NPJ drug discovery最新文献_第5页

Vector venom: venomics of Aedes albopictus reveals a large enzyme repertoire and novel cecropins with activity against E. coli. 载体毒液：白纹伊蚊的毒液组学揭示了大量的酶库和具有抗大肠杆菌活性的新型抗菌素。

NPJ drug discovery Pub Date : 2026-03-02 DOI: 10.1038/s44386-026-00041-w

Ludwig Dersch, Jonas Krämer, Sabine Hurka, Maik Damm, Ole Bohlken, Alejandra Centurión, Bodunrin Omokungbe, Lennart Schulte, Michael Marner, Kornelia Hardes, Till F Schäberle, Andreas Vilcinskas, Tim Lüddecke

{"title":"Vector venom: venomics of Aedes albopictus reveals a large enzyme repertoire and novel cecropins with activity against E. coli.","authors":"Ludwig Dersch, Jonas Krämer, Sabine Hurka, Maik Damm, Ole Bohlken, Alejandra Centurión, Bodunrin Omokungbe, Lennart Schulte, Michael Marner, Kornelia Hardes, Till F Schäberle, Andreas Vilcinskas, Tim Lüddecke","doi":"10.1038/s44386-026-00041-w","DOIUrl":"10.1038/s44386-026-00041-w","url":null,"abstract":"Mosquitoes are vectors of deadly diseases and pose a global health threat. Particularly, the Asian tiger mosquito Aedes albopictus can transmit several pathogens, and is expanding into temperate regions. During blood feeding, mosquitoes inject chemically complex saliva, here referred to as venom, which modulates hemostasis, inflammation, immune response and pathogen transmission. In-depth knowledge of mosquito venom is crucial for understanding disease biology and enabling biodiscovery. We present a venomics study of Ae. albopictus and identify 119 distinct proteins validated by mass spectrometry and transcriptomics. The venom is rich in enzymes (e.g., hydrolases and Apyrases) and non-enzymatic components (e.g., odorant binding proteins and protease inhibitors). Additionally, we identified six novel cecropin family antimicrobial peptides. Structural analyses indicate an amphipathic N-terminus, hinge region, and hydrophobic C-terminus consistent with type II channel formation. Functional assays revealed that these cecropins exert potent effects on E. coli while leaving mammalian epithelial cells and erythrocytes unaffected. Overall, our study reveals that mosquito venom is a source of diverse biomacromolecules, deepening our understanding of its physiology, vector biology, and biochemical ecology. This opens paths for new mosquito-control strategies and drug discovery.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preferential HER4 stimulation preserves neuregulin-induced improvement of myocardial function. 优先刺激HER4可保留神经调节蛋白诱导的心肌功能改善。

NPJ drug discovery Pub Date : 2026-03-02 DOI: 10.1038/s44386-026-00038-5

Samuel L Murphy, Wai Hong Wilson Tang, Xiaolei Zhuang, John Li

{"title":"Preferential HER4 stimulation preserves neuregulin-induced improvement of myocardial function.","authors":"Samuel L Murphy, Wai Hong Wilson Tang, Xiaolei Zhuang, John Li","doi":"10.1038/s44386-026-00038-5","DOIUrl":"10.1038/s44386-026-00038-5","url":null,"abstract":"Neuregulin-1 (NRG-1), the natural ligand for HER3 and HER4, has been established as an essential growth factor for heart function. Prior studies have demonstrated the therapeutic potential of NRG-1 in the treatment of heart failure, but HER3 receptor activation has been a concern given its cancer association. Here, we describe the novel antibody fusion protein JK07, consisting of an antagonistic HER3 antibody and the EGF-binding domain of NRG-1. We confirm the HER3 antibody and the NRG-1 domains both retain functional activity in vitro and in vivo. Importantly, we demonstrate here for the first time that preserving HER4 stimulation by NRG1 in the presence of attenuated HER3 stimulation is sufficient to realize the therapeutic effects of NRG-1 in treatment of heart failure. Comparing JK07 to a control NRG-1 antibody fusion which does not recognize HER3, we show that JK07 can achieve equivalent recovery of ejection fraction in a rodent model of chronic heart failure. Finally, JK07 exhibited therapeutic potential in heart failure in rhesus macaques. In conclusion, this work demonstrates selective HER4 activation is sufficient for NRG-1 to improve myocardial function in multiple pre-clinical heart failure models and that JK07 holds promise as a potential therapeutic intervention for heart failure.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interaction-constrained 3D molecular generation using a diffusion model enables structure-based pharmacophore modeling for drug design. 使用扩散模型的相互作用约束的3D分子生成使基于结构的药效团建模用于药物设计。

