Ultra-large library docking for ligand discovery
用于配体发现的超大文库对接
基本信息
- 批准号:9797487
- 负责人:
- 金额:$ 42.01万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-27 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAffinityAgonistBinding SitesBiologicalBiologyChargeChemicalsCommunitiesComputersCrystallographyDNADatabasesDockingDopamineDopamine ReceptorEnsureG-Protein-Coupled ReceptorsGrowthHydrophobicityIndividualInfrastructureJointsLeadLettersLibrariesLigandsMeasuresMethodsMolecularMorphologic artifactsOpioidOutcomePlanetsReactionResortTestingVirtual LibraryWorkbasechemical synthesiscombinatorial chemistrycomputing resourcesconformercostinnovationinterestkappa opioid receptorsnovelreceptorscaffoldsmall moleculesuccessthree dimensional structuretool
项目摘要
PROJECT SUMMARY / ABSTRACT
Despite much interest in expanding chemical space, diverse, billion molecule libraries remain inaccessible. In
principle, docking a virtual library could access some of this missing chemical space. This idea has until now
been vitiated by two key problems: 1. prediction of readily synthesized molecules has been challenging, without
resorting to strategies that collapse diversity; and 2. docking is notoriously inaccurate. Two recent advances
have made virtual library docking screens seem less fanciful. First, our collaborators at Enamine, a widely used
fine chemicals supplier, have defined a 0.7 billion molecule make-on-demand library based on >100 reactions
that they have under good control; >650,000 of these have been successfully synthesized. Second, while
docking retains serious errors, it has made pragmatic progress, and has found genuinely novel ligands for >100
targets. The specific aims are:
Aim 1. A robust, searchable, and dockable database of 3 billion diverse lead-like molecules. We will A.
Enumerate 3 billion vetted products from two- and three-component reactions. B. measure the diversity and
novelty of this library and how they differ from the world's in-stock molecules. C. Develop a community accessible
database and chemoinformatics infrastructure that can store, similarity search, and rapidly retrieve molecules
from this library. D. convert these molecules into biologically relevant 3D forms, including enumerating low-
energy conformers, partial atomic charges and other parameters, van der Waals parameters and solvation
energies for all library molecules, enabling their use for docking screens.
Aim 2. Dock and experimentally test the library against two targets. A. Screen the library against the dopamine
D4 and kappa-opioid receptors, seeking novel ligands. 250 to 500 library molecules will be tested per screen,
itself a 10-fold increase. A key question will be do we find novel, potent ligands, or are we overwhelmed by false
positives? B. As the library grows, do we continue to find ever more novel, in some sense ever more perfect,
high affinity ligands, or does discovery saturate? C. How does hit rate vary with docking score? As we will be
testing hundreds of molecules, we can afford to investigate not only those with the highest docking ranks, but
also molecules with mediocre and poor ranks. This has not been previously explored, certainly not at scale.
If successful, this project will increase the number of molecules available to the community by 1000-fold, and
demonstrate their utility for ligand discovery. Extensive preliminary results support its feasibility.
项目总结/摘要
尽管人们对扩大化学空间很感兴趣,但多样化的数十亿分子库仍然无法访问。在
原则上,对接虚拟库可以访问一些缺失的化学空间。这个想法直到现在
被两个关键问题破坏了:1。预测容易合成的分子一直具有挑战性,
诉诸瓦解多样性的策略; 2.对接是出了名的不准确。最近的两项进展
已经让虚拟图书馆的对接屏幕看起来不那么新奇了。首先,我们在Enamine的合作者,一个广泛使用的
精细化学品供应商,已经定义了一个基于>100个反应的7亿分子按需制造库
他们已经很好地控制了;其中超过650,000个已经成功合成。第二,虽然
对接保留了严重的错误,它已经取得了务实的进展,并发现了真正的新配体>100
目标的具体目标是:
目标1。一个强大的,可搜索的,可停靠的数据库,包含30亿种不同的铅样分子。我们将A。
列举30亿个来自两组分和三组分反应的审查产品。B。衡量多样性,
这个库的新奇以及它们与世界上库存分子的不同之处。C.开发一个可访问的社区
数据库和化学信息学基础设施,可以存储、相似性搜索和快速检索分子
从这个图书馆。D.将这些分子转化为生物学相关的3D形式,包括枚举低-
能量构象、部分原子电荷和其他参数、货车德瓦尔斯参数和溶剂化
为所有库分子提供能量,使其能够用于对接屏幕。
目标2.对接并针对两个靶标实验性地测试文库。A.对库进行多巴胺筛选
D4和κ-阿片受体,寻找新的配体。每次筛选将测试250至500个文库分子,
本身就增加了10倍。一个关键的问题是,我们是找到了新的、有效的配体,还是被错误的配体所淹没?
