• 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

药物发现与开发概述。

An overview of drug discovery and development.

机构信息

Department of biomedical Science, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan.

出版信息

Future Med Chem. 2020 May;12(10):939-947. doi: 10.4155/fmc-2019-0307. Epub 2020 Apr 9.

DOI:10.4155/fmc-2019-0307
PMID:32270704
Abstract

A new medicine will take an average of 10-15 years and more than US$2 billion before it can reach the pharmacy shelf. Traditionally, drug discovery relied on natural products as the main source of new drug entities, but was later shifted toward high-throughput synthesis and combinatorial chemistry-based development. New technologies such as ultra-high-throughput drug screening and artificial intelligence are being heavily employed to reduce the cost and the time of early drug discovery, but they remain relatively unchanged. However, are there other potentially faster and cheaper means of drug discovery? Is drug repurposing a viable alternative? In this review, we discuss the different means of drug discovery including their advantages and disadvantages.

摘要

一种新药的研发平均需要 10-15 年,并且需要超过 20 亿美元才能上市。传统上,药物发现依赖于天然产物作为新药实体的主要来源,但后来转向基于高通量合成和组合化学的开发。超高效药物筛选和人工智能等新技术正被大量用于降低早期药物发现的成本和时间,但它们相对不变。然而,是否有其他潜在的更快、更便宜的药物发现方法?药物再利用是一种可行的替代方案吗?在这篇综述中,我们讨论了不同的药物发现方法,包括它们的优缺点。

相似文献

1
An overview of drug discovery and development.药物发现与开发概述。
Future Med Chem. 2020 May;12(10):939-947. doi: 10.4155/fmc-2019-0307. Epub 2020 Apr 9.
2
AI-Driven Enhancements in Drug Screening and Optimization.人工智能驱动的药物筛选和优化增强。
Methods Mol Biol. 2024;2714:269-294. doi: 10.1007/978-1-0716-3441-7_15.
3
Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.人工智能技术在新冠病毒从头设计药物中的应用。
Int J Mol Sci. 2022 Mar 17;23(6):3261. doi: 10.3390/ijms23063261.
4
The strategies and techniques of drug discovery from natural products.天然产物药物发现的策略与技术。
Pharmacol Ther. 2020 Dec;216:107686. doi: 10.1016/j.pharmthera.2020.107686. Epub 2020 Sep 19.
5
Advancing Drug Discovery via Artificial Intelligence.人工智能推动药物发现。
Trends Pharmacol Sci. 2019 Aug;40(8):592-604. doi: 10.1016/j.tips.2019.06.004. Epub 2019 Jul 15.
6
The polypharmacology of natural products.天然产物的多药性。
Future Med Chem. 2018 Jun 1;10(11):1361-1368. doi: 10.4155/fmc-2017-0294. Epub 2018 Apr 20.
7
Data considerations for predictive modeling applied to the discovery of bioactive natural products.应用于生物活性天然产物发现的预测建模的数据考虑因素。
Drug Discov Today. 2022 Aug;27(8):2235-2243. doi: 10.1016/j.drudis.2022.05.009. Epub 2022 May 14.
8
Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.基于机器智能工具的数据中心分子分析和肝细胞癌治疗评估。
J Gastrointest Cancer. 2021 Dec;52(4):1266-1276. doi: 10.1007/s12029-021-00768-x. Epub 2021 Dec 15.
9
New Perspectives on Machine Learning in Drug Discovery.机器学习在药物发现中的新视角。
Curr Med Chem. 2021;28(32):6704-6728. doi: 10.2174/0929867327666201111144048.
10
High-throughput screening approaches for investigating drug metabolism and pharmacokinetics.用于研究药物代谢和药代动力学的高通量筛选方法。
Xenobiotica. 2001 Aug-Sep;31(8-9):557-89. doi: 10.1080/00498250110060978.

引用本文的文献

1
AGRL-DSE: Adaptive Graph Representation Learning on a Heterogeneous Graph for Drug Side Effect Prediction.AGRL-DSE:基于异构图的自适应图表示学习用于药物副作用预测
ACS Omega. 2025 Aug 18;10(34):38753-38765. doi: 10.1021/acsomega.5c04006. eCollection 2025 Sep 2.
2
Exploring protein inhibitors of through pharmacoinformatic approaches incorporating solubility-enhancing formulation insights.通过结合提高溶解度制剂见解的药物信息学方法探索[具体物质]的蛋白质抑制剂。 (注:原文中“Exploring...of...”中间缺少具体所探索的对象,这里补充了“[具体物质]”使句子完整)
Front Pharmacol. 2025 Aug 14;16:1630038. doi: 10.3389/fphar.2025.1630038. eCollection 2025.
3
Drug repurposing in traditional Chinese medicine: from empirical wisdom to modern therapeutic strategies.
中药中的药物重新利用:从经验智慧到现代治疗策略。
Front Pharmacol. 2025 Jul 31;16:1631727. doi: 10.3389/fphar.2025.1631727. eCollection 2025.
4
Prioritizing pathway signature using deep learning approach: a novel strategy for traditional Chinese medicine formula generation and optimization.使用深度学习方法对通路特征进行优先级排序:一种用于中药方剂生成和优化的新策略。
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf403.
5
From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes.从实验室到临床:人工智能如何重塑药物研发时间表和行业成果。
Pharmaceuticals (Basel). 2025 Jun 30;18(7):981. doi: 10.3390/ph18070981.
6
Untargeted Diversity-Oriented Synthesis for the Discovery of New Antitumor Agents: An Integrated Approach of Inverse Virtual Screening, Bioinformatics, and Omics for Target Deconvolution.用于发现新型抗肿瘤药物的非靶向多样性导向合成:一种用于靶点反卷积的逆虚拟筛选、生物信息学和组学的综合方法。
J Med Chem. 2025 Aug 14;68(15):16483-16517. doi: 10.1021/acs.jmedchem.5c01344. Epub 2025 Jul 24.
7
3D-EDiffMG: 3D equivariant diffusion-driven molecular generation to accelerate drug discovery.3D-EDiffMG:用于加速药物发现的3D等变扩散驱动分子生成
J Pharm Anal. 2025 Jun;15(6):101257. doi: 10.1016/j.jpha.2025.101257. Epub 2025 Mar 5.
8
Oncogenic Activity and Sorafenib Sensitivity of p.S214C Mutation in Lung Cancer.肺癌中p.S214C突变的致癌活性及索拉非尼敏感性
Cancers (Basel). 2025 Jul 4;17(13):2246. doi: 10.3390/cancers17132246.
9
Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.人工智能驱动的肿瘤学药物发现创新:变革传统流程并优化药物设计
Drug Des Devel Ther. 2025 Jul 3;19:5685-5707. doi: 10.2147/DDDT.S509769. eCollection 2025.
10
Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.利用机器学习模型评估药物发现与开发中的ADMET性质。
ADMET DMPK. 2025 Jun 7;13(3):2772. doi: 10.5599/admet.2772. eCollection 2025.