Jason S. Rush , Joshua D. Wertheimer , Steven D. Goldberg , Donald Raymond , Mateusz Szuchnicki , Andrew J. Baltus , Jeff Branson , Christopher F. Stratton , Aaron N. Patrick , Ruth Steele , Suraj Adhikary , Amanda Del Rosario , Annie Liu , Noah J. Gomersall , Michael Chung , Matthew J. Ranaghan , Xiebin Gu , Marta Brandt , Zhifang Cao , Adrian Bebenek , Blayne A. Oliver , Kasper Hoebe , Lawrence M. Szewczuk , Jennifer D. Venable , Daniel B. Graham , Jennifer Towne , Ramnik J. Xavier

Cell

DOI: 10.1016/j.cell.2025.12.013

Abstract

Human genetic association studies highlight key genes involved in disease pathology, yet targets identified by these analyses often fall outside the traditional definitions of druggability. A rare truncated variant of the scaffold protein CARD9 is linked with protection from Crohn’s disease, prompting us to pursue the development of inhibitors that might similarly modulate innate inflammatory responses. Using a phased approach, we first identified a ligandable site on CARD9 using a structurally diverse DNA-encoded library and defined this site in detail through X-ray crystallography. Building upon this, a subsequent ligand displacement screen identified additional molecules that uniquely engage CARD9 and prevent its assembly into scaffolds needed to nucleate a signalosome for downstream nuclear factor κB (NF-κB) induction. These inhibitors suppressed inflammatory cytokine production in dendritic cells and a humanized CARD9 mouse model. Collectively, this study illustrates a strategy for leveraging protective human genetic variants and chemical biology to tackle challenging targets for dampening inflammation.

Yiran Huang ,  Xiao Tan ,  Xiaoyu Li ,  Feng Xiong ,  Siu Ming Yiu

Bioinformatics (Oxford, England)

DOI: 10.1093/bioinformatics/btag001

Abstract

Motivation

DNA encoded library (DEL) technology has been developed as a powerful platform for drug development. Live cell-based selection methodologies were recently developed to expedite drug candidate discovery with higher biological relevance. Nevertheless, hit characterization is challenged by prominent background signals of cell-based selections. Therefore, automated data processing streamline compatible with noisy sequencing output is highly desirable.

Results

Herein we report an innovative automatic method that enables the most promising hit identification from large quantities of cell-based DEL datasets with improved accuracy and efficiency. This processing workflow is based on a comprehensive unsupervised algorithm incorporating data pre-processing, feature extracting and outlier filtering, descriptor-based classification, similarity score ranking and active compound prediction. We performed methodology development with two DEL selection datasets targeting insulin receptor (INSR) on live cells, from both ˜30 million- and 1.033 billion- membered libraries. The automated scheme has demonstrated high consistency with experimental results as well as self-adaptivity to on-cell DEL datasets with varied library scales. Extended methodology application to cellular thrombopoietin receptor (TPOR) further substantiated the algorithmic generalization capability regarding target proteins. Thus, this approach can serve as a widely applicable workflow automatically differentiating hit compounds and thereby facilitates drug development from candidate discovery.

James H. Walker ,  Kejia Yan ,  Ratmir Derda

Biochemistry

DOI: 10.1021/acs.biochem.5c00646

Abstract

Peptide-derived macrocycles are an emerging class of therapeutics capable of modulating protein–protein interactions that remain inaccessible to small molecules. Genetically encoded library (GEL) platforms such as phage and mRNA display have accelerated macrocyclic ligand discovery by linking peptide sequence to genotype and enabling selections from libraries with up to 10¹³ members. Efforts to expand the chemical space of GELs have included incorporation of electrophiles, either to generate libraries of true covalent ligands or to enable intramolecular reactions such as peptide cyclization. In the latter case, the electrophile is consumed during library construction, producing transient covalent libraries that enhance stability and diversity but are not designed for direct covalent engagement with targets. By contrast, recent advances have established robust strategies for embedding persistent electrophilic warheads that remain intact during library preparation and selectively react with nucleophilic residues on proteins. These approaches have yielded both reversible and irreversible covalent inhibitors against diverse classes of proteins, while also highlighting challenges in balancing electrophile reactivity with library integrity. Complementary developments in DNA-encoded covalent libraries further underscore the breadth of discovery platforms, though genetically encoded approaches remain uniquely powerful for macrocyclic peptides. Together, these advances define the trajectory of covalent genetically encoded libraries (cGELs) and point toward new opportunities for discovering ligands to historically undruggable targets.

