Literature DB >> 34881780

Artificial intelligence unifies knowledge and actions in drug repositioning.

Zheng Yin1, Stephen T C Wong1.   

Abstract

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology.

Entities:  

Keywords:  artificial intelligence; computational biology; deep learning; drug repositioning; systems medicine

Mesh:

Year:  2021        PMID: 34881780      PMCID: PMC8923082          DOI: 10.1042/ETLS20210223

Source DB:  PubMed          Journal:  Emerg Top Life Sci        ISSN: 2397-8554


  96 in total

1.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.

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Journal:  Science       Date:  2001-05-04       Impact factor: 47.728

2.  Off-label use of oncology drugs: the need for more data and then some.

Authors:  David G Pfister
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3.  Effect size estimation: methods and examples.

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4.  Old drug new use--amoxapine and its metabolites as potent bacterial β-glucuronidase inhibitors for alleviating cancer drug toxicity.

Authors:  Ren Kong; Timothy Liu; Xiaoping Zhu; Syed Ahmad; Alfred L Williams; Alexandria T Phan; Hong Zhao; John E Scott; Li-An Yeh; Stephen T C Wong
Journal:  Clin Cancer Res       Date:  2014-04-29       Impact factor: 12.531

5.  Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

Authors:  Charlotte A Nelson; Atul J Butte; Sergio E Baranzini
Journal:  Nat Commun       Date:  2019-07-10       Impact factor: 14.919

6.  Investigating drug repositioning opportunities in FDA drug labels through topic modeling.

Authors:  Halil Bisgin; Zhichao Liu; Reagan Kelly; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2012-09-11       Impact factor: 3.169

7.  Amyloid-β42/40 ratio drives tau pathology in 3D human neural cell culture models of Alzheimer's disease.

Authors:  Sang Su Kwak; Kevin J Washicosky; Emma Brand; Djuna von Maydell; Jenna Aronson; Susan Kim; Diane E Capen; Murat Cetinbas; Ruslan Sadreyev; Shen Ning; Enjana Bylykbashi; Weiming Xia; Steven L Wagner; Se Hoon Choi; Rudolph E Tanzi; Doo Yeon Kim
Journal:  Nat Commun       Date:  2020-03-13       Impact factor: 14.919

8.  Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.

Authors:  Adrian J Green; Martin J Mohlenkamp; Jhuma Das; Meenal Chaudhari; Lisa Truong; Robyn L Tanguay; David M Reif
Journal:  PLoS Comput Biol       Date:  2021-07-02       Impact factor: 4.475

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