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Recent Publications Citing ChemDB

  • Chengalroyen MD et al. (2020) Biological Profiling Enables Rapid Mechanistic Classification of Phenotypic Screening Hits and Identification of KatG Activation-Dependent Pyridine Carboxamide Prodrugs with Activity Against Mycobacterium tuberculosis. Front Cell Infect Microbiol. 10: 582416.
  • Druzhilovskiy DS et al. (2020) Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries. J Supercomp Front Innov. 7 (3): 57-76.
  • Goodswen SJ et al. (2021) Machine learning and applications in microbiology. FEMS Microbiol Rev.
  • Jackson SS et al. (2020) A 35-Year Review of Pre-Clinical HIV Therapeutics Research Reported by NIH ChemDB: Influences of Target Discoveries, Drug Approvals and Research Funding. J AIDS Clin Res. 11 (11).
  • Lane TR et al. (2021) Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery. Molecular pharmaceutics. 18 (1): 403-415.
  • Winkler DA. (2021) Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases. Front Chem. 9: 614073.
  • Schmalstig AA et al. (2022) Mycobacterium abscessus drug discovery using machine learning. Tuberculosis. 132: 102168.


ChemDB Database Updates

The ChemDB Database was updated in January 2023 with the following:

  • 966 new compounds
  • 45 new literature references
  • 983 new lines of anti-HIV cellular data
  • 220 new lines of anti-HIV enzyme inhibition data
  • 1,445 new lines of anti-opportunistic infection data

ChemDB Release Notes

Version: v2.0

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If using ChemDB information for publication or abstract presentation, please cite the website in addition to the original information source.    Database last updated: January 2023