Abstract
Background
The misfunction of the protein 16SrRNA methyltransferase usually results in Urinary tract (UTI), and Gastrointestinal (GI) infections, sepsis, pneumonia, and also cause wound infections. It confers resistance to aminoglycoside medications, which complicates the treatment of infections caused by these bacteria.
Objectives
Herein, we aim to investigate the role of artificial intelligence (AI) in medical sciences to provide the solutions as a significant need in medical therapy for infections.
Methodology: Using an AI drug design tool, three effective de novo medicinal compounds that target the 16SrRNA methyltransferase protein were discovered. The computational tools used includes: Expasy for protein annotation, Protparam to calculate physiochemical parameters, SWISS-MODEL to estimate the 3D structure, and Uniprot to generate the 16SrRNA methyltransferase protein sequence. An adequate foundation for the development and validation of AI-designed phytochemical medicines for infections is provided by quality assessment, binding site prediction, drug design with WADDAICA, toxicity screening, ADMET evaluation, and docking analysis with CB-dock.
Results
Comprehensive pharmacokinetic and toxicology analyses provided the non-toxic character of AI-designed doxycycline by demonstrating its exceptional absorption in the blood–brain barrier. The AI-designed doxycycline docks with the 16SrRNA methyltransferase protein with a noteworthy affinity of about − 7.6 kcal/mol, indicating potential therapeutic value.
Conclusion
Even though the in silico studies show efficacy and safety, still there is need of in vivo trials to investigate the hidden medical aspects. By addressing existing constraints, this work considerably expands the knowledge about newer methods and also helps to understand deep insights of dug design mechanism for treatment.