NPJ drug discovery最新文献_第4页

A novel small molecule remyelination therapy for multiple sclerosis. 一种新的小分子髓鞘再生治疗多发性硬化症。

NPJ drug discovery Pub Date : 2026-07-23 DOI: 10.1038/s44386-026-00060-7

Yan Yang, Brain Bai, Lars J S Knutsen, Richard J Hamlyn, Qiao-Ling Cui, Jack Antel, Wendy Macklin, Satish Medicetty, Bruce D Trapp

{"title":"A novel small molecule remyelination therapy for multiple sclerosis.","authors":"Yan Yang, Brain Bai, Lars J S Knutsen, Richard J Hamlyn, Qiao-Ling Cui, Jack Antel, Wendy Macklin, Satish Medicetty, Bruce D Trapp","doi":"10.1038/s44386-026-00060-7","DOIUrl":"10.1038/s44386-026-00060-7","url":null,"abstract":"We conducted a phenotypic screen of 20,000 central nervous system (CNS)-biased small molecules for their ability to stimulate differentiation of mouse oligodendrocyte progenitor cells (OPCs) into oligodendrocytes. We identified a lead hit (CN045) with an EC50 of 40 nM in the OPC differentiation assay and a chemical scaffold conducive to modifications. In OPC differentiation assays, CN045 demonstrated higher potency than a known OPC differentiation compound Triiodothyronine (T3). CN045 promoted myelin-like ensheathment of engineered nanofibers by mouse and human OPCs and significantly increased remyelination in white and gray matter regions of mouse brain following cuprizone/rapamycin-induced demyelination. In terms of pharmacokinetics, CN045 is CNS-penetrable with low cytotoxicity. CN045 has a short half-life in vivo, but its chemical scaffold is conducive to structural modifications that can improve its metabolic properties. Collectively, these results demonstrate that CN045 is an attractive lead candidate for enhancing OPC differentiation and remyelination in multiple sclerosis patients.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13396656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148581698","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

Prioritizing neuroactive ligands using motif-guided virtual discovery and zebrafish profiling. 使用基序引导的虚拟发现和斑马鱼分析优先考虑神经活性配体。

NPJ drug discovery Pub Date : 2026-07-21 DOI: 10.1038/s44386-026-00058-1

Ari B Ginsparg, Jaqueline A Martinez, Ishaan Patel, Angélique Buton, Laura T Lee, Raheel Sarwar, Rocco Moretti, Stephanie Puig, Summer B Thyme

{"title":"Prioritizing neuroactive ligands using motif-guided virtual discovery and zebrafish profiling.","authors":"Ari B Ginsparg, Jaqueline A Martinez, Ishaan Patel, Angélique Buton, Laura T Lee, Raheel Sarwar, Rocco Moretti, Stephanie Puig, Summer B Thyme","doi":"10.1038/s44386-026-00058-1","DOIUrl":"10.1038/s44386-026-00058-1","url":null,"abstract":"Virtual screening of ultra-large chemical libraries is a highly effective strategy for early-stage ligand discovery. However, these pipelines often yield thousands of molecules that pass computational filters, and in silico-derived interaction energies do not consistently predict experimental efficacy. Furthermore, many high-affinity hits do not necessarily function effectively in an organism with tissues, barriers, and extensive off-target possibilities. Here, we establish and apply an integrated pipeline that prioritizes candidate molecules through structure-guided computation and zebrafish-based functional profiling. We introduce Rosetta Engine for Anchoring Ligands with a Motif (\"REAL-M\"), a screening algorithm that uses structural interaction data from the Protein Data Bank (PDB) to guide ligand placement and selection. Using the hypocretin receptor as a test case, 28 of 30 predicted antagonists significantly blocked binding of the cognate peptide agonist in a cell-based reporter assay, including six chemically diverse molecules with efficacy comparable to preexisting antagonists. Three of these six significantly mitigated hypocretin-induced larval zebrafish hyperactivity. Secondary testing with a zebrafish hcrtr2 null mutant ensured that behavioral phenotypes were on target, in contrast to off-target phenotypes observed with a clinically approved antagonist. This pipeline is readily adaptable to the thousands of zebrafish proteins with highly conserved binding pockets.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13389409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148552000","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

Targeting druggable kinases in neuropathic pain. 靶向药物激酶治疗神经性疼痛。

NPJ drug discovery Pub Date : 2026-07-21 DOI: 10.1038/s44386-026-00055-4

Harrison Benson, Komalpreet Kaur, Mahmoud Salama Ahmed

引用次数: 0

Generative AI for controllable protein sequence design: A survey. 生成式人工智能在可控蛋白质序列设计中的应用综述。

NPJ drug discovery Pub Date : 2026-07-03 DOI: 10.1038/s44386-026-00054-5

Yiheng Zhu, Zitai Kong, Jialu Wu, Mingze Yin, Weize Liu, Yuqiang Han, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu

引用次数: 0

Efficient and valid large molecule generation via self-supervised generative models. 基于自监督生成模型的高效有效大分子生成。

