Nigerian scientist, Dr Elijah Kolawole Oladipo, has led an international research team that used artificial intelligence to identify promising molecules for safer, more targeted breast cancer treatment.

The study, published in the Future Journal of Pharmaceutical Sciences, combined AI-powered drug discovery with advanced 3D protein modelling to identify therapeutic peptides capable of targeting proteins associated with breast cancer.

Oladipo, who is based at Adeleke University and the Helix Biogen Institute, led researchers from Nigeria, Ethiopia, the United Kingdom and the United States in the study.

The researchers used machine-learning algorithms and Colab AlphaFold2 to screen a global database containing more than 1,500 natural peptides.

The AI system assessed the compounds for structural stability, toxicity and allergenic potential before narrowing the search to three leading candidates derived from animal sources.

The shortlisted molecules were then subjected to virtual simulations against Matrix Metalloproteinase 1 and Epidermal Growth Factor Receptor, two proteins associated with breast cancer progression.

According to the researchers, one AI-optimised peptide inspired by the green shield bug, Metalnikowin IIA, showed stronger binding affinity, greater precision and superior structural stability than Buserelin, an FDA-approved anticancer peptide used as the benchmark.

Oladipo said the research demonstrated how AI could accelerate the search for safer cancer therapies by enabling scientists to design precise, nature-inspired treatments aimed at targeting cancer cells while sparing healthy tissue.

The researchers, however, cautioned that the findings remain at the computational stage and have not been validated through laboratory or clinical studies.

They said further testing in living cells and animal models would be required before the compounds could progress towards human treatment.

The team is seeking international research grants, pharmaceutical partnerships and global health collaborations to fund laboratory validation of the findings.