NPJ drug discovery Pub Date : 2026-03-02 DOI: 10.1038/s44386-026-00040-x

Masami Sako, Nobuaki Yasuo, Masakazu Sekijima

{"title":"Interaction-constrained 3D molecular generation using a diffusion model enables structure-based pharmacophore modeling for drug design.","authors":"Masami Sako, Nobuaki Yasuo, Masakazu Sekijima","doi":"10.1038/s44386-026-00040-x","DOIUrl":"10.1038/s44386-026-00040-x","url":null,"abstract":"A key challenge in structure-based drug design is generating three-dimensional molecules while preserving essential protein-ligand interactions. We propose DiffPharma, a structure-based pharmacophore modeling framework based on a conditional diffusion model, to generate molecules that satisfy specified interaction constraints. The proposed method incorporates a semantic fusion architecture that integrates multiple interaction-specific neural networks, each designed to capture distinct molecular interactions such as hydrogen bonds and hydrophobic interactions. Experimental results demonstrate that DiffPharma achieves a residue-level interaction similarity of up to 0.9, significantly outperforming baseline models. To assess the method's generalizability, ligands were generated for AKT serine/threonine kinase 1 and serine β-lactamase, successfully preserving key interaction features. The effectiveness of the method is demonstrated through a practical case study targeting the SARS-CoV-2 main protease. Molecular dynamics simulations indicate that the generated molecules maintain both structural stability and key interactions comparable to those of a bioactive reference ligand. In addition, the molecular mechanics generalized Born surface area (MM/GBSA) calculations based on MD trajectories suggest that several generated molecules may exhibit relatively favorable binding tendencies compared with the reference. The implementation of the DiffPharma, including code and an execution environment on Google Colab, is available under the MIT license at GitHub: https://github.com/sekijima-lab/DiffPharma .","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-guided competitive docking for virtual screening and compound efficacy prediction. 人工智能引导下的虚拟筛选与复合疗效预测竞争对接。

NPJ drug discovery Pub Date : 2026-02-16 DOI: 10.1038/s44386-026-00039-4

Manon Mirgaux, Valeria Barcelli, Adeline C Y Chua, Pablo Bifani, René Wintjens

{"title":"AI-guided competitive docking for virtual screening and compound efficacy prediction.","authors":"Manon Mirgaux, Valeria Barcelli, Adeline C Y Chua, Pablo Bifani, René Wintjens","doi":"10.1038/s44386-026-00039-4","DOIUrl":"10.1038/s44386-026-00039-4","url":null,"abstract":"Machine learning has revolutionized protein structure and interaction prediction, yet its full potential for drug discovery is still emerging. In this study, we show that denoise diffusion-based co-folding methods-such as AlphaFold3 and Boltz-1/2-not only achieve highly accurate protein-ligand interaction predictions but can also separate active compounds from inactive ones. We introduce a simple and effective strategy, pairwise competitive docking, which ranks candidate molecules by directly comparing their relative binding to a protein's target site. Applied to 17 protein benchmark systems, the method generated rankings consistent with experimental trends, although the degree of agreement varied considerably by system, with concordance indices ranging from 0.52 (indicating no meaningful correlation) to 0.89 (indicating strong correlation). Notably, our rankings showed strong agreement with Boltz-2 affinity predictions, positioning our method as a practical alternative for inhibitor prioritization. Finally, we show how pairwise competitive docking can accelerate the identification of promising hits within a large chemical library and guide the de novo design of inhibitors with improved predicted potency. Collectively, these findings highlight how modern machine-learning models can make structure-based drug design faster, more reliable, and more cost-effective than relying solely on experimental workflows.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma. 一种系统级机器学习方法揭示了透明细胞肾细胞癌的治疗靶点。

NPJ drug discovery Pub Date : 2026-02-09 DOI: 10.1038/s44386-025-00036-z

Silas Ruhrberg Estévez, Greta Baltusyte, Gehad Youssef, Namshik Han

{"title":"A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma.","authors":"Silas Ruhrberg Estévez, Greta Baltusyte, Gehad Youssef, Namshik Han","doi":"10.1038/s44386-025-00036-z","DOIUrl":"10.1038/s44386-025-00036-z","url":null,"abstract":"Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with limited treatment options and high rates of resistance to first-line kinase inhibitors. Current therapies largely target the tumor microenvironment, leaving intrinsic tumor vulnerabilities underexplored. Here, we introduce a systems-based machine learning pipeline that integrates single-cell RNA sequencing, protein interaction networks, and drug proximity analysis to identify therapeutic targets in ccRCC. Candidate genes were refined using CRISPR screening data and functional relevance and validated across independent transcriptomic datasets. The pipeline recovered several established treatment pathways and uncovered previously underexplored therapeutic mechanisms, including ABL1, CDK4/6, and JAK inhibition. We identified FDA-approved compounds acting through these pathways, three of which, Ribociclib, Ponatinib, and Dasatinib, showed superior efficacy to current therapies across renal cancer cell lines in preclinical screens. By acting through mechanisms distinct from current therapies, they represent promising candidates for combination strategies aimed at overcoming resistance and improving clinical outcomes in ccRCC.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13252859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in RNA-based therapies from bench to bedside. rna疗法从实验到临床的进展。

