Treffer: Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.
Title:
Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.
Authors:
Kell DB; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K.; Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark., Samanta S; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K., Swainston N; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K.
Source:
The Biochemical journal [Biochem J] 2020 Dec 11; Vol. 477 (23), pp. 4559-4580.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't; Review
Language:
English
Journal Info:
Publisher: Published by Portland Press on behalf of the Biochemical Society Country of Publication: England NLM ID: 2984726R Publication Model: Print Cited Medium: Internet ISSN: 1470-8728 (Electronic) Linking ISSN: 02646021 NLM ISO Abbreviation: Biochem J Subsets: MEDLINE
Imprint Name(s):
Original Publication: London, UK : Published by Portland Press on behalf of the Biochemical Society
MeSH Terms:
References:
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J Chem Inf Model. 2012 Nov 26;52(11):2864-75. (PMID: 23088335)
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Future Med Chem. 2018 Nov;10(22):2641-2658. (PMID: 30499744)
Mini Rev Med Chem. 2020;20(14):1375-1388. (PMID: 32348219)
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Med Res Rev. 1996 Jan;16(1):3-50. (PMID: 8788213)
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J Chem Inf Model. 2019 Jul 22;59(7):3166-3176. (PMID: 31273995)
Nat Biotechnol. 2020 Feb;38(2):143-145. (PMID: 32001834)
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Mar Drugs. 2020 Nov 23;18(11):. (PMID: 33238416)
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Mol Biosyst. 2009 Sep;5(9):1051-7. (PMID: 19668871)
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J Chem Inf Model. 2016 Feb 22;56(2):286-99. (PMID: 26818135)
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J Med Chem. 2020 Aug 27;63(16):8705-8722. (PMID: 32366098)
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ACS Med Chem Lett. 2020 Jul 14;11(8):1496-1505. (PMID: 32832015)
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J Cheminform. 2020 Sep 4;12(1):53. (PMID: 33431037)
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Science. 2020 Oct 2;370(6512):101-108. (PMID: 33004517)
Nature. 2016 Jan 28;529(7587):484-9. (PMID: 26819042)
J Cheminform. 2019 Mar 12;11(1):20. (PMID: 30868314)
Neural Comput. 2010 Dec;22(12):3207-20. (PMID: 20858131)
Chem Pharm Bull (Tokyo). 2020;68(3):227-233. (PMID: 32115529)
Chem Soc Rev. 2015 Mar 7;44(5):1172-239. (PMID: 25503938)
Neural Comput. 2019 Jul;31(7):1235-1270. (PMID: 31113301)
Neural Comput. 1997 Nov 15;9(8):1735-80. (PMID: 9377276)
Front Pharmacol. 2018 Nov 13;9:1275. (PMID: 30524275)
Drug Discov Today. 2019 Oct;24(10):2017-2032. (PMID: 31377227)
Chem Sci. 2019 Mar 1;10(17):4640-4651. (PMID: 31123574)
J Chem Inf Model. 2019 Aug 26;59(8):3370-3388. (PMID: 31361484)
Chem Sci. 2020 May 19;11(24):6134-6148. (PMID: 32832059)
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Mol Syst Biol. 2020 Sep;16(9):e9198. (PMID: 32975352)
Proteins. 2019 Dec;87(12):1141-1148. (PMID: 31602685)
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Mol Inform. 2018 Jan;37(1-2):. (PMID: 29442446)
ACS Synth Biol. 2020 Nov 20;9(11):2927-2935. (PMID: 33064458)
Nature. 2018 Mar 28;555(7698):604-610. (PMID: 29595767)
Bioinformatics. 2019 Sep 15;35(18):3329-3338. (PMID: 30768156)
Molecules. 2020 Jul 29;25(15):. (PMID: 32751155)
Angew Chem Int Ed Engl. 2020 Jan 7;59(2):725-730. (PMID: 31750610)
Cell Syst. 2020 Jul 22;11(1):49-62.e16. (PMID: 32711843)
Nature. 2020 Jan;577(7792):706-710. (PMID: 31942072)
