covalent Fragments Targeting the Methyl-Lysine Reader Protein MPP8
Using high-throughput MALDI-TOF mass spectrometry to screen covalent fragment libraries, the authors identify two novel acrylamide-containing fragments that target and label the methyl-lysine reader protein MPP8 at cysteine 99, a site adjacent to its functional binding pocket. The results highlight the value of efficient screening approaches for targets such as MPP8, a challenging protein target with potential implications for cancer therapeutics.
- An MRC-5 Cell Based High-Throughput, High-Content Imaging Assay to Identify Hits Against Trypanosoma cruzi Intracellular Parasites
This study develops and validates a new high-throughput, high-content imaging assay in a 384-well format that rapidly assesses potential drug candidates against Trypanosoma cruzi, the parasite that causes Chagas disease. The multiplexed platform simultaneously measures both anti-parasitic activity and host cell toxicity, offering an efficient path to discovering safer, more effective therapies beyond the currently limited and side-effect-prone drugs benznidazole and nifurtimox.
- Binder2030: A Quantitative Membrane Proteome Binding Dataset Enabling AI-Driven Drug Discovery
The authors present Binder2030, a curated affinity selection–mass spectrometry dataset containing nearly 3,400 small-molecule ligands measured against roughly 400 membrane proteins, including G protein-coupled receptors, solute carrier transporters and ion channels. This resource provides standardized dissociation constant measurements and chemical annotations, helping bridge a critical gap in quantitative ligand-binding data for a class of proteins that represents over half of all therapeutic targets.
Access to this volume of SLAS Discovery is available at https://www.slas-discovery.org/issue/S2472-5552(25)X0010-1
All active SLAS Discovery and SLAS Technology call for papers are available at: https://www.slas.org/publications/call-for-papers/