NPJ drug discovery Pub Date : 2026-07-03 DOI: 10.1038/s44386-026-00052-7

Doyoung Kwak, Md Raiyan Chowdhury, Byung-Jun Yoon, Xiaoning Qian

{"title":"Efficient and valid large molecule generation via self-supervised generative models.","authors":"Doyoung Kwak, Md Raiyan Chowdhury, Byung-Jun Yoon, Xiaoning Qian","doi":"10.1038/s44386-026-00052-7","DOIUrl":"10.1038/s44386-026-00052-7","url":null,"abstract":"The realm of molecular design, particularly for large molecules, presents unique challenges and opportunities in drug discovery and materials science. Large molecule design is inherently more complex and less explored compared to designing small molecules, adding significant difficulty in generative modeling. We aim to establish strong baselines for better scalability, efficiency, and generative performance in this domain. We evaluate the scalability and performance of generative AI models, initially effective for small molecule design, in generating large molecules for potential drugs in gene-based therapies, immunotherapies, hormonal regulators, and targeted cancer therapies. Our findings indicate that computational strategies and model architectures designed for small molecules may not readily extend to large molecular structures. To address these limitations, we explore masked language modeling strategies alongside advanced tokenization methods, including Atom-Pair Encoding (APE), to enhance generative AI models. We probe how incorporating such strategies, particularly the APE tokenization method that explicitly captures structural and chemical characteristics, can significantly improve design capabilities for complex molecular structures. Overall, our results demonstrate both the potential and challenges of deep generative modeling for large molecules and how the proposed enhancements may bridge the gap in generating large molecules when novel discovery is the ultimate goal.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383546","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

V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces. v - synthesis -下一代基于结构的千兆级化学空间虚拟筛选工具。

NPJ drug discovery Pub Date : 2026-07-03 DOI: 10.1038/s44386-026-00053-6

Antonina L Nazarova, Anastasiia V Sadybekov, Arman A Sadybekov, Mykola Protopopov, Dmytro S Radchenko, Yurii S Moroz, Olga O Tarkhanova, Vsevolod Katritch

{"title":"V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.","authors":"Antonina L Nazarova, Anastasiia V Sadybekov, Arman A Sadybekov, Mykola Protopopov, Dmytro S Radchenko, Yurii S Moroz, Olga O Tarkhanova, Vsevolod Katritch","doi":"10.1038/s44386-026-00053-6","DOIUrl":"10.1038/s44386-026-00053-6","url":null,"abstract":"On-demand chemical spaces of drug-like compounds open new horizons in the discovery of ligands and drug candidates for clinically relevant targets, but also expose the scalability of computational screening as a key bottleneck. Recently, we introduced the V-SYNTHES modular screening approach, showing >1000-fold acceleration relative to direct brute-force docking of fully enumerated ultra-large chemical libraries. Initially, the method was based on the early version of Enamine REAL space (11 billion compounds) and validated on two relatively well-characterized targets. Here we present an upgraded V-SYNTHES2 workflow with improved automation features and scalability, expanding to REAL Space of 36 billion readily available compounds, and assessing its performance on new, more challenging targets. V-SYNTHES2 introduces a new geometry-based CapSelect tool that fully automates fragment selection based on docking scores and binding poses of the Minimal Enumeration Library (MEL). The method shows excellent enrichment and binding pose reproducibility in computational benchmarks, including targets with shallow pockets, RNA-binding sites, GPCRs, and phospholipid-binding enzymes. Experimental validation shows the utility of this workflow in prospective screening campaigns for two novel targets. The fully automated V-SYNTHES2 workflow can be deployed on computing clusters or clouds, offering a powerful tool for effective screening of giga-scale chemical spaces.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383529","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

Assessing the ligand native-like pose using a quantum mechanical-derived hydropathic score for protein-ligand complementarity. 利用量子力学衍生的蛋白质-配体互补性的亲水评分来评估配体的天然姿态。

NPJ drug discovery Pub Date : 2026-06-19 DOI: 10.1038/s44386-026-00048-3

Brian Medel-Lacruz, William J Zamora, Enric Herrero, Glen E Kellogg, Jana Selent, Javier Vázquez, F Javier Luque

{"title":"Assessing the ligand native-like pose using a quantum mechanical-derived hydropathic score for protein-ligand complementarity.","authors":"Brian Medel-Lacruz, William J Zamora, Enric Herrero, Glen E Kellogg, Jana Selent, Javier Vázquez, F Javier Luque","doi":"10.1038/s44386-026-00048-3","DOIUrl":"10.1038/s44386-026-00048-3","url":null,"abstract":"Elucidating the correct binding mode of drug-like compounds is crucial to reveal the molecular determinants underlying the recognition by the target protein and to estimate the binding affinity, thus guiding ensuing hit-to-lead optimization. However, identifying the near-native binding pose in docking experiments remains a major hurdle. Assuming that the hydropathic complementarity principle is the major driving force in ligand-protein recognition, the suitability of simple hydropathicity-based scoring functions to reveal the near-native pose was examined. A benchmarking dataset of 1000 ligand-protein complexes purposely designed to encompass bioactive and decoy poses was used to assess the performance of 3D hydropathicity atomic descriptors derived from empirical and quantum mechanical models. This strategy led to a predictive accuracy of ca. 90% when the non-negligible influence of the conformational stress is also considered. The results reveal the need to leverage an understanding of nonpolar/polar chemical features for the successful discrimination of the near-native pose.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13282475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364753","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