Daniela Schaub , Alice Lessing , Gerlis von Haugwitz , Fabian Meyer , Jörg Scheuermann , Rebecca Buller

DOI: 10.1021/acscentsci.5c01516

Abstract

DNA-encoded libraries (DELs) have become a powerful platform in drug discovery, practiced both by the pharmaceutical industry and academia. Each small molecule contained in a DEL is covalently linked to a DNA tag which serves as an amplifiable barcode facilitating binder identification. However, the chemical diversity accessible in DELs remains limited by the need to perform reactions under conditions that preserve the integrity of the DNA tag. Additionally, chemical reactions must proceed with high efficiency and selectivity to minimize side products and unreacted starting materials, which cannot be removed and may hamper hit identification. Consequently, expanding the DEL chemical space requires the development of methods that combine high reaction performance with DNA compatibility. In this outlook, we highlight the potential of enzymatic catalysis for on-DNA synthesis, which offers a promising route to expand DEL-accessible chemical space.

John C. Faver , Flora Sundersingh , Lauren A. Viarengo-Baker , Ying-Chu Chen , Katelyn Billings , Patrick F. Riley , Ching-Hsuan Tsai , Christopher S. Kollmann

Journal of Medicinal Chemistry

DOI: 10.1021/acs.jmedchem.5c02259

Abstract

DNA-encoded chemical libraries (DELs) enable the highly efficient screening of billions of small molecules for binding to a target of interest and provide valuable training data for machine learning models for virtual screening. However, DEL screening data are notoriously noisy due in large part to significant variance in the synthetic yield of library members. Here, we show an analysis from a split-sample DEL screening strategy against Bruton’s tyrosine kinase (BTK), which includes a panel of affinity selections against the target at varying concentrations and a probabilistic model to estimate the binding affinity and relative input concentrations of library members. We compared model predictions to SPR measurements of resynthesized DNA-conjugated compounds and found that this methodology yielded an improved ranking of library members by binding affinity compared to enrichment metrics. Additionally, the method successfully recovered a library member with a potent binding affinity that would not have been detected in our standard DEL selection.

Gui-Ping Gao , Quan-Ke Li , Jin-Cheng Ma , Zhi-Jun Zhang , Shao-Yong Zhang , Ying-Qian Liu

Bioorganic & Medicinal Chemistry

DOI: 10.1016/j.bmc.2025.118541

Abstract

Indole, an aromatic heterocyclic compound formed by the fusion of a benzene ring with a pyrrole ring, is widely distributed in the secondary metabolites of plants, animals, and marine organisms. Owing to its unique physicochemical properties and high structural modifiability, indole derivatives can engage in specific interactions with various biological targets, demonstrating a broad spectrum of bioactivities including anticancer, anti-inflammatory, antiviral, and antibacterial effects. Consequently, indole holds an indispensable position in innovative drug discovery and development. This review provides a comprehensive summary of the primary strategies employed in the discovery of indole derivatives. These encompass structure optimization approaches inspired by natural products, such as structure simplification, diversity-oriented synthesis (DOS), biology-oriented synthesis (BIOS), the “pseudo-natural product” (PNP) strategy, and bioinspired synthesis based on biosynthetic building blocks. Additionally, strategies like scaffold hopping, molecular hybridization, drug repurposing, and multicomponent reactions (MCRs) for constructing indole-based molecules are discussed. Particular emphasis is placed on target structure-based discovery strategies for indole derivatives, including ligand-based structure modification, molecular docking-assisted high-throughput virtual screening, and fragment-based drug design (FBDD). Furthermore, the application of emerging techniques such as phenotypic screening, DNA-encoded library (DEL) technology, and free energy perturbation (FEP) calculations in indole-based drug research and development is highlighted. This review aims to systematically organize the multi-dimensional R&D framework for indole derivatives, analyze the specific value of each strategy in addressing drug discovery challenges, and provide a theoretical foundation and methodological support for the rational design and development of novel indole-based drugs. It is anticipated that this work will further enhance the efficiency and innovation level in the development of this class of compounds.