Nature. 2020 Jan;577(7788):89-94. (PMID: 31894144)
Chem Sci. 2018 Nov 19;10(6):1692-1701. (PMID: 30842833)
Angew Chem Int Ed Engl. 2020 Dec 14;59(51):22858-22893. (PMID: 31553511)
Polymers (Basel). 2020 Jan 08;12(1):. (PMID: 31936321)
J Cheminform. 2019 Nov 21;11(1):71. (PMID: 33430971)
J Chem Inf Model. 2020 Jan 27;60(1):77-91. (PMID: 31809029)
Mol Inform. 2018 Jan;37(1-2):. (PMID: 29095571)
Sci Rep. 2021 Jan 11;11(1):321. (PMID: 33432013)
Angew Chem Int Ed Engl. 2016 May 10;55(20):5904-37. (PMID: 27062365)
Front Neurosci. 2019 Mar 26;13:231. (PMID: 30971873)
Cell. 2020 Feb 20;180(4):688-702.e13. (PMID: 32084340)
PLoS One. 2020 Apr 16;15(4):e0229862. (PMID: 32298284)
Lancet. 2016 Jan 9;387(10014):176-87. (PMID: 26603922)
Nat Chem Biol. 2020 Apr;16(4):469-478. (PMID: 32152546)
J Med Chem. 2006 Jul 27;49(15):4568-73. (PMID: 16854061)
Curr Pharm Des. 2007;13(34):3494-504. (PMID: 18220786)
Chimia (Aarau). 2019 Dec 18;73(12):1018-1023. (PMID: 31883554)
Expert Opin Drug Discov. 2020 Jul;15(7):755-764. (PMID: 32228116)
ACS Omega. 2020 Aug 10;5(33):20746-20754. (PMID: 32875208)
J Comput Aided Mol Des. 2013 Aug;27(8):675-9. (PMID: 23963658)
J Med Chem. 2009 May 14;52(9):2952-63. (PMID: 19348472)
J Cheminform. 2020 Apr 10;12(1):22. (PMID: 33430998)
Angew Chem Int Ed Engl. 2020 Dec 21;59(52):23414-23436. (PMID: 31553509)
Nat Rev Drug Discov. 2020 May;19(5):353-364. (PMID: 31801986)
Chem Sci. 2019 Aug 16;10(40):9219-9232. (PMID: 32055308)
Microbiology (Reading). 1998 May;144 ( Pt 5):1157-1170. (PMID: 9611790)
Front Pharmacol. 2014 Oct 31;5:231. (PMID: 25400580)
Cytometry A. 2019 Apr;95(4):366-380. (PMID: 30565841)
Nat Commun. 2020 Jul 15;11(1):3551. (PMID: 32669542)
J Chem Inf Model. 2015 Nov 23;55(11):2324-37. (PMID: 26479676)
J Cheminform. 2009 Jun 10;1(1):8. (PMID: 20298526)
Nature. 2020 Jul;583(7815):237-241. (PMID: 32641813)
Molecules. 2020 Jul 16;25(14):. (PMID: 32708785)
Comput Struct Biotechnol J. 2020 Mar 30;18:784-790. (PMID: 32280433)
Nat Rev Microbiol. 2015 Jun;13(6):343-59. (PMID: 25978706)
Nat Chem. 2012 Jan 24;4(2):90-8. (PMID: 22270643)
Nat Rev Drug Discov. 2008 Mar;7(3):205-20. (PMID: 18309312)
Nat Biotechnol. 2017 Jan;35(1):48-55. (PMID: 27941803)
Metabolomics. 2015;11(2):323-339. (PMID: 25750602)
J Cheminform. 2020 Feb 18;12(1):14. (PMID: 33430996)
Insights Imaging. 2018 Aug;9(4):611-629. (PMID: 29934920)
IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1798-828. (PMID: 23787338)
Front Pharmacol. 2018 Sep 07;9:1011. (PMID: 30245630)
Front Pharmacol. 2019 Nov 05;10:1303. (PMID: 31749705)
Drug Discov Today. 2018 Jun;23(6):1241-1250. (PMID: 29366762)
ACS Cent Sci. 2018 Feb 28;4(2):268-276. (PMID: 29532027)
ACS Synth Biol. 2020 Aug 21;9(8):2154-2161. (PMID: 32649182)
Science. 2019 Aug 9;365(6453):. (PMID: 31395756)
Proc Natl Acad Sci U S A. 2019 Aug 6;116(32):15849-15854. (PMID: 31341078)
Neural Netw. 2015 Jan;61:85-117. (PMID: 25462637)
IEEE Trans Pattern Anal Mach Intell. 2020 Aug;42(8):1856-1867. (PMID: 30908191)
Angew Chem Int Ed Engl. 2019 Oct 7;58(41):14420-14426. (PMID: 31433107)
Nat Rev Neurosci. 2020 Jun;21(6):335-346. (PMID: 32303713)
Science. 2018 Dec 7;362(6419):1140-1144. (PMID: 30523106)
ACS Cent Sci. 2018 Jul 25;4(7):793-804. (PMID: 30062108)
Anal Chem. 2020 Jan 21;92(2):1720-1729. (PMID: 31661259)
FEBS J. 2013 Dec;280(23):5957-80. (PMID: 23552054)
Contributed Indexing:
Keywords: artificial intelligence; cheminformatics; deep learning
Entry Date(s):
Date Created: 20201208 Date Completed: 20210310 Latest Revision: 20240330
Update Code:
20250114
PubMed Central ID:
PMC7733676
DOI:
10.1042/BCJ20200781
PMID:
33290527
Database:
MEDLINE
Weitere Informationen
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
(© 2020 The Author(s).)