Ndubuisi Ugah 
What once demanded a decade of lab trials and millions of naira in reagents can now be sketched out in a matter of months, thanks to a wave of artificial-intelligence tools reshaping pharmaceutical R&D in Nigeria.
Speaking at the faculty of pharmacy public lecture at the University of Uyo, pharmacognosist Dr Enema Onojah John described how machine-learning models trained on spectroscopic data, ethnobotanical records and historical clinical outcomes now pinpoint the most promising plant molecules long before glassware comes off the shelf.
The core advantage, he explained, is predictive triage. AI engines rapidly rank thousands of phytochemicals for binding affinity, toxicity risk and synthetic feasibility; three hurdles that traditionally soak up years of trial-and-error.
“Instead of extracting and testing different parts of plants and wasting time isolating compounds that may not have any impacts, AI can help predict the effectiveness of specific modification of a chemical structure of compounds, reducing many years of research,” Dr Enema said, adding that early pilots have trimmed wet-lab spending by nearly half while tripling the number of viable leads entering pre-clinical studies.
A key proof point came earlier this year when an AI-nominated quercetin analogue from Dennettia tripetala advanced smoothly through animal models, cutting inflammatory pain in mice by 72 percent with no observable organ stress.
The compound emerged from a mixed dataset of local field collections, public repositories and proprietary spectra run through convolutional neural networks, a workflow Dr Enema calls “computational ethnopharmacology.”
Although several research groups are developing their own pipelines, Zeeg AI, Dr Enema’s Lagos-based start-up, has been first to integrate real-time field interactions with AI, helping to reshape how research is done.
By giving botanists in Calabar or Jos the ability to beam mass spectra straight to the cloud for instant ranking, the platform transforms remote forests into high-throughput screening grounds.
Looking ahead, Dr Enema envisions a national network where public labs, universities and local pharma firms share anonymised datasets, steadily improving model accuracy and reducing Nigeria’s reliance on imported finished drugs.
“AI is not replacing the bench,” he emphasized, “but it is telling us exactly where to look, compressing discovery timelines so our biodiversity can translate into real medicines while the need is still urgent.”
