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Results for “Small Molecule Libraries”

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PubMed2026

First-in-Class Small Molecule Inhibitor of Oncogene AVIL in Glioblastoma.

Glioblastoma multiforme (GBM) is the most prevalent and aggressive malignant primary brain tumor, marked by rapid growth, extensive invasiveness, and a median survival of only ∼15 months despite current multimodal therapy. To identify new therapeutic vulnerabilities, we investigated the actin-regulatory protein AVIL, previously implicated through a MARS-AVIL gene fusion in rhabdomyosarcoma. Comprehensive genomic and transcriptomic analyses across REMBRANDT, TCGA, and CGGA datasets revealed recurrent AVIL amplification and consistently elevated AVIL expression in GBM compared with normal brain tissue. AVIL was overexpressed across all GBM molecular subtypes and glioma stem cell (GSC) states but was nearly undetectable in normal astrocytes, neural stem cells, and brain tissues. Functional studies demonstrated that AVIL is both necessary and sufficient for glioma genesis: AVIL silencing eradicated GBM cells in vitro and suppressed xenograft growth in vivo, while AVIL overexpression enhanced proliferation, migration, and transformation. Mechanistically, AVIL drives tumor progression through actin cytoskeleton remodeling and activation of the FOXM1-LIN28B oncogenic pathway. Using a small molecule microarray screen, we identified a selective AVIL-binding compound (compound A) that potently inhibited GBM cell growth with minimal toxicity to normal astrocytes. Gene expression changes induced by compound A mirrored those following AVIL knockdown, indicating on-target activity. Compound A demonstrated robust antitumor efficacy in multiple preclinical GBM models, including orthotopic xenografts, GSC-derived tumors, patient-derived xenografts, and temozolomide-resistant GBM with favorable pharmacokinetics and blood-brain barrier penetration. The minimal AVIL expression in normal tissues and lack of phenotype in AVIL-deficient mice underscore its potential as a low-toxicity therapeutic target. Together, these findings establish AVIL as a critical oncogenic driver in GBM and introduce a first-in-class AVIL inhibitor with strong translational promise for precision neuro-oncology.

Glioblastoma↗

PubMed2024

Identification of potent pan-ephrin receptor kinase inhibitors using DNA-encoded chemistry technology.

EPH receptors (EPHs), the largest family of tyrosine kinases, phosphorylate downstream substrates upon binding of ephrin cell surface-associated ligands. In a large cohort of endometriotic lesions from individuals with endometriosis, we found that EPHA2 and EPHA4 expressions are increased in endometriotic lesions relative to normal eutopic endometrium. Because signaling through EPHs is associated with increased cell migration and invasion, we hypothesized that chemical inhibition of EPHA2/4 could have therapeutic value. We screened DNA-encoded chemical libraries (DECL) to rapidly identify EPHA2/4 kinase inhibitors. Hit compound, CDD-2693, exhibited picomolar/nanomolar kinase activity against EPHA2 (Ki: 4.0 nM) and EPHA4 (Ki: 0.81 nM). Kinome profiling revealed that CDD-2693 bound to most EPH family and SRC family kinases. Using NanoBRET target engagement assays, CDD-2693 had nanomolar activity versus EPHA2 (IC50: 461 nM) and EPHA4 (IC50: 40 nM) but was a micromolar inhibitor of SRC, YES, and FGR. Chemical optimization produced CDD-3167, having picomolar biochemical activity toward EPHA2 (Ki: 0.13 nM) and EPHA4 (Ki: 0.38 nM) with excellent cell-based potency EPHA2 (IC50: 8.0 nM) and EPHA4 (IC50: 2.3 nM). Moreover, CDD-3167 maintained superior off-target cellular selectivity. In 12Z endometriotic epithelial cells, CDD-2693 and CDD-3167 significantly decreased EFNA5 (ligand) induced phosphorylation of EPHA2/4, decreased 12Z cell viability, and decreased IL-1β-mediated expression of prostaglandin synthase 2 (PTGS2). CDD-2693 and CDD-3167 decreased expansion of primary endometrial epithelial organoids from patients with endometriosis and decreased Ewing's sarcoma viability. Thus, using DECL, we identified potent pan-EPH inhibitors that show specificity and activity in cellular models of endometriosis and cancer.

