Targeting JMJD1C to selectively disrupt tumor Treg cell fitness enhances antitumor immunity.

Regulatory T (Treg) cells are critical for immune tolerance but also form a barrier to antitumor immunity. As therapeutic strategies involving Treg cell depletion are limited by concurrent autoimmune disorders, identification of intratumoral Treg cell-specific regulatory mechanisms is needed for selective targeting. Epigenetic modulators can be targeted with small compounds, but intratumoral Treg cell-specific epigenetic regulators have been unexplored. Here, we show that JMJD1C, a histone demethylase upregulated by cytokines in the tumor microenvironment, is essential for tumor Treg cell fitness but dispensable for systemic immune homeostasis. JMJD1C deletion enhanced AKT signals in a manner dependent on histone H3 lysine 9 dimethylation (H3K9me2) demethylase and STAT3 signals independently of H3K9me2 demethylase, leading to robust interferon-γ production and tumor Treg cell fragility. We have also developed an oral JMJD1C inhibitor that suppresses tumor growth by targeting intratumoral Treg cells. Overall, this study identifies JMJD1C as an epigenetic hub that can integrate signals to establish tumor Treg cell fitness, and we present a specific JMJD1C inhibitor that can target tumor Treg cells without affecting systemic immune homeostasis.

Cytoplasmic DNA sensing by KU complex in aged CD4+ T cell potentiates T cell activation and aging-related autoimmune inflammation.

Aging is associated with DNA accumulation and increased homeostatic proliferation of circulating T cells. Although these attributes are associated with aging-related autoimmunity, their direct contributions remain unclear. Conventionally, KU complex, the regulatory subunit of DNA-dependent protein kinase (DNA-PK), together with the catalytic subunit of DNA-PK (DNA-PKcs), mediates DNA damage repair in the nucleus. Here, we found KU complex abundantly expressed in the cytoplasm, where it recognized accumulated cytoplasmic DNA in aged human and mouse CD4+ T cells. This process enhanced T cell activation and pathology of experimental autoimmune encephalomyelitis (EAE) in aged mice. Mechanistically, KU-mediated DNA sensing facilitated DNA-PKcs recruitment and phosphorylation of the kinase ZAK. This activated AKT and mTOR pathways, promoting CD4+ T cell proliferation and activation. We developed a specific ZAK inhibitor, which dampened EAE pathology in aged mice. Overall, these findings demonstrate a KU-mediated cytoplasmic DNA-sensing pathway in CD4+ T cells that potentiates aging-related autoimmunity.

Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining.

Accurate prediction of molecular properties is fundamental in drug discovery and development, providing crucial guidance for effective drug design. A critical factor in achieving accurate molecular property prediction lies in the appropriate representation of molecular structures. Presently, prevalent deep learning-based molecular representations rely on 2D structure information as the primary molecular representation, often overlooking essential three-dimensional (3D) conformational information due to the inherent limitations of 2D structures in conveying atomic spatial relationships. In this study, we propose employing the Gram matrix as a condensed representation of 3D molecular structures and for efficient pretraining objectives. Subsequently, we leverage this matrix to construct a novel molecular representation model, Pre-GTM, which inherently encapsulates 3D information. The model accurately predicts the 3D structure of a molecule by estimating the Gram matrix. Our findings demonstrate that Pre-GTM model outperforms the baseline Graphormer model and other pretrained models in the QM9 and MoleculeNet quantitative property prediction task. The integration of the Gram matrix as a condensed representation of 3D molecular structure, incorporated into the Pre-GTM model, opens up promising avenues for its potential application across various domains of molecular research, including drug design, materials science, and chemical engineering.

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

SNX10-mediated LPS sensing causes intestinal barrier dysfunction via a caspase-5-dependent signaling cascade.

Altered intestinal microbial composition promotes intestinal barrier dysfunction and triggers the initiation and recurrence of inflammatory bowel disease (IBD). Current treatments for IBD are focused on control of inflammation rather than on maintaining intestinal epithelial barrier function. Here, we show that the internalization of Gram-negative bacterial outer membrane vesicles (OMVs) in human intestinal epithelial cells promotes recruitment of caspase-5 and PIKfyve to early endosomal membranes via sorting nexin 10 (SNX10), resulting in LPS release from OMVs into the cytosol. Caspase-5 activated by cytosolic LPS leads to Lyn phosphorylation, which in turn promotes nuclear translocalization of Snail/Slug, downregulation of E-cadherin expression, and intestinal barrier dysfunction. SNX10 deletion or treatment with DC-SX029, a novel SNX10 inhibitor, rescues OMV-induced intestinal barrier dysfunction and ameliorates colitis in mice by blocking cytosolic LPS release, caspase-5 activation, and downstream signaling. Our results show that targeting SNX10 may be a new therapeutic approach for restoring intestinal epithelial barrier function and promising strategy for IBD treatment.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Goliath induces inflammation in obese mice by linking fatty acid ß-oxidation to glycolysis.

