Mr. Emmanuel Onah | Biomedical Engineering | Best Researcher Award

Mr. Emmanuel Onah | Southern Illinois University | United States

Mr. Emmanuel Onah is a highly accomplished biomedical scientist and pharmaceutical researcher currently pursuing a Ph.D. in Biomedical Sciences with a concentration in Medicinal Chemistry at Southern Illinois University System. He holds a B. Pharm degree from the University of Nigeria, Nsukka, graduating with distinctions. His research expertise spans computational drug discovery, molecular modeling, pharmacognosy, and medicinal chemistry, with a strong focus on applying machine learning and in silico techniques to address biomedical challenges. Notably, he developed an innovative system to predict HIV-1 protease cleavage sites using hybrid machine learning classifiers, significantly accelerating the identification of novel protease inhibitors. He has also constructed predictive models for thyroid cancer recurrence and collaborated with leading medicinal chemists on molecular docking, pharmacophore modeling, and virtual screening of synthetic and natural compounds. His internship at the National Institute for Pharmaceutical Research and Development involved evaluating pharmaceutical formulations, assessing antimicrobial activity of plant extracts, and exploring hematinic properties of medicinal plants. As an undergraduate, he conducted in silico docking of FDA-approved drugs against antipsoriatic targets and synthesized pharmaceutical dyes characterized via UV-Vis spectroscopy. With 18 publications cited 192 times and an h-index of 7, Mr. Onah demonstrates a strong commitment to advancing medicinal chemistry and translational biomedical research.

Profile: Scopus | Orcid

Featured Publications

Onah, E., Ibezim, A., Osigwe, S. C., Okoroafor, P. U., Ukoha, O. P., de Siqueira-Neto, J. L., Ntie-Kang, F., & Ramanathan, K. (2024). Potential dual inhibitors of hexokinases and mitochondrial complex I discovered through machine learning approach. Scientific African.

Onah, E., Eze, U. J., Abdulraheem, A. S., Ezigbo, U. G., & Amorha, K. C. (2024, September 26). Optimizing unsupervised feature engineering and predictive models for thyroid cancer recurrence prediction [Preprint]. Crossref.

Ibezim, A., Onah, E., Osigwe, S. C., Okoroafor, P. U., Ukoha, O. P., de Siqueira-Neto, J. L., Ntie-Kang, F., & Ramanathan, K. (2023). Potential dual inhibitors of hexokinases and mitochondrial complex I discovered through machine learning approach [SSRN].

Onah, E., Uzor, P. F., Ugwoke, I. C., Eze, J. U., Ugwuanyi, S. T., Chukwudi, I. R., & Ibezim, A. (2022). Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors. BMC Bioinformatics.

Onah, E., Uzor, P. F., Ugwoke, I. C., Eze, J. U., Ugwuanyi, S. T., Chukwudi, I. R., & Ibezim, A. (2022). Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors [Preprint]. Research Square.

Ibezim, A., Onah, E., Dim, E. N., & Ntie-Kang, F. (2021). A computational multi-targeting approach for drug repositioning for psoriasis treatment. BMC Complementary Medicine and Therapies.