Humans↗

PubMed2026

Small molecule inhibition of CPSF3 may impact R-loop distribution and abundance.

R-loops are three-stranded nucleic acid structures consisting of an RNA/DNA hybrid and a displaced strand of DNA. These structures have been implicated in a variety of regulatory cellular processes. Their untimed or excess accumulation, however, can cause genomic instability and induce DNA damage. Most R-loops form co-transcriptionally when the nascent transcript reanneals to unwound DNA duplex. Changes in transcription rates have the potential to impact R-loop formation, and compounds that modulate R-loop formation would be useful molecular tools and therapeutic leads. Cleavage and Polyadenylation Specific Factor 3 (CPSF3) recognizes the pre-mRNA 3' cleavage site, cleaves the transcript prior to polyadenylation, and has been linked to R-loop formation. Inhibition of CPSF3 has been found to induce transcriptional readthrough and cell proliferation defects. A previous report suggested that inhibition of CPSF3 with a small molecule causes a global increase in R-loop formation. Here, we test the impact of YT-II-100, a novel inhibitor of CPSF3. We find that addition of YT-II-100 increases global R-loop formation but does not change R-loop formation at specific genes that are normally used as positive controls for R-loop formation. We performed parallel assays using a previously reported compound, JTE-607, and observed similar results. Our data emphasize the need for cautious interpretation of experiments using JTE-607 and YT-II-100. There may be different mechanisms of R-loop formation depending on gene loci, where the control of R-loop formation by agonists at certain genes may differ from the trends observed for impacts on global R-loop formation.

R-Loop Structures↗

PubMed2017

Small-Molecule Inhibitors Targeting DNA Repair and DNA Repair Deficiency in Research and Cancer Therapy.

To maintain stable genomes and to avoid cancer and aging, cells need to repair a multitude of deleterious DNA lesions, which arise constantly in every cell. Processes that support genome integrity in normal cells, however, allow cancer cells to develop resistance to radiation and DNA-damaging chemotherapeutics. Chemical inhibition of the key DNA repair proteins and pharmacologically induced synthetic lethality have become instrumental in both dissecting the complex DNA repair networks and as promising anticancer agents. The difficulty in capitalizing on synthetically lethal interactions in cancer cells is that many potential targets do not possess well-defined small-molecule binding determinates. In this review, we discuss several successful campaigns to identify and leverage small-molecule inhibitors of the DNA repair proteins, from PARP1, a paradigm case for clinically successful small-molecule inhibitors, to coveted new targets, such as RAD51 recombinase, RAD52 DNA repair protein, MRE11 nuclease, and WRN DNA helicase.

DNA Helicases↗

PubMed2025

The limitations of small molecule and genetic screening in phenotypic drug discovery.

Phenotypic screens carried out with functional genomics or small molecules have led to novel biological insights, revealed previously unknown targets for drug discovery programs, and provided starting points for the development of first-in-class therapies. Despite being valuable research tools, genetic and compound screening also have significant limitations. This perspective aims to shed a light on those limitations and provide mitigation strategies when available, with a goal of helping phenotypic screening practitioners gain an understanding of how and when to best utilize either approach.

Drug Discovery↗

PubMed2025

ChemPerturb-seq screen identifies a small molecule cocktail enhancing human beta cell survival after subcutaneous transplantation.

Traditional chemical screens have focused on a single assay per screen, making them labor intensive and costly. Here, we combined a chemical screen with single-cell RNA sequencing (scRNA-seq) to perform Chemical Perturb-seq (ChemPerturb-seq), enabling a systematic analysis of the molecular changes of human beta cells upon individual small molecule treatments. Using this platform, we performed an in vivo barcoded screen and discovered a small molecule cocktail, including beta-lipotropin 61-91, insulin growth factor-1, and prostaglandin E2, with which preconditioning human beta cells and primary islets significantly enhanced function and survival when transplanted subcutaneously to female, but not to male, mice. We identified two additional molecules, serotonin and histamine, that promote islet function when transplanted subcutaneously to male mice using ChemPerturb-seq. Such small molecule cocktails could be applied to improve the current FDA-approved islet transplantation procedure. Finally, we developed an artificial intelligence (AI)-powered website, ChemPerturbDB, which provides user-friendly open access analysis of the extensive ChemPerturb-seq dataset.