Obesity is associated with metabolic disorders and chronic inflammation. However, the obesity-associated metabolic contribution to inflammatory induction remains elusive. Here, we show that, compared with lean mice, CD4+ T cells from obese mice exhibit elevated basal levels of fatty acid ß-oxidation (FAO), which promote T cell glycolysis and thus hyperactivation, leading to enhanced induction of inflammation. Mechanistically, the FAO rate-limiting enzyme carnitine palmitoyltransferase 1a (Cpt1a) stabilizes the mitochondrial E3 ubiquitin ligase Goliath, which mediates deubiquitination of calcineurin and thus enhances activation of NF-AT signaling, thereby promoting glycolysis and hyperactivation of CD4+ T cells in obesity. We also report the specific GOLIATH inhibitor DC-Gonib32, which blocks this FAO-glycolysis metabolic axis in CD4+ T cells of obese mice and reduces the induction of inflammation. Overall, these findings establish a role of a Goliath-bridged FAO-glycolysis axis in mediating CD4+ T cell hyperactivation and thus inflammation in obese mice.

RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.

Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the 'digitalization' (also known as 'encoding') of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction. Herein, RNAincoder (deep learning-based encoder for RNA-associated interactions) was therefore proposed to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools. RNAincoder can be accessed at https://idrblab.org/rnaincoder/.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.

Design, synthesis, and bioevaluation of SOS1 PROTACs derived from pyrido[2,3-d]pyrimidin-7-one-based SOS1 inhibitor.

Oncogenic KRAS mutations drive an approximately 25 % of all human cancers. Son of Sevenless 1 (SOS1), a critical guanine nucleotide exchange factor, catalyzes the activation of KRAS. Targeting SOS1 degradation has engaged as a promising therapeutic strategy for KRAS-mutant cancers. Herein, we designed and synthesized a series of novel CRBN-recruiting SOS1 PROTACs using the pyrido[2,3-d]pyrimidin-7-one-based SOS1 inhibitor as the warhead. One representative compound 11o effectively induced the degradation of SOS1 in three different KRAS-mutant cancer cell lines with DC50 values ranging from 1.85 to 7.53 nM. Mechanism studies demonstrated that 11o-induced SOS1 degradation was dependent on CRBN and proteasome. Moreover, 11o inhibited the phosphorylation of ERK and displayed potent anti-proliferative activities against SW620, A549 and DLD-1 cells. Further optimization of 11o may provide us promising SOS1 degraders with favorable drug-like properties for developing new chemotherapies targeting KRAS-driven cancers.

Modifiable and Non-Modifiable Risk Factors for Premature Coronary Heart Disease (PCHD): Systematic Review and Meta-Analysis.

AIM: We aimed to compare the prevalence of modifiable and non-modifiable coronary heart disease (CHD) risk factors among those with premature CHD and healthy individuals. METHODS: PubMed, CINAHL, Embase, and Web of Science databases were searched (review protocol is registered in PROSPERO CRD42020173216). The quality of studies was assessed using the National Heart, Lung and Blood Institute tool for cross-sectional, cohort and case-control studies. Meta-analyses were performed using Review Manager 5.3. Effect sizes for categorical and continuous variables, odds ratio (OR) and mean differences (MD)/standardised mean differences (SMD) with 95% confidence intervals (CI) were reported. RESULTS: A total of n=208 primary studies were included in this review. Individuals presenting with premature CHD (PCHD, age ≤65 years) had higher mean body mass index (MD 0.54 kg/m2, 95% CI 0.24, 0.83), total cholesterol (SMD 0.27, 95% CI 0.17, 0.38), triglycerides (SMD 0.50, 95% CI 0.41, 0.60) and lower high-density lipoprotein cholesterol (SMD 0.79, 95% CI: -0.91, -0.68) compared with healthy individuals. Individuals presenting with PCHD were more likely to be smokers (OR 2.88, 95% CI 2.51, 3.31), consumed excessive alcohol (OR 1.40, 95% CI 1.05, 1.86), had higher mean lipoprotein (a) levels (SMD 0.41, 95% CI 0.28, 0.54), and had a positive family history of CHD (OR 3.65, 95% CI 2.87, 4.66) compared with healthy individuals. Also, they were more likely to be obese (OR 1.59, 95% CI 1.32, 1.91), and to have had dyslipidaemia (OR 2.74, 95% CI 2.18, 3.45), hypertension (OR 2.80, 95% CI 2.28, 3.45), and type 2 diabetes mellitus (OR 2.93, 95% CI 2.50, 3.45) compared with healthy individuals. CONCLUSION: This meta-analysis confirms current knowledge of risk factors for PCHD, and identifying these early may reduce CHD in young adults.

