A Nigerian researcher, Dr. Gideon Gyebi, has said that emerging computational technologies can accelerate the discovery of new antibiotics and help combat the growing global challenge of antimicrobial resistance (AMR).
Gyebi, a scientist in Computational and Systems Biology, stated this while highlighting findings from his recent study in Abuja on Tuesday.
The study, titled “Computational Profiling of Terpenoids for Putative Dual-Target Leads Against Staphylococcus aureus Penicillin-Binding Protein 2a and Beta-Lactamase,” was conducted at the Durban University of Technology, South Africa.
It demonstrates how Artificial Intelligence (AI), machine learning, and molecular modelling can fast-track drug discovery and reduce costs.
According to Gyebi, the research focused on Staphylococcus aureus (S. aureus)—a bacterium responsible for many hospital-acquired infections and a major contributor to antimicrobial resistance.
He noted that the emergence of Methicillin-Resistant Staphylococcus aureus (MRSA) had made treatment more difficult globally, limiting the effectiveness of commonly used antibiotics.
“By focusing on S. aureus, this study directly addresses the urgent need for innovative strategies to combat antimicrobial resistance,” Gyebi said.
“Computational biology is transforming the way we think about medicine. By simulating how potential drugs interact with bacterial proteins, we can guide experiments more intelligently and make discoveries faster.”
He explained that computational studies provide speed, cost efficiency, and precision often not achievable with traditional laboratory methods.
“Thousands of compounds can be virtually screened within hours, whereas the same process in the lab could take months or even years,” he said.
Gyebi added that computational modelling allows researchers to visualise how drugs interact with bacterial proteins at the molecular level, helping to identify promising compounds before expensive laboratory testing begins.
He stressed that computational tools do not replace experiments but complement them, providing a roadmap that makes laboratory studies more focused and efficient.
“Emerging computational approaches increase our chances of overcoming antibiotic resistance by speeding up discovery and reducing trial and error,” he said.
The study identified natural compounds known as terpenoids that could block two major bacterial defence systems—Penicillin-Binding Protein 2a (PBP2a) and Beta-lactamase (B-lactamase)—simultaneously.
“The enzyme Beta-lactamase degrades antibiotics before they can act, while PBP2a has low affinity for most antibiotics. Targeting both mechanisms could restore the effectiveness of drugs that S. aureus has learned to resist,” he explained.
Gyebi said that a dual-target approach significantly improves the chances of antibiotics working effectively against resistant bacteria.
According to the World Health Organization (WHO), antimicrobial resistance is one of the top ten global public health threats, projected to cause 10 million deaths annually by 2050 if not contained.
With more than 70 scientific publications indexed in Scopus and Web of Science and over 1,000 citations, Gyebi is among Africa’s emerging scientists using technology to address global health challenges.
He emphasised that integrating computational studies, AI, and biotechnology could redefine global antibiotic discovery and provide faster solutions to the escalating threat of drug-resistant infections.
