将人工智能引入网络药理学学科建设

被引:4
作者
韩露
机构
[1] 北京药理毒理研究所
关键词
D O I
暂无
中图分类号
R96 [药理学]; TP18 [人工智能理论];
学科分类号
100706 [药理学]; 140502 [人工智能];
摘要
<正>网络药理学的提出是药理学的一大进步,它标志着药理学的研究从传统的一药一靶,一钥匙一锁的研究思想走向了系统化的研究模式[1]。在过去十年的网络药理学研究中,网络及网络科学中的多种度量指标成为了药理研究中的描述和研究工具,为药物新用途发现及药物治疗理论的完善提供了全新视角。然而,随着药理学研究数据的爆炸式增长
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页码:852 / 854
页数:3
相关论文
共 6 条
[1]
Machine learning for molecular and materials science [J].
Butler, Keith T. ;
Davies, Daniel W. ;
Cartwright, Hugh ;
Isayev, Olexandr ;
Walsh, Aron .
NATURE, 2018, 559 (7715) :547-555
[2]
Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data [J].
Koleti, Amar ;
Terryn, Raymond ;
Stathias, Vasileios ;
Chung, Caty ;
Cooper, Daniel J. ;
Turner, John P. ;
Vidovic, Dusica ;
Forlin, Michele ;
Kelley, Tanya T. ;
D'Urso, Alessandro ;
Allen, Bryce K. ;
Torre, Denis ;
Jagodnik, Kathleen M. ;
Wang, Lily ;
Jenkins, Sherry L. ;
Mader, Christopher ;
Niu, Wen ;
Fazel, Mehdi ;
Mahi, Naim ;
Pilarczyk, Marcin ;
Clark, Nicholas ;
Shamsaei, Behrouz ;
Meller, Jarek ;
Vasiliauskas, Juozas ;
Reichard, John ;
Medvedovic, Mario ;
Ma'ayan, Avi ;
Pillai, Ajay ;
Schurer, Stephan C. .
NUCLEIC ACIDS RESEARCH, 2018, 46 (D1) :D558-D566
[3]
AI-powered drug discovery captures pharma interest [J].
Smalley, Eric .
NATURE BIOTECHNOLOGY, 2017, 35 (07) :604-605
[4]
Representing high throughput expression profiles via perturbation barcodes reveals compound targets [J].
Filzen, Tracey M. ;
Kutchukian, Peter S. ;
Hermes, Jeffrey D. ;
Li, Jing ;
Tudor, Matthew .
PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (02)
[5]
IBM debuts hyped 'cognitive cloud' biotech HQ in Cambridge [J].
Laursen, Lucas .
NATURE BIOTECHNOLOGY, 2015, 33 (12) :1219-1220
[6]
Network pharmacology: the next paradigm in drug discovery [J].
Hopkins, Andrew L. .
NATURE CHEMICAL BIOLOGY, 2008, 4 (11) :682-690