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

Abstract

Background: Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and its derivatives, they have a special place in the chemistry of nitrogen-containing heterocyclic molecules. Objective: The objective of this study is to analyze the interaction of several heterocyclic molecules using molecular docking and machine learning approaches to find out the possible antibacterial drugs. Methods: The molecular docking analysis of heterocyclic-based analogues against the sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 modification (PDB ID: 6ZYB) is discussed. Results: Many heterocyclic-based derivatives show several residual interaction, affinity, and hydrogen bonding with sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 alteration which are identified by the investigation of in silico molecular docking analysis of such heterocyclic derivatives. Conclusion: 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.