As the global threat of antimicrobial resistance (AMR) deepens, a Nigerian researcher, Dr. Gideon Gyebi, is championing the use of artificial intelligence (AI) and computational biology to accelerate the discovery of new antibiotics and restore the effectiveness of existing ones.
Dr. Gyebi, a specialist in Computational and Systems Biology, recently unveiled findings from his study titled “Computational Profiling of Terpenoids for Putative Dual-Target Leads Against Staphylococcus Aureus Penicillin-Binding Protein 2a and Beta-Lactamase.” The research, conducted at the Durban University of Technology, South Africa, highlights how AI, machine learning, and molecular modelling can transform traditional drug discovery by predicting how new compounds interact with bacterial proteins.
Focusing on Staphylococcus aureus (S. aureus)—a bacterium responsible for many hospital-acquired infections and a key symbol of antibiotic resistance—the study explores innovative ways to outsmart Methicillin-Resistant Staphylococcus Aureus (MRSA). MRSA’s ability to evade treatment has made it one of the world’s most formidable public health challenges, limiting the effectiveness of widely used antibiotics.
“Computational biology is transforming the way we think about medicine,” Gyebi explained. “By simulating how potential drugs interact with bacterial proteins, we can guide experiments more intelligently, reduce costs, and make discoveries much faster.”
Unlike traditional laboratory methods that can take months or even years, computational studies allow scientists to virtually screen thousands of compounds within hours, pinpointing the most promising candidates for further testing. Gyebi noted that while these tools do not replace laboratory experiments, they complement them by providing a precise roadmap for experimental validation — saving both time and resources.
His study zeroes in on terpenoids, a class of natural compounds known for their diverse biological activities. Using computational modelling, Gyebi and his team identified terpenoids capable of simultaneously blocking two key bacterial defence mechanisms — the Penicillin-Binding Protein 2a (PBP2a) and the Beta-Lactamase enzyme.
“The synthesis of Beta-Lactamase allows bacteria to degrade antibiotics before they can act, while PBP2a reduces the ability of antibiotics to bind effectively,” he explained. “Together, they form a double defence system that makes MRSA particularly difficult to treat. A dual-target approach that blocks both mechanisms could restore the potency of common antibiotics that these bacteria have learned to resist.”
This strategy, Gyebi said, could mark a new frontier in antibiotic re-engineering, potentially reviving the usefulness of existing drugs against resistant strains.
The urgency of his work is underscored by the World Health Organisation’s warning that antimicrobial resistance is one of the top ten global health threats, projected to cause up to 10 million deaths annually by 2050 if left unchecked.
With over 70 publications indexed in Scopus and Web of Science and more than 1,000 citations, Dr. Gyebi stands among a growing cadre of African scientists leveraging technology to tackle complex biomedical challenges. His vision is clear: merging computational studies, AI, and biotechnology to redefine the global antibiotic discovery pipeline and deliver faster, smarter solutions to the escalating crisis of drug-resistant infections.