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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1983 1
1984 1
1985 2
1986 1
1988 2
1989 4
1990 6
1991 8
1992 3
1993 6
1994 8
1995 8
1996 14
1997 8
1998 17
1999 20
2000 25
2001 29
2002 38
2003 47
2004 54
2005 57
2006 73
2007 98
2008 108
2009 126
2010 109
2011 146
2012 128
2013 185
2014 193
2015 298
2016 297
2017 369
2018 642
2019 966
2020 1217
2021 522
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5,294 results
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Page 1
Artificial intelligence and machine learning to fight COVID-19.
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Alimadadi A, et al. Physiol Genomics. 2020 Apr 1;52(4):200-202. doi: 10.1152/physiolgenomics.00029.2020. Epub 2020 Mar 27. Physiol Genomics. 2020. PMID: 32216577 Free PMC article. No abstract available.
Artificial intelligence in drug development: present status and future prospects.
Mak KK, Pichika MR. Mak KK, et al. Drug Discov Today. 2019 Mar;24(3):773-780. doi: 10.1016/j.drudis.2018.11.014. Epub 2018 Nov 22. Drug Discov Today. 2019. PMID: 30472429 Review.
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. ...In this review, we discuss the major causes of attrition rates in new drug approvals, the possible way
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific
Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.
Koromina M, Pandi MT, Patrinos GP. Koromina M, et al. OMICS. 2019 Nov;23(11):539-548. doi: 10.1089/omi.2019.0151. Epub 2019 Oct 25. OMICS. 2019. PMID: 31651216
In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. ...As with system sci …
In this expert review, we describe the most frequently used machine learning algorithms in drug development pipe …
Big Data and Artificial Intelligence Modeling for Drug Discovery.
Zhu H. Zhu H. Annu Rev Pharmacol Toxicol. 2020 Jan 6;60:573-589. doi: 10.1146/annurev-pharmtox-010919-023324. Epub 2019 Sep 13. Annu Rev Pharmacol Toxicol. 2020. PMID: 31518513 Free PMC article. Review.
Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial inte
Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling base …
Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.
Jing Y, Bian Y, Hu Z, Wang L, Xie XQ. Jing Y, et al. AAPS J. 2018 Mar 30;20(3):58. doi: 10.1208/s12248-018-0210-0. AAPS J. 2018. PMID: 29603063 Free PMC article. Review.
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional …
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.
Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Rifaioglu AS, et al. Brief Bioinform. 2019 Sep 27;20(5):1878-1912. doi: 10.1093/bib/bby061. Brief Bioinform. 2019. PMID: 30084866 Free PMC article. Review.
The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natu …
The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including …
A Deep Learning Approach to Antibiotic Discovery.
Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ. Stokes JM, et al. Cell. 2020 Feb 20;180(4):688-702.e13. doi: 10.1016/j.cell.2020.01.021. Cell. 2020. PMID: 32084340 Free article.
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. ...Additionally, from a discret …
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, …
Machine Learning in Drug Discovery and Development Part 1: A Primer.
Talevi A, Morales JF, Hather G, Podichetty JT, Kim S, Bloomingdale PC, Kim S, Burton J, Brown JD, Winterstein AG, Schmidt S, White JK, Conrado DJ. Talevi A, et al. CPT Pharmacometrics Syst Pharmacol. 2020 Mar;9(3):129-142. doi: 10.1002/psp4.12491. Epub 2020 Mar 11. CPT Pharmacometrics Syst Pharmacol. 2020. PMID: 31905263 Free PMC article.
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. ...A companion article will summarize applications of ML in drug discover
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.
Yang X, Wang Y, Byrne R, Schneider G, Yang S. Yang X, et al. Chem Rev. 2019 Sep 25;119(18):10520-10594. doi: 10.1021/acs.chemrev.8b00728. Epub 2019 Jul 11. Chem Rev. 2019. PMID: 31294972 Review.
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, so
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the
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