• 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

通过自动配体导向进化 (AILDE) 方法进行化合物的命中至先导优化的方案。

Protocol for hit-to-lead optimization of compounds by auto ligand directing evolution (AILDE) approach.

机构信息

Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.

International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China.

出版信息

STAR Protoc. 2021 Feb 1;2(1):100312. doi: 10.1016/j.xpro.2021.100312. eCollection 2021 Mar 19.

DOI:10.1016/j.xpro.2021.100312
PMID:33554146
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7856476/
Abstract

Hit-to-lead (H2L) optimization is crucial for drug design, which has become an increasing concern in medicinal chemistry. A virtual screening strategy of auto ligand directing evolution (AILDE) has been developed to yield promising lead compounds rapidly and efficiently. The protocol includes instructions for fragment compound library construction, conformational sampling by molecular dynamics simulation, ligand modification by fragment growing, as well as the binding free energy prediction. For complete details on the use and execution of this protocol, please refer to Wu et al. (2020).

摘要

从苗头化合物到先导化合物的优化(H2L)在药物设计中至关重要,这已经成为药物化学领域日益关注的问题。我们开发了一种自动配体定向进化(AILDE)的虚拟筛选策略,以快速高效地获得有前途的先导化合物。该方案包括片段化合物库构建、分子动力学模拟构象采样、片段生长进行配体修饰以及结合自由能预测的说明。有关该方案使用和执行的完整详细信息,请参阅 Wu 等人(2020 年)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/01d8e07e4f54/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/459b3ed1f73c/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/7b51217a3702/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/1e69f00d9861/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/672b9f271646/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/083403e3e330/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/4337de063390/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/01d8e07e4f54/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/459b3ed1f73c/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/7b51217a3702/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/1e69f00d9861/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/672b9f271646/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/083403e3e330/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/4337de063390/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0c5/7856476/01d8e07e4f54/gr6.jpg

相似文献

1
Protocol for hit-to-lead optimization of compounds by auto ligand directing evolution (AILDE) approach.通过自动配体导向进化 (AILDE) 方法进行化合物的命中至先导优化的方案。
STAR Protoc. 2021 Feb 1;2(1):100312. doi: 10.1016/j.xpro.2021.100312. eCollection 2021 Mar 19.
2
Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead.计算机辅助配体定向进化以促进药物先导物的快速高效发现。
iScience. 2020 Jun 26;23(6):101179. doi: 10.1016/j.isci.2020.101179. Epub 2020 May 18.
3
In silico fragment-based drug discovery: setup and validation of a fragment-to-lead computational protocol using S4MPLE.基于片段的计算机药物发现:使用 S4MPLE 建立和验证片段至先导物的计算方案。
J Chem Inf Model. 2013 Apr 22;53(4):836-51. doi: 10.1021/ci4000163. Epub 2013 Apr 11.
4
Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.基于结构的药物发现中从苗头化合物到先导化合物以及先导化合物优化阶段所使用的计算方法。
Methods Mol Biol. 2018;1705:375-394. doi: 10.1007/978-1-4939-7465-8_19.
5
Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches.基于结构的方法指导的化学驱动的从头化合物优化。
Mol Inform. 2018 Sep;37(9-10):e1800059. doi: 10.1002/minf.201800059. Epub 2018 Jul 27.
6
Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.基于配体结合构象预测的蛋白质-化合物对接的后处理:片段药物发现的计算结构基药物筛选。
Curr Top Med Chem. 2010;10(6):680-94. doi: 10.2174/156802610791111452.
7
Fragment-Based Drug Design of Selective HDAC6 Inhibitors.选择性组蛋白去乙酰化酶6(HDAC6)抑制剂的基于片段的药物设计
Methods Mol Biol. 2021;2266:155-170. doi: 10.1007/978-1-0716-1209-5_9.
8
Combining ligand- and structure-based in silico methods for the identification of natural product-based inhibitors of Akt1.运用基于配体和结构的计算方法鉴定 Akt1 的天然产物抑制剂。
Mol Divers. 2020 Feb;24(1):45-60. doi: 10.1007/s11030-019-09924-9. Epub 2019 Feb 23.
9
Free resources to assist structure-based virtual ligand screening experiments.用于辅助基于结构的虚拟配体筛选实验的免费资源。
Curr Protein Pept Sci. 2007 Aug;8(4):381-411. doi: 10.2174/138920307781369391.
10
Structure-Based Virtual Screening.基于结构的虚拟筛选
Methods Mol Biol. 2017;1558:111-124. doi: 10.1007/978-1-4939-6783-4_5.

