• 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

描述计算药物开发中的新兴公司。

Characterizing emerging companies in computational drug development.

机构信息

Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Bellevue Asset Management, Zurich, Switzerland.

出版信息

Nat Comput Sci. 2024 Feb;4(2):96-103. doi: 10.1038/s43588-024-00594-8. Epub 2024 Feb 26.

DOI:10.1038/s43588-024-00594-8
PMID:38413778
Abstract

Computation promises to accelerate, de-risk and optimize drug research and development. An increasing number of companies have entered this space, specializing in the design of new algorithms, computing on proprietary data, and/or development of hardware to improve distinct drug pipeline stages. The large number of such companies and their unique strategies and deals have created a highly complex and competitive industry. We comprehensively analyze the companies in this space to highlight trends and opportunities, identifying highly occupied areas of risk and currently underrepresented niches of high value.

摘要

计算有望加速、降低风险并优化药物研发。越来越多的公司进入了这个领域,专注于设计新算法、利用专有数据进行计算,以及/或者开发硬件来改善不同的药物研发阶段。如此众多的公司及其独特的战略和交易,使得这个行业变得非常复杂和具有竞争力。我们全面分析了这个领域的公司,以突出趋势和机会,确定风险高度集中的领域和目前价值被低估的利基市场。

相似文献

1
Characterizing emerging companies in computational drug development.描述计算药物开发中的新兴公司。
Nat Comput Sci. 2024 Feb;4(2):96-103. doi: 10.1038/s43588-024-00594-8. Epub 2024 Feb 26.
2
[Early achievements of the Danish pharmaceutical industry--8. Lundbeck].[丹麦制药行业的早期成就——8. 灵北公司]
Theriaca. 2016(43):9-61.
3
[Development of antituberculous drugs: current status and future prospects].[抗结核药物的研发:现状与未来前景]
Kekkaku. 2006 Dec;81(12):753-74.
4
State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions.用于药物设计目的的计算工具的最新进展和传播:对意大利学术界和工业机构的调查。
Future Med Chem. 2013 May;5(8):907-27. doi: 10.4155/fmc.13.59.
5
[An analysis of the pharmaceuticals market in Vietnam].[越南药品市场分析]
Sante. 2001 Jul-Sep;11(3):155-60.
6
Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey.2007年至2017年大型生物制药组织中的药品不良反应案例安全实践:一项行业调查
Pharmaceut Med. 2019 Dec;33(6):499-510. doi: 10.1007/s40290-019-00307-x.
7
Equipment and analytical companies meeting continuous challenges. May 20-21, 2014 Continuous Manufacturing Symposium.设备与分析公司面临持续挑战。2014年5月20 - 21日 连续制造研讨会
J Pharm Sci. 2015 Mar;104(3):821-31. doi: 10.1002/jps.24282. Epub 2014 Dec 1.
8
Quantitative Analysis for Chinese and US-listed Pharmaceutical Companies by the LightGBM Algorithm.基于LightGBM算法对中国和美国上市制药公司的定量分析。
Curr Comput Aided Drug Des. 2023;19(6):405-415. doi: 10.2174/1573409919666230126095901.
9
Quantum computing's potential for drug discovery: Early stage industry dynamics.量子计算在药物研发中的潜力:早期行业动态。
Drug Discov Today. 2021 Jul;26(7):1680-1688. doi: 10.1016/j.drudis.2021.06.003. Epub 2021 Jun 11.
10
Systematic risk identification and assessment using a new risk map in pharmaceutical R&D.在制药研发中使用新的风险地图进行系统风险识别与评估。
Drug Discov Today. 2021 Dec;26(12):2786-2793. doi: 10.1016/j.drudis.2021.06.015. Epub 2021 Jul 3.

引用本文的文献

1
Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.从药物-靶点相互作用的角度阐明人工智能在药物开发中的作用。
J Pharm Anal. 2025 Mar;15(3):101144. doi: 10.1016/j.jpha.2024.101144. Epub 2024 Nov 14.
2
Finding the most potent compounds using active learning on molecular pairs.利用分子对的主动学习寻找最有效的化合物。
Beilstein J Org Chem. 2024 Aug 27;20:2152-2162. doi: 10.3762/bjoc.20.185. eCollection 2024.
3
Machine learning trims the peptide drug design process to a sweet spot.

本文引用的文献

1
Drug discovery companies are customizing ChatGPT: here's how.药物研发公司正在定制ChatGPT:方法如下。
Nat Biotechnol. 2023 May;41(5):585-586. doi: 10.1038/s41587-023-01788-7.
2
FDA no longer has to require animal testing for new drugs.美国食品药品监督管理局不再需要对新药进行动物试验。
Science. 2023 Jan 13;379(6628):127-128. doi: 10.1126/science.adg6276. Epub 2023 Jan 12.
3
In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.计算机毒理学:从构效关系到深度学习及不良结局途径。
机器学习将肽类药物设计过程优化至最佳状态。
Nat Chem. 2024 Sep;16(9):1394-1395. doi: 10.1038/s41557-024-01610-0.
Wiley Interdiscip Rev Comput Mol Sci. 2020 Jul-Aug;10(4):e1475. doi: 10.1002/wcms.1475. Epub 2020 Mar 31.
4
Enabling high-throughput biology with flexible open-source automation.借助灵活的开源自动化实现高通量生物学。
Mol Syst Biol. 2021 Mar;17(3):e9942. doi: 10.15252/msb.20209942.
5
Practical considerations for active machine learning in drug discovery.药物发现中主动机器学习的实用考虑。
Drug Discov Today Technol. 2019 Dec;32-33:73-79. doi: 10.1016/j.ddtec.2020.06.001. Epub 2020 Jul 19.
6
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet.人工智能在药物研发中的应用:哪些是现实的,哪些是虚幻的?第 1 部分:产生影响的途径,以及我们为何尚未实现。
Drug Discov Today. 2021 Feb;26(2):511-524. doi: 10.1016/j.drudis.2020.12.009. Epub 2020 Dec 17.
7
Organ-on-a-Chip: A New Paradigm for Drug Development.器官芯片:药物开发的新模式。
Trends Pharmacol Sci. 2021 Feb;42(2):119-133. doi: 10.1016/j.tips.2020.11.009. Epub 2020 Dec 16.
8
Artificial intelligence in chemistry and drug design.化学与药物设计中的人工智能
J Comput Aided Mol Des. 2020 Jul;34(7):709-715. doi: 10.1007/s10822-020-00317-x.
9
Active machine learning helps drug hunters tackle biology.主动式机器学习助力药物研发人员应对生物学难题。
Nat Biotechnol. 2020 May;38(5):512-514. doi: 10.1038/s41587-020-0521-4.
10
Big Data and Artificial Intelligence Modeling for Drug Discovery.大数据和人工智能在药物发现中的建模。
Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:573-589. doi: 10.1146/annurev-pharmtox-010919-023324. Epub 2019 Sep 13.