NPJ drug discovery Pub Date : 2026-02-09 DOI: 10.1038/s44386-025-00037-y

Amrita Singh, Muktika Tekade, Sreeharsha Nagaraja, Azad Bharti, Rakesh Kumar Tekade

引用次数: 0

Quantum-machine-assisted drug discovery. 量子机器辅助药物发现。

NPJ drug discovery Pub Date : 2026-01-07 DOI: 10.1038/s44386-025-00033-2

Yidong Zhou, Jintai Chen, Jinglei Cheng, Xu Cao, Yuanyuan Zhang, Gopal Karemore, Marinka Zitnik, Frederic T Chong, Junyu Liu, Tianfan Fu, Zhiding Liang

引用次数: 0

A genetics-guided integrative framework for drug repurposing: identifying antihypertensive drugs for type 2 diabetes. 药物再利用的遗传学指导综合框架：确定2型糖尿病的降压药物。

NPJ drug discovery Pub Date : 2026-01-01 Epub Date: 2026-02-09 DOI: 10.1038/s44386-025-00032-3

Xue Zhong, Qiang Wei, Anshul Tiwari, Quan Wang, Yuting Tan, Rui Chen, Yan Yan, Nancy J Cox, Bingshan Li

{"title":"A genetics-guided integrative framework for drug repurposing: identifying antihypertensive drugs for type 2 diabetes.","authors":"Xue Zhong, Qiang Wei, Anshul Tiwari, Quan Wang, Yuting Tan, Rui Chen, Yan Yan, Nancy J Cox, Bingshan Li","doi":"10.1038/s44386-025-00032-3","DOIUrl":"10.1038/s44386-025-00032-3","url":null,"abstract":"Genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D Genome-Wide Association Study (GWAS) signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. We identified telmisartan that belongs to the Angiotensin Receptor Blockers (ARB) drug category as a candidate, and validated the ARB antihypertensive drugs' potential therapeutic effects on T2D in Vanderbilt University Medical Center (VUMC)'s Electronic Health Records (EHR) data with over 3 million patients.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of potent, selective cPLA₂ inhibitors for targeting neuroinflammation in Alzheimer's disease and other neurodegenerative disorders. 开发针对阿尔茨海默病和其他神经退行性疾病的神经炎症的强效，选择性cPLA2抑制剂。

NPJ drug discovery Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1038/s44386-025-00035-0

Anastasiia V Sadybekov, Marlon Vincent Duro, Shaowei Wang, Brandon Ebright, Dante Dikeman, Cristelle Hugo, Bilal Ersen Kerman, Qiu-Lan Ma, Antonina L Nazarova, Arman A Sadybekov, Isaac Asante, Stan G Louie, Vsevolod Katritch, Hussein N Yassine

{"title":"Development of potent, selective cPLA2 inhibitors for targeting neuroinflammation in Alzheimer's disease and other neurodegenerative disorders.","authors":"Anastasiia V Sadybekov, Marlon Vincent Duro, Shaowei Wang, Brandon Ebright, Dante Dikeman, Cristelle Hugo, Bilal Ersen Kerman, Qiu-Lan Ma, Antonina L Nazarova, Arman A Sadybekov, Isaac Asante, Stan G Louie, Vsevolod Katritch, Hussein N Yassine","doi":"10.1038/s44386-025-00035-0","DOIUrl":"10.1038/s44386-025-00035-0","url":null,"abstract":"Chronic neuroinflammation plays a key role in the progression of Alzheimer's disease (AD), and the cytosolic calcium-dependent phospholipase A2 (cPLA2) enzyme is a critical mediator of inflammatory lipid signaling pathways. Here we investigate the therapeutic potential of novel cPLA2 inhibitors in modulating neuroinflammation in AD. By leveraging the giga-scale V-SYNTHES2 virtual screening in on-demand chemical space and conducting two rounds of optimization for potency and selectivity, we have identified BRI-50460, achieving an IC50 of 0.88 nM in cellular assays that measure cPLA2-mediated arachidonic acid release. In vivo studies revealed favorable brain-to-plasma ratios, highlighting the ability of BRI-50460 to penetrate the central nervous system, modulating neuroinflammatory pathways, and restoring lipid homeostasis. In astrocytes and neurons derived from human induced pluripotent stem cells, BRI-50460 mitigates the effects of amyloid beta 42 oligomers on cPLA2 activation, tau hyperphosphorylation, and synaptic loss. Our results support that small molecule inhibitors of cPLA2 can modulate the downstream inflammatory signaling, offering a promising therapeutic strategy for neurodegenerative diseases.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12779542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145954886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovation breakthrough in the Alzheimer's disease pharmaceutical industry. 阿尔茨海默病医药行业的创新突破。

NPJ drug discovery Pub Date : 2026-01-01 Epub Date: 2026-03-02 DOI: 10.1038/s44386-026-00044-7

Hua Cheng, Leyi Kang, Yulong Xu, Rudolph E Tanzi, Can Zhang, Changning Wang

引用次数: 0