引用本文的文献

1
Investigating the effect of , on using integrated computational approaches.使用综合计算方法研究……对……的影响。 (注:原文中两个“……”处内容缺失,以上是根据现有内容翻译的大致意思。)
In Silico Pharmacol. 2024 Nov 9;12(2):102. doi: 10.1007/s40203-024-00278-1. eCollection 2024.
2
AILDE Computer-Aided Discovery of Novel Ibuprofen-Coumarin Antitumor Lead Compounds Targeting Cyclooxygenase-2.AILDE计算机辅助发现靶向环氧合酶-2的新型布洛芬-香豆素抗肿瘤先导化合物
ACS Omega. 2024 Sep 19;9(39):41021-41031. doi: 10.1021/acsomega.4c06596. eCollection 2024 Oct 1.
3
Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

本文引用的文献

1
Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead.计算机辅助配体定向进化以促进药物先导物的快速高效发现。
iScience. 2020 Jun 26;23(6):101179. doi: 10.1016/j.isci.2020.101179. Epub 2020 May 18.
2
AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation.AIMMS套件:一个专门用于预测蛋白质突变引起的耐药性的网络服务器。
Brief Bioinform. 2020 Jan 17;21(1):318-328. doi: 10.1093/bib/bby113.
3
PADFrag: A Database Built for the Exploration of Bioactive Fragment Space for Drug Discovery.
计算机辅助和人工智能方法设计新型 4-羟基苯丙酮酸双加氧酶抑制剂。
Int J Mol Sci. 2022 Jul 15;23(14):7822. doi: 10.3390/ijms23147822.
4
An drug repositioning workflow for host-based antivirals.基于宿主的抗病毒药物的药物重定位工作流程。
STAR Protoc. 2021 Jul 7;2(3):100653. doi: 10.1016/j.xpro.2021.100653. eCollection 2021 Sep 17.
PADFrag:一个用于探索具有药物发现潜力的生物活性片段空间的数据库。
J Chem Inf Model. 2018 Sep 24;58(9):1725-1730. doi: 10.1021/acs.jcim.8b00285. Epub 2018 Sep 6.
4
Integrated Strategy for Lead Optimization Based on Fragment Growing: The Diversity-Oriented-Target-Focused-Synthesis Approach.基于片段生长的先导优化综合策略:面向多样性的靶向聚焦合成方法。
J Med Chem. 2018 Jul 12;61(13):5719-5732. doi: 10.1021/acs.jmedchem.8b00653. Epub 2018 Jun 22.
5
Fragment Database FDB-17.片段数据库 FDB-17。
J Chem Inf Model. 2017 Apr 24;57(4):700-709. doi: 10.1021/acs.jcim.7b00020. Epub 2017 Apr 11.
6
Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics.用于大分子结构与动力学建模的采样方法原理与概述
PLoS Comput Biol. 2016 Apr 28;12(4):e1004619. doi: 10.1371/journal.pcbi.1004619. eCollection 2016 Apr.
7
Exploring activity cliffs in medicinal chemistry.探索药物化学中的活性断崖。
J Med Chem. 2012 Apr 12;55(7):2932-42. doi: 10.1021/jm201706b. Epub 2012 Jan 27.
8
Molecular docking: a powerful approach for structure-based drug discovery.分子对接:基于结构的药物发现的强大方法。
Curr Comput Aided Drug Des. 2011 Jun;7(2):146-57. doi: 10.2174/157340911795677602.
9
Predicting free energy changes using structural ensembles.使用结构系综预测自由能变化。
Nat Methods. 2009 Jan;6(1):3-4. doi: 10.1038/nmeth0109-3.
10
Modeling the catalysis of anti-cocaine catalytic antibody: competing reaction pathways and free energy barriers.抗可卡因催化抗体催化作用的建模:竞争反应途径和自由能垒
J Am Chem Soc. 2008 Apr 16;130(15):5140-9. doi: 10.1021/ja077972s. Epub 2008 Mar 15.