Humans↗

PubMed2025

Chromatin Regulatory Targets for Anticancer Therapeutics.

Chromatin serves to organize and compact the genome but also functions as a signaling hub for the dynamic regulation of transcriptional programs that control cell type specification. The historical discovery that several pro-differentiation anti-cancer agents target chromatin regulatory enzymes buoyed early interest in developing drugs that modulate chromatin structure and function. Chromatin-based drug discovery has since flourished alongside major advances in discovery chemistry and target selection, producing a rich collection of chemical probes, drugs, and drug candidates targeting chromatin regulatory processes. The substantial growth and maturity of this field over the last several decades provides an opportunity to reflect on the successes and failures associated with translating chromatin regulatory targets into anti-cancer drugs. Taking a target-centric perspective, we discuss the motivation for pursuing specific chromatin regulatory proteins and review the chemistries that enabled small molecule discovery and development. In so doing, we hope to evaluate the strength of these targets, the agents that prosecute them, and the prospects for future efforts in this field.

Humans↗

PubMed2026

Structure-based drug design of small-molecule c-Myc G-quadruplex binders.

The c-Myc oncogene is crucial in tumorigenesis. Although it is a promising therapeutic target, its protein lacks a conventional drug-binding pocket, making it traditionally "undruggable". Recent studies show that the c-Myc promoter can form a G-quadruplex (G4) structure, which suppresses transcription and offers a new strategy for indirect inhibition. In this study, structure-based virtual screening was performed using the c-Myc G4 crystal structure to screen the ChemDiv compound library, aiming to identify small molecules that bind to the G4 structure. Candidate compounds were evaluated in preliminary in vitro assays for biological activity. The results showed that Y502-3888 binds to the c-Myc G4 and downregulates c-Myc expression at both mRNA and protein levels. Collectively, these findings support the potential of Y502-3888 as a c-Myc G4 binder for the treatment of multiple myeloma (MM), providing a foundation for future development of anticancer agents targeting the c-Myc G4.

G-Quadruplexes↗

PubMed2026

Ligand-Mediated Reprogramming Redirects Liver-Tropic Ionizable Lipid Nanoparticles for Lung-Selective mRNA Delivery.

Systemic delivery of messenger RNA (mRNA) to target tissues and cells using lipid nanoparticles (LNPs) holds transformative potential for gene therapy. However, most clinically validated LNP exhibit strong liver tropism, and redirecting their organ specificity without redesigning entirely new chemistries remains challenging. Here we present a ligand-mediated lipid reprogramming approach that repurposes chemically defined, liver-tropic, ionizable lipids (lipidoids) for mRNA delivery beyond the liver. From a library of 90 degradable lipidoids, we identified 2-t6b as a potent liver-targeting platform. By site-specific displaying of small molecule ligands onto 2-t6b headgroup, we engineered a series of reconfigured lipidoids that achieve lung-specific targeting while retaining the parent delivery scaffold. Ligand7-2-t6b-lipid-functionalized LNP achieved over 200-fold higher mRNA translation in the lungs compared to the parent liver-tropic LNP. Proteomics and molecular docking analysis revealed enhanced binding of the modified lipid to vitronectin, a serum glycoprotein that improves integrin binding and thus promotes cellular uptake and translation efficiency. Ligand-mediated 2-t6b/ligand7 LNPs achieved outperformed efficacy and therapeutic potential in lung-specific genome editing relative to SORT-constructed 2-t6b LNP system. Our modular reprogramming strategy provides a generalizable framework to upgrade existing liver-biased LNPs into lung-selective mRNA carriers, advancing next-generation tissue-specific mRNA therapies for gene editing, protein replacement therapy, and regenerative medicine.

RNA, Messenger↗

PubMed2026

DeepMASS v.2: An enhanced deep learning platform for large-scale discovery and structural annotation of unknown plant metabolites.

