• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

超大库对接发现新化学型。

Ultra-large library docking for discovering new chemotypes.

机构信息

Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.

State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, China.

出版信息

Nature. 2019 Feb;566(7743):224-229. doi: 10.1038/s41586-019-0917-9. Epub 2019 Feb 6.

DOI:10.1038/s41586-019-0917-9
PMID:30728502
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6383769/
Abstract

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D dopamine receptor.

摘要

尽管人们对拓展化学空间有着浓厚的兴趣,但包含数亿至数十亿种不同分子的库仍然难以获取。在这里,我们研究了基于结构的对接,使用 130 个特征明确的反应生成了 1.7 亿个按需合成的化合物。由此产生的库具有多样性,代表了超过 1070 万个其他无法获得的支架。我们模拟了库中每个化合物与 AmpC β-内酰胺酶(AmpC)和 D 多巴胺受体的对接。从排名最高的分子中,合成了 44 个和 549 个化合物,分别用于测试与 AmpC 和 D 多巴胺受体的相互作用。我们发现了一种 AmpC 的酚盐抑制剂,它揭示了一组以前未知的抑制剂。对这种分子进行了优化,得到了 77 nM 的抑制效果,使其成为已知最有效的非共价 AmpC 抑制剂之一。该抑制剂和其他 AmpC 抑制剂的晶体结构证实了对接预测。对于 D 多巴胺受体,命中率几乎随着对接得分单调下降,而命中率与得分曲线预测该库包含 453,000 个 D 多巴胺受体配体。在 81 个新发现的化学型中,有 30 个显示出亚微摩尔的活性,包括 D 多巴胺受体的 180 pM 亚型选择性激动剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/441d7da4a9de/nihms-1518048-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e3cc0fcc6707/nihms-1518048-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e250bc8b1c51/nihms-1518048-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/1498b395e5b0/nihms-1518048-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/aa2ff39c683f/nihms-1518048-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/6c7a84121209/nihms-1518048-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/b4c464739980/nihms-1518048-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e4ba7a4b2fd6/nihms-1518048-f0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/918518b4bb7b/nihms-1518048-f0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/8286d91e9073/nihms-1518048-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/0b6e6b631546/nihms-1518048-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/fbb30dca1e66/nihms-1518048-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/441d7da4a9de/nihms-1518048-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e3cc0fcc6707/nihms-1518048-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e250bc8b1c51/nihms-1518048-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/1498b395e5b0/nihms-1518048-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/aa2ff39c683f/nihms-1518048-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/6c7a84121209/nihms-1518048-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/b4c464739980/nihms-1518048-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/e4ba7a4b2fd6/nihms-1518048-f0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/918518b4bb7b/nihms-1518048-f0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/8286d91e9073/nihms-1518048-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/0b6e6b631546/nihms-1518048-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/fbb30dca1e66/nihms-1518048-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c164/6383769/441d7da4a9de/nihms-1518048-f0004.jpg

相似文献

1
Ultra-large library docking for discovering new chemotypes.超大库对接发现新化学型。
Nature. 2019 Feb;566(7743):224-229. doi: 10.1038/s41586-019-0917-9. Epub 2019 Feb 6.
2
Identifying Artifacts from Large Library Docking.从大型库对接中识别伪影。
J Med Chem. 2024 Sep 26;67(18):16796-16806. doi: 10.1021/acs.jmedchem.4c01632. Epub 2024 Sep 10.
3
Covalent docking of large libraries for the discovery of chemical probes.利用共价对接技术对大型文库进行筛选,以发现化学探针。
Nat Chem Biol. 2014 Dec;10(12):1066-72. doi: 10.1038/nchembio.1666. Epub 2014 Oct 26.
4
Validation of the AmpC β-lactamase binding site and identification of inhibitors with novel scaffolds.验证 AmpC β-内酰胺酶结合位点和鉴定具有新型骨架的抑制剂。
J Chem Inf Model. 2012 May 25;52(5):1367-75. doi: 10.1021/ci300068m. Epub 2012 May 4.
5
Docking for fragment inhibitors of AmpC beta-lactamase.AmpC β-内酰胺酶片段抑制剂的对接
Proc Natl Acad Sci U S A. 2009 May 5;106(18):7455-60. doi: 10.1073/pnas.0813029106. Epub 2009 Apr 22.
6
Covalent docking of selected boron-based serine beta-lactamase inhibitors.所选硼基丝氨酸β-内酰胺酶抑制剂的共价对接
J Comput Aided Mol Des. 2015 May;29(5):441-50. doi: 10.1007/s10822-015-9834-7. Epub 2015 Feb 13.
7
Increasing chemical space coverage by combining empirical and computational fragment screens.通过组合经验和计算片段筛选来增加化学空间覆盖度。
ACS Chem Biol. 2014 Jul 18;9(7):1528-35. doi: 10.1021/cb5001636. Epub 2014 May 20.
8
First virtual screening and experimental validation of inhibitors targeting GES-5 carbapenemase.针对 GES-5 碳青霉烯酶的抑制剂的首次虚拟筛选和实验验证。
J Comput Aided Mol Des. 2019 Feb;33(2):295-305. doi: 10.1007/s10822-018-0182-2. Epub 2019 Jan 2.
9
D dopamine receptor high-resolution structures enable the discovery of selective agonists.D 多巴胺受体的高分辨率结构有助于发现选择性激动剂。
Science. 2017 Oct 20;358(6361):381-386. doi: 10.1126/science.aan5468.
10
Benzyl Phenylsemicarbazides: A Chemistry-Driven Approach Leading to G Protein-Biased Dopamine D Receptor Agonists with High Subtype Selectivity.苯甲基苯并脒:一种基于化学的方法,可产生高亚型选择性的 G 蛋白偏置多巴胺 D 受体激动剂。
J Med Chem. 2019 Nov 14;62(21):9658-9679. doi: 10.1021/acs.jmedchem.9b01085. Epub 2019 Oct 30.