A cross-sectional survey on COVID-19 phobia and its influencing factors among undergraduate nursing students.

BACKGROUND: The COVID-19 pandemic has significantly impacted the psychological well-being of undergraduate nursing students in China. It is vital to have an understanding of their COVID-19 phobia and its predictors, especially during transitions in public health policy. OBJECTIVE: This study aims to evaluate the situation and factors contributing to COVID-19 phobia among nursing students in Southwestern China. METHODS: A cross-sectional study was conducted in December 2022 among nursing undergraduates in southwestern China. A convenience sample of 317 undergraduate nursing students from all grades at a medical university in Chengdu was assessed using the COVID-19 Phobia Scale (C19P). RESULTS: The mean COVID-19 Phobia Scale (C19P-SC) score was 52.92 (± 13.02), indicating moderate levels of phobia, with gender, chronic disease, and perceived susceptibility being significant predictors. 11.67% of the students reported an infection history, while 81.39% knew an infected individual. Notably, fourth-year students showed significantly higher phobia level than first-year. Gender, chronic disease, perceived susceptibility, and risk significantly predicted COVID-19 phobia, explaining 16.4% of the variance Results of the thematic analysis revealed four main themes related to COVID-19 phobia and career choice among nursing undergraduates: concerns of infection risk, professional commitment, compromise and acceptance, and confronting phobia. CONCLUSIONS: This study discovered a significant level of COVID-19 phobia among undergraduate nursing students and identified several risk factors, including being female, having chronic diseases, perceiving a high susceptibility to the virus, and perceiving a high level of harm after infection. These findings highlight the importance of educators focusing on the mental well-being of nursing students, particularly seniors, to reduce the impact of phobia on their social interactions and career aspirations. This will help ensure that they are well-prepared for their future roles in healthcare.

Multi-instance learning of graph neural networks for aqueous pKa prediction.

MOTIVATION: The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-consuming, especially for the exact determination of micro-pKa information at the atomic level. Hence, a fast and accurate prediction of pKa values of chemical compounds is of broad interest. RESULTS: Here, we compiled a large-scale pKa dataset containing 16 595 compounds with 17 489 pKa values. Based on this dataset, a novel pKa prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro-pKa values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pKa was also able to automatically deconvolute the predicted macro-pKa into discrete micro-pKa values. AVAILABILITY AND IMPLEMENTATION: The Graph-pKa model is now freely accessible via a web-based interface (https://pka.simm.ac.cn/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Do patients with prediabetes managed with metformin achieve better glycaemic control? A national study using primary care medical records.

AIMS: To estimate the effectiveness of metformin on glycaemic parameters among participants with incident prediabetes attending Australian general practices. METHODS: This retrospective cohort study used electronic health records of regular participants (3+ visits in two consecutive years) attending 383 Australian general practices (MedicineInsight). Participants with 'incident' prediabetes (newly recorded diagnosis between 2012 and 2017) and their glycaemic parameters (haemoglobin A1c [HbA1c] or fasting blood glucose [FBG]) at 6-, 12-, and 18-24 months post diagnosis (unexposed) or post-management with metformin (treatment) were identified from the database. We estimated the average treatment effect (ATE) of metformin management on glycaemic parameters using both linear regression and augmented inverse probability weighting. RESULTS: Of the 4770 investigated participants with 'incident' prediabetes, 10.2% were managed with metformin. Participants on metformin had higher HbA1c levels at the baseline than those unexposed (mean 45 mmol/mol [6.2%] and 41 mmol/mol [5.9%], respectively), but no differences were observed at 6-12 months (mmol/mol ATE 0.0, 95% CI -0.4; 0.7) or 12-18 months (ATE -0.3, 95% CI -1.2; 0.3). However, participants on metformin had lower mean HbA1c mmol/mol at 18-24 months (ATE -1.1, 95% CI -2.0; 0.1) than those unexposed. Consistent results were observed for FBG (ATE at 6-12 months -0.14 [95% CI -0.25; -0.04], 12-18 months 0.02 [95% CI -0.08; 0.13] and 18-24 months -0.07 [95% CI -0.25; 0.12]). CONCLUSION: The higher HbA1c and FBG baseline levels among participants with 'incident' prediabetes managed with metformin improved after 6-12 months of starting pharmacological management, and the effect persisted for up to 24 months. Management with metformin could prevent further deterioration of glycaemic levels.

