AI-driven virtual screening for combatting antibiotic resistance: a promising therapeutic approach
Year: 2026
Type: article
Abstract: Introduction. The emergence of multi-resistant and pan-resistant enterobacteria to betalactam antibiotics poses a significant threat to human health. The prevailing resistance mechanism in enterobacteria involves the production of beta-lactamases, enzymes that break down the beta-lactam cycle. Thus, compounds inhibiting these enzymes seem promising candidates for potential drugs. Materials and methods. The study employed three main methods: 1) Virtual screening using molecular docking with Autodock Vina to identify potential inhibitors of beta-lactamase enzymes; 2) Molecular dynamics simulations using Desmond software to assess the stability of the compound-target interactions; 3) ADMET prediction using Admet AI to evaluate the pharmacokinetic properties and drug-likeness of the selected compound. Results. The results highlight compound's effectiveness, with a binding affinity of -11.458 kcal/mol, surpassing that of the commercial beta-lactam inhibitor clavulanic acid (-6.8 kcal/mol). Molecular dynamics results, including RMSD and RMSF plots, support the stability of the compound under simulated conditions. ADMET predictions show that the compound meets all drug-likeness rules and has good pharmacokinetic properties, underscoring its potential as a promising therapeutic candidate. Conclusion. This study highlights the potential of virtual screening and molecular dynamics as powerful tools in the search for novel inhibitors of beta-lactamase enzyme. Further in vitro and in vivo studies are warranted to confirm these findings. Key messages. The compound demonstrates higher binding affinity to beta-lactamase enzymes than the commercial inhibitor clavulanic acid. Molecular dynamics simulations confirm the stability of the compound under physiological conditions. ADMET predictions indicate that the compound has favorable drug-likeness and pharmacokinetic properties, supporting its therapeutic potential.
Institutions: Institut Pasteur du Maroc, University of Hassan II Casablanca
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225452373