Platforms for antibiotic discovery

By Kim Lewis. Nature reviews. Drug discovery, 2013

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Challenges in Infective Endocarditis. (2017, background): “There are multiple underlying mechanisms, including the very high bacterial density and poor antibiotic penetration within vegetations, low bacterial metabolic activity, and production of protective biofilms on prosthetic material (103).”
Predictive compound accumulation rules yield a broad-spectrum antibiotic (2017, neutral): “5: (1) ATCC 29213; (2) NRS3; (3) MG1655, (4) ELZ4017; (5) ELZ4045; (6) ELZ4346; (7) ELZ4081; (8) ELZ4072; (9) ELZ4239; (10) BAA-2469; (11) ATCC 19606; (12) KB304; (13) KB349; (14) WO22; (15) BD335; (16) IF101; (17) ATCC 27736; (18) 4-38; (19) 6-73; (20) ATCC 29893; (21) 1-60; (22) 3-46; (23) PAO1.”
Discovery of a structural class of antibiotics with explainable deep learning (2023, background): “Antibiotic candidates have been discovered in the past decade through various approaches based on natural product mining 2,3 , high-throughput screening 4 , evolution and phylogeny analyses 5,6 , structure-guided and rational design 7,8 , and in silico screens using machine learning 1,12–14 .”
A new antibiotic selectively kills Gram-negative pathogens (2019, neutral): “It is difficult to find compounds acting against Gram-negative bacteria 1,2 .”