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- W3030677265 abstract "Drug discovery faces a crisis. The industry has used up the “obvious” space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof of concept of an approach to rationally identify a “chemical vocabulary” related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability." @default.
- W3030677265 created "2020-06-05" @default.
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- W3030677265 date "2020-05-26" @default.
- W3030677265 modified "2023-10-11" @default.
- W3030677265 title "Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria" @default.
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- W3030677265 doi "https://doi.org/10.1021/acs.jcim.0c00352" @default.
- W3030677265 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7768862" @default.
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