• 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

BRADSHAW:一个自动化分子设计系统。

BRADSHAW: a system for automated molecular design.

机构信息

Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK.

Department of Molecular Design, Data and Computational Sciences, GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA, 19426, USA.

出版信息

J Comput Aided Mol Des. 2020 Jul;34(7):747-765. doi: 10.1007/s10822-019-00234-8. Epub 2019 Oct 21.

DOI:10.1007/s10822-019-00234-8
PMID:31637565
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7292824/
Abstract

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

摘要

本文介绍了 BRADSHAW(使用异构自动化工作流的生物反应分析和设计系统),这是一个用于自动化分子设计的系统,集成了化学结构生成、实验设计、主动学习和化学信息学工具的方法。该系统的简单用户界面旨在方便大规模自动化设计,同时最小化引入新算法所需的软件开发,这在一个快速发展的领域是一个关键要求。该系统体现了自动化、最佳实践、实验设计以及传统化学信息学和现代机器学习算法的使用理念。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/911699674793/10822_2019_234_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/532e1859215c/10822_2019_234_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/77c5c8839178/10822_2019_234_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/eeff65398b47/10822_2019_234_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/48db063045c6/10822_2019_234_Sch1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/1ca6f0b41e87/10822_2019_234_Sch2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/911699674793/10822_2019_234_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/532e1859215c/10822_2019_234_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/77c5c8839178/10822_2019_234_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/eeff65398b47/10822_2019_234_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/48db063045c6/10822_2019_234_Sch1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/1ca6f0b41e87/10822_2019_234_Sch2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e534/7292824/911699674793/10822_2019_234_Fig4_HTML.jpg

相似文献

1
BRADSHAW: a system for automated molecular design.BRADSHAW:一个自动化分子设计系统。
J Comput Aided Mol Des. 2020 Jul;34(7):747-765. doi: 10.1007/s10822-019-00234-8. Epub 2019 Oct 21.
2
Diversifying chemical libraries with generative topographic mapping.利用生成地形映射对化学文库进行多样化处理。
J Comput Aided Mol Des. 2020 Jul;34(7):805-815. doi: 10.1007/s10822-019-00215-x. Epub 2019 Aug 12.
3
Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.用于分析和设计优化的小分子集合和库的 cheminformatics 工具。
Cell Chem Biol. 2019 May 16;26(5):765-777.e3. doi: 10.1016/j.chembiol.2019.02.018. Epub 2019 Apr 4.
4
Deep Learning in Chemistry.深度学习在化学中的应用。
J Chem Inf Model. 2019 Jun 24;59(6):2545-2559. doi: 10.1021/acs.jcim.9b00266. Epub 2019 Jun 13.
5
In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.利用机器学习和深度学习方法对化学物质诱导的血液毒性进行计算机模拟预测。
Mol Divers. 2021 Aug;25(3):1585-1596. doi: 10.1007/s11030-021-10255-x. Epub 2021 Jul 1.
6
Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.用于数据管理和机器学习的自动化工作流程以开发定量结构-活性关系。
Methods Mol Biol. 2025;2834:115-130. doi: 10.1007/978-1-0716-4003-6_5.
7
Virtual Compound Libraries in Computer-Assisted Drug Discovery.计算机辅助药物发现中的虚拟化合物库。
J Chem Inf Model. 2019 Feb 25;59(2):644-651. doi: 10.1021/acs.jcim.8b00737. Epub 2019 Jan 24.
8
Artificial Intelligence and Cheminformatics-Guided Modern Privileged Scaffold Research.人工智能与 cheminformatics 指导的现代特权支架研究。
Curr Top Med Chem. 2021;21(28):2593-2608. doi: 10.2174/1568026621666210512020434.
9
Molecular Scaffold Hopping via Holistic Molecular Representation.通过整体分子表征进行分子骨架跳跃
Methods Mol Biol. 2021;2266:11-35. doi: 10.1007/978-1-0716-1209-5_2.
10
ChemSuite: A package for chemoinformatics calculations and machine learning.ChemSuite:一个用于化学信息学计算和机器学习的软件包。
Chem Biol Drug Des. 2019 May;93(5):960-964. doi: 10.1111/cbdd.13479. Epub 2019 Mar 7.