143D, a novel selective KRASG12C inhibitor exhibits potent antitumor activity in preclinical models.

The KRASG12C mutant has emerged as an important therapeutic target in recent years. Covalent inhibitors have shown promising antitumor activity against KRASG12C-mutant cancers in the clinic. In this study, a structure-based and focused chemical library analysis was performed, which led to the identification of 143D as a novel, highly potent and selective KRASG12C inhibitor. The antitumor efficacy of 143D in vitro and in vivo was comparable with that of AMG510 and of MRTX849, two well-characterized KRASG12C inhibitors. At low nanomolar concentrations, 143D showed biochemical and cellular potency for inhibiting the effects of the KRASG12C mutation. 143D selectively inhibited cell proliferation and induced G1-phase cell cycle arrest and apoptosis by downregulating KRASG12C-dependent signal transduction. Compared with MRTX849, 143D exhibited a longer half-life and higher maximum concentration (Cmax) and area under the curve (AUC) values in mouse models, as determined by tissue distribution assays. Additionally, 143D crossed the blood‒brain barrier. Treatment with 143D led to the sustained inhibition of KRAS signaling and tumor regression in KRASG12C-mutant tumors. Moreover, 143D combined with EGFR/MEK/ERK signaling inhibitors showed enhanced antitumor activity both in vitro and in vivo. Taken together, our findings indicate that 143D may be a promising drug candidate with favorable pharmaceutical properties for the treatment of cancers harboring the KRASG12C mutation.

Discovery and identification of a novel small molecule BCL-2 inhibitor that binds to the BH4 domain.

The B-cell lymphoma 2 (BCL-2) protein family plays a pivotal role in regulating the apoptosis process. BCL-2, as an antiapoptotic protein in this family, mediates apoptosis resistance and is an ideal target for cell death strategies in cancer therapy. Traditional treatment modalities target BCL-2 by occupying the hydrophobic pocket formed by BCL-2 homology (BH) domains 1-3, while in recent years, the BH4 domain of BCL-2 has also been considered an attractive novel target. Herein, we describe the discovery and identification of DC-B01, a novel BCL-2 inhibitor targeting the BH4 domain, through virtual screening combined with biophysical and biochemical methods. Our results from surface plasmon resonance and cellular thermal shift assay confirmed that the BH4 domain is responsible for the interaction between BCL-2 and DC-B01. As evidenced by further cell-based experiments, DC-B01 induced cell killing in a BCL-2-dependent manner and triggered apoptosis via the mitochondria-mediated pathway. DC-B01 disrupted the BCL-2/c-Myc interaction and consequently suppressed the transcriptional activity of c-Myc. Moreover, DC-B01 inhibited tumor growth in vivo in a BCL­2­dependent manner. Collectively, these results indicate that DC-B01 is a promising BCL-2 BH4 domain inhibitor with the potential for further development.

Discovery and characterization of a novel cGAS covalent inhibitor for the treatment of inflammatory bowel disease.

Cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, acts as a nucleotidyl transferase that catalyzes ATP and GTP to form cyclic GMP-AMP (cGAMP) and plays a critical role in innate immunity. Hyperactivation of cGAS-STING signaling contributes to hyperinflammatory responses. Therefore, cGAS is considered a promising target for the treatment of inflammatory diseases. Herein, we report the discovery and identification of several novel types of cGAS inhibitors by pyrophosphatase (PPiase)-coupled activity assays. Among these inhibitors, 1-(1-phenyl-3,4-dihydro-1H-pyrrolo[1,2-a]pyrazin-2-yl)prop-2-yn-1-one (compound 3) displayed the highest potency and selectivity at the cellular level. Compound 3 exhibited better inhibitory activity and pathway selectivity than RU.521, which is a selective cGAS inhibitor with anti-inflammatory effects in vitro and in vivo. Thermostability analysis, nuclear magnetic resonance and isothermal titration calorimetry assays confirmed that compound 3 directly binds to the cGAS protein. Mass spectrometry and mutation analysis revealed that compound 3 covalently binds to Cys419 of cGAS. Notably, compound 3 demonstrated promising therapeutic efficacy in a dextran sulfate sodium (DSS)-induced mouse colitis model. These results collectively suggest that compound 3 will be useful for understanding the biological function of cGAS and has the potential to be further developed for inflammatory disease therapies.