Mechanistic and therapeutic insights into class A GPCR dimers in neuropsychiatric disorders. A类GPCR二聚体在神经精神疾病中的机制和治疗见解。

NPJ drug discovery Pub Date : 2026-06-03 DOI: 10.1038/s44386-026-00049-2

Zhaojing Wang, Linlin Wang, Shicheng Zhang

引用次数: 0

Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides. 深度学习驱动的集成管道，用于从头设计和合成抗菌肽。

NPJ drug discovery Pub Date : 2026-05-04 DOI: 10.1038/s44386-026-00045-6

Jiahui Liu, Yun Chen, Jian Tang, Xupu Xing, Jin-Shun Lin, Juping Sun, Xin-Hui Xing, Juan Li, Can Yang Zhang

{"title":"Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides.","authors":"Jiahui Liu, Yun Chen, Jian Tang, Xupu Xing, Jin-Shun Lin, Juping Sun, Xin-Hui Xing, Juan Li, Can Yang Zhang","doi":"10.1038/s44386-026-00045-6","DOIUrl":"10.1038/s44386-026-00045-6","url":null,"abstract":"Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics against bacterial infections. However, the discovery of AMPs is impeded by the limitations of biochemical screening and the difficulty computational approaches face in balancing efficacy with structural diversity. We proposed an integrated \"generation-evaluation-validation\" framework to facilitate de novo discovery of AMPs. First, we constructed a soft prompt-tuned ProtGPT2 to efficiently generate candidates AMPs with both novel structures and promising therapeutic potential. Secondly, we adopted a multiple-choice learning ensemble model that enables high-confidence evaluation of candidates via a dynamic voting network. Finally, antimicrobial experiments were used to validate the activity of top-ranked de novo AMPs by monitoring bacterial surface changes. Out of nine candidates, four exhibited potent strain-specific activity, while two demonstrated broad-spectrum efficacy. All tested AMPs exhibited strong biofilm inhibition, potent membrane disruption, and minimal hemolysis, indicating significant therapeutic potential. With strong generalizability and versatility beyond AMPs, the proposed framework's modular design will facilitate adaptation to diverse peptide design tasks in the future. By integrating soft prompt tuning, multimodal ensemble learning, and experimental verification, this framework presents a practical and scalable strategy for rapid, resource-efficient de novo peptide discovery, particularly suited for applications where experimental throughput and cost are critical constraints.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364761","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

MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery. 哺乳动物-生物医学发现的分子对齐多模态结构和语言。

NPJ drug discovery Pub Date : 2026-05-04 DOI: 10.1038/s44386-026-00047-4

Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner, Alex Golts, Jeffrey K Weber, Ella Barkan, Simona Rabinovici-Cohen, Sagi Polaczek, Ido Amos, Ben Shapira, Liam Hazan, Matan Ninio, Sivan Ravid, Michael M Danziger, Yosi Shamay, Sharon Kurant, Joseph A Morrone, Parthasarathy Suryanarayanan, Michal Rosen-Zvi, Efrat Hexter

{"title":"MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.","authors":"Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato, Vadim Ratner, Alex Golts, Jeffrey K Weber, Ella Barkan, Simona Rabinovici-Cohen, Sagi Polaczek, Ido Amos, Ben Shapira, Liam Hazan, Matan Ninio, Sivan Ravid, Michael M Danziger, Yosi Shamay, Sharon Kurant, Joseph A Morrone, Parthasarathy Suryanarayanan, Michal Rosen-Zvi, Efrat Hexter","doi":"10.1038/s44386-026-00047-4","DOIUrl":"10.1038/s44386-026-00047-4","url":null,"abstract":"Modern AI (Artificial Intelligence) methods offer new opportunities in pharmacology by enabling improved modeling of disease mechanisms and drug action learned from large and heterogeneous biological datasets. A central challenge is developing models that can jointly integrate disparate biomedical modalities. We introduce MAMMAL (Molecular Aligned Multi Modal Architecture and Language), a foundation model for cross-modal learning, designed to address the challenges associated with drug discovery tasks. MAMMAL was pre-trained on 2 billion samples across protein and antibody sequences, small molecules, and gene expression profiles, and supports classification, regression, and generative tasks on cross-modal inputs. Across eleven benchmarks covering multiple stages of the drug discovery pipeline, MAMMAL achieves state-of-the-art performance on nine tasks and competitive results on two. In an antibody-antigen binding benchmark, fine-tuned MAMMAL prediction scores significantly outperform AlphaFold3 confidence scores, used here as a reference proxy for binding likelihood, in five of seven antigen targets. The MAMMAL framework and pretrained models are publicly available to support open and collaborative research.","PeriodicalId":520448,"journal":{"name":"NPJ drug discovery","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13267068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364789","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