A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

药物重新定位 重新调整用途 计算机科学 药物发现 药物开发 人工智能 药品 机器学习 数据科学 生物信息学 药理学 生物 生态学
作者
Faheem Ahmed,Afaque Manzoor Soomro,Abdul Rahim Chethikkattuveli Salih,Anupama Samantasinghar,Arun Asif,In Suk Kang,Kyung Hyun Choi
出处
期刊:Biomedicine & Pharmacotherapy [Elsevier]
卷期号:153: 113350-113350 被引量:1
标识
DOI:10.1016/j.biopha.2022.113350
摘要

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models. Process of drug repurposing in Covid-19. The data related to drugs, diseases, proteins, and genes etc. are extracted from different data sources (databases) followed use of different repurposing methods. Repurposing methods are used to find out the potential drugs which are then validated in in-vitro (2D or 3D) cell culture and in-vivo animal models. Finally, the shortlisted drugs are forwarded to clinical trials and successful drugs are repurposed • Drug repurposing is an effective and preferable alternative to de-novo drug discovery. • A systematic review of different strategies to drug repurposing for Covid-19 is given. • Limitations of repurposing approaches with recommendations are presented for Covid-19. • Network-based drug repurposing for Covid-19 is successful, easy to explain and interpret.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
独特背包完成签到,获得积分10
刚刚
刚刚
米十二发布了新的文献求助10
刚刚
1秒前
云云邶完成签到,获得积分10
1秒前
dsjacn完成签到 ,获得积分10
1秒前
2秒前
唠叨的曼易完成签到,获得积分10
2秒前
长情契完成签到,获得积分10
3秒前
3秒前
4秒前
yyh发布了新的文献求助10
5秒前
yx发布了新的文献求助10
6秒前
huxy完成签到,获得积分10
6秒前
刘一刀发布了新的文献求助10
7秒前
lingyao发布了新的文献求助10
8秒前
桐桐应助符fu采纳,获得10
9秒前
852应助嗳7采纳,获得10
9秒前
10秒前
是容许鸭完成签到 ,获得积分10
10秒前
半芹发布了新的文献求助20
10秒前
帅气之槐发布了新的文献求助30
11秒前
科研通AI2S应助huxy采纳,获得10
11秒前
雪芜发布了新的文献求助10
11秒前
11秒前
ljs关注了科研通微信公众号
11秒前
qingzhou发布了新的文献求助10
12秒前
苍穹完成签到,获得积分10
13秒前
刘一刀完成签到,获得积分10
14秒前
tang完成签到,获得积分10
14秒前
菜鸡发布了新的文献求助10
15秒前
16秒前
longchb发布了新的文献求助10
16秒前
Lucky完成签到,获得积分10
17秒前
17秒前
18秒前
18秒前
mengdewen完成签到,获得积分10
18秒前
19秒前
高分求助中
Sustainability in Tides Chemistry 2000
Bayesian Models of Cognition:Reverse Engineering the Mind 800
Essentials of thematic analysis 700
A Dissection Guide & Atlas to the Rabbit 600
Very-high-order BVD Schemes Using β-variable THINC Method 568
Внешняя политика КНР: о сущности внешнеполитического курса современного китайского руководства 500
Revolution und Konterrevolution in China [by A. Losowsky] 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3124336
求助须知:如何正确求助?哪些是违规求助? 2774637
关于积分的说明 7723368
捐赠科研通 2430117
什么是DOI,文献DOI怎么找? 1290937
科研通“疑难数据库(出版商)”最低求助积分说明 621972
版权声明 600297