Mining biology for antibiotic discovery

被引：1

作者：

de la Fuente-nunez, Cesar ^{[1
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机构：

[1] Univ Penn, Inst Biomed Informat, Inst Translat Med & Therapeut, Perelman Sch Med,Machine Biol Grp,Dept Psychiat, Philadelphia, PA 19104 USA

[2] Univ Penn, Inst Biomed Informat, Inst Translat Med & Therapeut, Perelman Sch Med,Machine Biol Grp,Dept Microbiol, Philadelphia, PA 19104 USA

[3] Univ Penn, Sch Engn & Appl Sci, Dept Bioengn, Philadelphia, PA 19104 USA

[4] Univ Penn, Sch Engn & Appl Sci, Dept Chem, Philadelphia, PA 19104 USA

[5] Univ Penn, Sch Engn & Appl Sci, Dept Biomol Engn, Philadelphia, PA 19104 USA

[6] Univ Penn, Sch Arts & Sci, Dept Chem, Philadelphia, PA 19104 USA

[7] Univ Penn, Penn Inst Computat Sci, Philadelphia, PA 19104 USA

来源：

PLOS BIOLOGY | 2024年 / 22卷 / 11期

关键词：

D O I：

10.1371/journal.pbio.3002946

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.

引用

页数：4

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