积极的?B。随着图书馆的发展,我们是否会继续发现更多的新奇,在某种意义上更完美,
高亲和力配体,还是发现饱和?C.命中率如何随对接分数变化?因为我们将
通过测试数百个分子,我们不仅可以研究那些对接级别最高的分子,
也包括那些等级一般的和等级差的分子。这是以前没有探索过的,当然也不是大规模的。
如果成功,该项目将使社区可用的分子数量增加1000倍,
证明了它们对于配体发现的效用。大量的初步结果支持其可行性。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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John J. Irwin其他文献
Docking for molecules that bind in a symmetric stack to Alzheimer’s disease tau fibrils with SymDOCK
- DOI:
10.1016/j.bpj.2023.11.1855 - 发表时间:
2024-02-08 - 期刊:
- 影响因子:
- 作者:
Matthew S. Smith;Peter Kunach;Ian S. Knight;Rian Kormos;Joseph G. Pepe;Isabella Glenn;John J. Irwin;William F. DeGrado;Marc I. Diamond;Sarah H. Shahmoradian;Brian K. Shoichet - 通讯作者:
Brian K. Shoichet
The impact of library size and scale of testing on virtual screening
图书馆规模和测试规模对虚拟筛选的影响
- DOI:
10.1038/s41589-024-01797-w - 发表时间:
2025-01-03 - 期刊:
- 影响因子:13.700
- 作者:
Fangyu Liu;Olivier Mailhot;Isabella S. Glenn;Seth F. Vigneron;Violla Bassim;Xinyu Xu;Karla Fonseca-Valencia;Matthew S. Smith;Dmytro S. Radchenko;James S. Fraser;Yurii S. Moroz;John J. Irwin;Brian K. Shoichet - 通讯作者:
Brian K. Shoichet
Modeling the expansion of virtual screening libraries
对虚拟筛选库的扩展进行建模
- DOI:
10.1038/s41589-022-01234-w - 发表时间:
2023-01-16 - 期刊:
- 影响因子:13.700
- 作者:
Jiankun Lyu;John J. Irwin;Brian K. Shoichet - 通讯作者:
Brian K. Shoichet
Virtual library docking for cannabinoid-1 receptor agonists with reduced side effects
具有减少副作用的大麻素 1 受体激动剂的虚拟库对接
- DOI:
10.1038/s41467-025-57136-7 - 发表时间:
2025-03-06 - 期刊:
- 影响因子:15.700
- 作者:
Tia A. Tummino;Christos Iliopoulos-Tsoutsouvas;Joao M. Braz;Evan S. O’Brien;Reed M. Stein;Veronica Craik;Ngan K. Tran;Suthakar Ganapathy;Fangyu Liu;Yuki Shiimura;Fei Tong;Thanh C. Ho;Dmytro S. Radchenko;Yurii S. Moroz;Sian Rodriguez Rosado;Karnika Bhardwaj;Jorge Benitez;Yongfeng Liu;Herthana Kandasamy;Claire Normand;Meriem Semache;Laurent Sabbagh;Isabella Glenn;John J. Irwin;Kaavya Krishna Kumar;Alexandros Makriyannis;Allan I. Basbaum;Brian K. Shoichet - 通讯作者:
Brian K. Shoichet
John J. Irwin的其他文献
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{{ truncateString('John J. Irwin', 18)}}的其他基金
Ultra-large library docking for ligand discovery
用于配体发现的超大文库对接
- 批准号:
10240701 - 财政年份:2019
- 资助金额:
$ 42.01万 - 项目类别:
Ultra-large library docking for ligand discovery
用于配体发现的超大文库对接
- 批准号:
10473611 - 财政年份:2019
- 资助金额:
$ 42.01万 - 项目类别:
Ultra-large library docking for ligand discovery
用于配体发现的超大文库对接
- 批准号:
10023266 - 财政年份:2019
- 资助金额:
$ 42.01万 - 项目类别:
A Web-Based Automatic Virtual Screening System
基于网络的自动虚拟筛选系统
- 批准号:
10297015 - 财政年份:2004
- 资助金额:
$ 42.01万 - 项目类别:
A Web-Based Automatic Virtual Screening System
基于网络的自动虚拟筛选系统
- 批准号:
10612058 - 财政年份:2004
- 资助金额:
$ 42.01万 - 项目类别:
A Web-Based Automatic Virtual Screening System
基于网络的自动虚拟筛选系统
- 批准号:
10434959 - 财政年份:2004
- 资助金额:
$ 42.01万 - 项目类别:
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