引用本文的文献

1
Alphappimi: a comprehensive deep learning framework for predicting PPI-modulator interactions.Alphappimi:用于预测蛋白质-蛋白质相互作用调节剂相互作用的综合深度学习框架。
J Cheminform. 2025 Aug 29;17(1):134. doi: 10.1186/s13321-025-01077-2.
2
Gout management: Patent analytics and computational drug design explores URAT1 inhibitors landscape.痛风管理:专利分析与计算药物设计探索尿酸转运蛋白1抑制剂格局。
PLoS One. 2025 Aug 13;20(8):e0328559. doi: 10.1371/journal.pone.0328559. eCollection 2025.
3
Accurate prediction of drug-protein interactions by maintaining the original topological relationships among embeddings.

本文引用的文献

1
Stan: A Probabilistic Programming Language.斯坦:一种概率编程语言。
J Stat Softw. 2017;76. doi: 10.18637/jss.v076.i01. Epub 2017 Jan 11.
2
Structural determinants of 5-HT receptor activation and biased agonism.5-HT 受体激活和偏向激动的结构决定因素。
Nat Struct Mol Biol. 2018 Sep;25(9):787-796. doi: 10.1038/s41594-018-0116-7. Epub 2018 Aug 20.
3
Impaired β-arrestin recruitment and reduced desensitization by non-catechol agonists of the D1 dopamine receptor.D1多巴胺受体的非儿茶酚胺激动剂导致β-抑制蛋白募集受损和脱敏作用减弱。
通过保持嵌入之间的原始拓扑关系来准确预测药物-蛋白质相互作用。
BMC Biol. 2025 Aug 5;23(1):243. doi: 10.1186/s12915-025-02338-0.
4
STELLA provides a drug design framework enabling extensive fragment-level chemical space exploration and balanced multi-parameter optimization.STELLA提供了一个药物设计框架,能够进行广泛的片段级化学空间探索和平衡的多参数优化。
Sci Rep. 2025 Aug 1;15(1):28135. doi: 10.1038/s41598-025-12685-1.
5
A bottom-up approach to find lead compounds in expansive chemical spaces.一种在广阔化学空间中寻找先导化合物的自下而上方法。
Commun Chem. 2025 Aug 1;8(1):225. doi: 10.1038/s42004-025-01610-2.
6
Sequence-based virtual screening using transformers.基于序列的使用变压器的虚拟筛选。
Nat Commun. 2025 Jul 28;16(1):6925. doi: 10.1038/s41467-025-61833-8.
7
Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.用于蛋白质-配体相互作用预测的多尺度拓扑结构到序列变压器
Nat Mach Intell. 2024 Jul;6(7):799-810. doi: 10.1038/s42256-024-00855-1. Epub 2024 Jun 21.
8
Protein-ligand data at scale to support machine learning.大规模蛋白质-配体数据以支持机器学习。
Nat Rev Chem. 2025 Jul 23. doi: 10.1038/s41570-025-00737-z.
9
AI meets physics in computational structure-based drug discovery for GPCRs.在基于计算结构的G蛋白偶联受体药物发现中,人工智能与物理学相遇。
NPJ Drug Discov. 2025;2(1):16. doi: 10.1038/s44386-025-00019-0. Epub 2025 Jul 3.
10
MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.MIC:一种用于在冷冻电镜和晶体结构中确定离子和水分子位置的深度学习工具。
Nat Commun. 2025 Jul 4;16(1):6182. doi: 10.1038/s41467-025-61315-x.
Nat Commun. 2018 Feb 14;9(1):674. doi: 10.1038/s41467-017-02776-7.
4
D dopamine receptor high-resolution structures enable the discovery of selective agonists.D 多巴胺受体的高分辨率结构有助于发现选择性激动剂。
Science. 2017 Oct 20;358(6361):381-386. doi: 10.1126/science.aan5468.
5
Return of D Dopamine Receptor Antagonists in Drug Discovery.多巴胺D受体拮抗剂在药物研发中的回归。
J Med Chem. 2017 Sep 14;60(17):7233-7243. doi: 10.1021/acs.jmedchem.7b00151. Epub 2017 May 17.
6
Allosteric "beta-blocker" isolated from a DNA-encoded small molecule library.从 DNA 编码的小分子文库中分离出的变构“β阻断剂”。
Proc Natl Acad Sci U S A. 2017 Feb 14;114(7):1708-1713. doi: 10.1073/pnas.1620645114. Epub 2017 Jan 27.
7
DNA-encoded chemistry: enabling the deeper sampling of chemical space.DNA 编码化学:实现更深入的化学空间采样。
Nat Rev Drug Discov. 2017 Feb;16(2):131-147. doi: 10.1038/nrd.2016.213. Epub 2016 Dec 9.
8
The ChEMBL database in 2017.2017年的ChEMBL数据库。
Nucleic Acids Res. 2017 Jan 4;45(D1):D945-D954. doi: 10.1093/nar/gkw1074. Epub 2016 Nov 28.
9
Expanding Synthesizable Space of Disubstituted 1,2,4-Oxadiazoles.扩展二取代1,2,4-恶二唑的可合成空间。
ACS Comb Sci. 2016 Oct 10;18(10):616-624. doi: 10.1021/acscombsci.6b00103. Epub 2016 Sep 7.
10
Structure-based discovery of opioid analgesics with reduced side effects.基于结构的副作用减少的阿片类镇痛药的发现。
Nature. 2016 Sep 8;537(7619):185-190. doi: 10.1038/nature19112. Epub 2016 Aug 17.