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Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from Escherichia coli: In Silico Molecular Docking Study and Machine Learning Classifiers.

Shivangi SharmaRahul ChoubeyManish GuptaShivendra Singh
Published in: Medicinal chemistry (Shariqah (United Arab Emirates)) (2024)
The dataset from the molecular docking study was used for additional optimum analysis, and the molecular descriptors were classified using a variety of machine learning classifiers, including the GB Classifier, CB Classifier, RF Classifier, SV Classifier, KNN Classifier, and Voting Classifier. The research presented here showed that heterocyclic derivatives may operate as potent antibacterial agents when combined with other compounds to produce highly efficient antibacterial agents.
Keyphrases
  • molecular docking
  • machine learning
  • highly efficient
  • molecular dynamics simulations
  • escherichia coli
  • artificial intelligence
  • transcription factor
  • pseudomonas aeruginosa
  • single molecule