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Invasive fungal infections are increasing worldwide due to an expanding number of immunocompromised patients as well as an increase in drug-resistant fungi. While fungal resistance has increased, this resistance has not been accompanied by the development of new antifungals. A common class of antifungal agents that are prescribed are the azoles, which contain either a triazole or an imidazole group. Unfortunately, current azoles, like fluconazole, have been shown to be less effective with the increase in resistant fungal pathogens. Therefore, the development of novel azole antifungal compounds is of urgent need. The objective of this research was to synthesize triazole-containing small molecules with potent antifungal activity. The scaffold of the synthesized compounds contains a triazole moiety and was synthesized via a copper-catalyzed azide-alkyne click reaction (CuAAC) between the appropriate alkyne and azide intermediates. The minimum inhibitory concentrations of these compounds were determined using standard broth microdilution assays against opportunistic bacteria and fungi associated with life-threatening invasive fungal infections. Although the synthesized compounds possessed no antimicrobial activity, these results can be used to further the long-term goal of developing and optimizing lead compounds with potentin vitroantifungal activity.
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Quaternary ammonia compounds in disinfectant products: evaluating the potential for promoting antibiotic resistance and disrupting wastewater treatment plant performance
Quaternary ammonium compounds (QACs) are a class of compounds that were widely used as disinfectants during the COVID-19 pandemic and continue to be used as disinfecting agents.
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- PAR ID:
- 10499056
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Environmental Science: Advances
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2754-7000
- Page Range / eLocation ID:
- 208 to 226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract MotivationTandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds’ 3D conformations, and thus neglected critical structural information. ResultsWe present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. Availability and implementationThe codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3VA00063J