Determining the structures of unknown metabolites remains a fundamental bottleneck in plant metabolomics, as the vast chemical diversity of plant secondary metabolites far exceeds the coverage of existing spectral libraries. Here, we present DeepMASS v.2, a substantially enhanced platform for annotating unknown metabolites from liquid chromatography-tandem mass spectrometry data, designed to address this challenge at scale. DeepMASS v.2 leverages a semantic spectral representation model trained on millions of spectra from GNPS, NIST, and in-house resources. By integrating Spec2Vec-based embeddings with HNSW (hierarchical navigable small world) graph retrieval and a unified chemical space defined by molecular fingerprints, DeepMASS v.2 identifies structurally related neighbors of unknown spectra and ranks candidate structures according to their proximity to the predicted structural neighborhoods within chemical space. Benchmarking against Critical Assessment of Small Molecule Identification datasets and a curated natural product collection demonstrated that DeepMASS v.2 outperforms state-of-the-art in silico annotation tools, including SIRIUS, CFM-ID, MetFrag, and MS-Finder. Importantly, DeepMASS v.2 maintains strong performance for metabolites absent from spectral libraries, highlighting its capacity to annotate genuinely unknown compounds. Application of DeepMASS v.2 to large-scale plant metabolomics datasets demonstrated its ability to expand accessible metabolome coverage. Implemented as an intuitive web platform, DeepMASS v.2 provides the community with a scalable, interpretable, and high-throughput solution for structural annotation, enabling more comprehensive characterization of plant chemical diversity and accelerating natural product discovery in molecular plant science. The DeepMASS v.2 web server is publicly available at http://deepmass.cn.

Metabolomics↗

PubMed2025

Genome-wide CRISPR Screening Identifies NFκB and c-MET as Druggable Targets to Sensitize Lenvatinib Treatment in Hepatocellular Carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC), the dominant form of liver cancer, is a leading cause of cancer death worldwide. Sorafenib and lenvatinib have long been the 2 limited options of first-line treatments for patients with unresectable advanced HCC. However, the single-drug treatment strategy only shows modest survival benefit, mostly because of the survival ability of cancer cells to activate alternative pathways for compensation. In this study, we aim to identify druggable targets contributing to lenvatinib resistance and evaluate the efficacy of combining respective inhibitors and lenvatinib on HCC. METHODS: Genome-scale clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 knockout library screening was applied on the vehicle group and lenvatinib treatment group. Identified druggable candidates were validated individually on HCC cell models. Therapeutic effects of the combined treatment of inhibitors of candidate genes and lenvatinib were evaluated in vitro and in vivo. RESULTS: We successfully identified NFKB1 and MET as critical drivers for the development of lenvatinib resistance in HCC cells. By perturbing the 2 genes with either CRISPR knockout or RNA interference approaches, lenvatinib treatments were significantly sensitized. Moreover, using small molecules QNZ and cabozantinib to target NFKB1 and MET, respectively, this together with lenvatinib could synergistically induce apoptosis and suppress HCC growth in vitro and in vivo. CONCLUSION: Our results demonstrated that genome-wide CRISPR/Cas9 screening is a powerful tool for the design of rational combinational cancer therapy and provided candidate genes possible for combined treatments with lenvatinib to improve therapy efficacy.

Carcinoma, Hepatocellular↗

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗

PubMed2025

Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space.

Significant efforts have been recently dedicated to the discovery of small molecule inhibitors against the Macrodomain 1 (Mac1) of nonstructural protein 3 (NSP3) as potential antivirals for SARS-CoV-2. Thus, Mac1 has also been selected as the target for the Critical Assessment of Hit-finding Experiments (CACHE) challenge #3. As contestants in that challenge, we developed a computational strategy that ranked on the top among all 23 participants in the competition and resulted in the discovery of a novel chemical series of non-charged Mac1 inhibitors. Those have been identified through the combination of machine learning-accelerated virtual screening of Enamine REAL Diversity Subset of approximately 25 million compounds and consequent hit expansion into the entire Enamine REAL Space library. In particular, the initially identified hit compound CACHE3-HI_1706_56 (KD = 20 μM) was explored by probing 17 close analogues from a library of 44 billion molecules from the Enamine REAL. All those analogues effectively displaced the Mac1-binding ADP-ribose peptide, and 12 were confirmed to engage with Mac1 by the Surface Plasmon Resonance experiments, revealing a new chemical series of compounds for hit-to-lead optimization. The structure of the CACHE3-HI_1706_56-Mac1 complex was further determined at high resolution with crystallography, confirming initial computational predictions. Our results illustrate the effectiveness of ML-accelerated docking to rapidly identify novel chemical series and provide a strong foundation for the development of SARS-CoV-2 NSP3 Mac1 inhibitors.

CACHE challenge↗