引用本文的文献

1
Defining Levels of Automated Chemical Design.定义自动化化学设计的层次。
J Med Chem. 2022 May 26;65(10):7073-7087. doi: 10.1021/acs.jmedchem.2c00334. Epub 2022 May 5.
2
Systemic evolutionary chemical space exploration for drug discovery.用于药物发现的系统进化化学空间探索。
J Cheminform. 2022 Apr 1;14(1):19. doi: 10.1186/s13321-022-00598-4.
3
Artificial Intelligence for Autonomous Molecular Design: A Perspective.人工智能自主分子设计:一个视角。

本文引用的文献

1
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models.分子集(MOSES):分子生成模型的基准测试平台。
Front Pharmacol. 2020 Dec 18;11:565644. doi: 10.3389/fphar.2020.565644. eCollection 2020.
2
Improvement in ADMET Prediction with Multitask Deep Featurization.多任务深度特征化提高 ADMET 预测
J Med Chem. 2020 Aug 27;63(16):8835-8848. doi: 10.1021/acs.jmedchem.9b02187. Epub 2020 May 12.
3
Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.
Molecules. 2021 Nov 9;26(22):6761. doi: 10.3390/molecules26226761.
4
Has Artificial Intelligence Impacted Drug Discovery?人工智能是否影响了药物发现?
Methods Mol Biol. 2022;2390:153-176. doi: 10.1007/978-1-0716-1787-8_6.
5
Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.机器学习在药理学和 ADMET 终点建模中的应用。
Methods Mol Biol. 2022;2390:61-101. doi: 10.1007/978-1-0716-1787-8_2.
6
GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.GenUI:用于从头分子生成和化学信息学的交互式可扩展开源软件平台。
J Cheminform. 2021 Sep 25;13(1):73. doi: 10.1186/s13321-021-00550-y.
7
Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.机器学习加速的基于量子力学的原子模拟在工业中的应用。
J Comput Aided Mol Des. 2021 Apr;35(4):557-586. doi: 10.1007/s10822-020-00346-6. Epub 2020 Oct 9.
基于反应的枚举、主动学习和自由能计算,快速探索合成上可处理的化学空间并优化细胞周期蛋白依赖性激酶 2 抑制剂的效力。
J Chem Inf Model. 2019 Sep 23;59(9):3782-3793. doi: 10.1021/acs.jcim.9b00367. Epub 2019 Aug 22.
4
GuacaMol: Benchmarking Models for de Novo Molecular Design.GuacaMol:从头设计分子的模型基准测试。
J Chem Inf Model. 2019 Mar 25;59(3):1096-1108. doi: 10.1021/acs.jcim.8b00839. Epub 2019 Mar 19.
5
Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.用于 ADME-Tox 性质的预测性多任务深度神经网络模型:从大数据集学习。
J Chem Inf Model. 2019 Mar 25;59(3):1253-1268. doi: 10.1021/acs.jcim.8b00785. Epub 2019 Jan 24.
6
De Novo Molecule Design by Translating from Reduced Graphs to SMILES.从头设计分子:从简化图到 SMILES 的转换。
J Chem Inf Model. 2019 Mar 25;59(3):1136-1146. doi: 10.1021/acs.jcim.8b00626. Epub 2018 Dec 21.
7
Targeting adenosine A receptor antagonism for treatment of cancer.针对腺苷 A 受体拮抗作用治疗癌症。
Expert Opin Drug Discov. 2018 Nov;13(11):997-1003. doi: 10.1080/17460441.2018.1534825. Epub 2018 Oct 18.
8
The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction.利用匹配分子系列网络进行跨靶点构效关系转换和活性预测。
Medchemcomm. 2017 Oct 11;8(11):2067-2078. doi: 10.1039/c7md00465f. eCollection 2017 Nov 1.
9
Molecular generative model based on conditional variational autoencoder for de novo molecular design.基于条件变分自编码器的分子生成模型用于从头分子设计。
J Cheminform. 2018 Jul 11;10(1):31. doi: 10.1186/s13321-018-0286-7.
10
The OECD QSAR Toolbox Starts Its Second Decade.经济合作与发展组织(OECD)定量构效关系(QSAR)工具箱迎来了它的第二个十年。
Methods Mol Biol. 2018;1800:55-77. doi: 10.1007/978-1-4939-7899-1_2.