Babatunde Owoloye

The prospect of developing therapeutic agents that can improve the health and quality of life of millions affected by disease has been a constant source of motivation for me. My interest in biomedicine and drug discovery was sparked during the penultimate year of my undergraduate studies after attending a presentation on the phytochemical analysis and antimicrobial properties of Eugenia caryophyllata. This exposure highlighted the medicinal potential of natural compounds and demonstrated how computational approaches can accelerate drug development, motivating me to pursue training in computational biology and drug discovery.

After completing my undergraduate degree, I joined the Biological Sciences Central Laboratory at the Federal University, Lokoja, as a research intern. There, I gained hands-on experience in molecular biology, biochemical assays, and computational biology techniques. I participated in RNA and DNA extraction from diverse biological samples and collaborated on a malaria research project involving the extraction and PCR-based identification of Plasmodium falciparum from dried blood spots using 18S rRNA primers. This experience strengthened my laboratory competence and understanding of applied molecular diagnostics, and the protocol we developed has since been adopted by other students for research projects.

To further develop my computational skills, I was selected for the H3ABioNet 2024 Introduction to Bioinformatics program. During this intensive training, I gained experience navigating public biological databases and performing sequence alignment, phylogenetic analysis, and protein and nucleotide analyses using tools such as EMBOSS, ClustalW, MUSCLE, T-Coffee, and NCBI resources. The training provided a strong foundation in bioinformatics and reinforced my interest in genomics research and personalised medicine.

I am proficient in genomic data analysis within Linux environments and have analysed Plasmodium falciparum genome sequences using tools such as FastQC, Trimmomatic, BWA, SAMtools, and GATK. I also possess strong programming skills in Python and R for genomic analysis, statistics, and data visualisation, and I am experienced with biostatistical software including GraphPad Prism and SPSS.

My exposure to computational drug discovery was further strengthened through HackBio virtual internships in Drug Development and Machine Learning, Next Generation Sequencing, and Coding for Biologists. During these programs, I applied in silico tools such as Schrödinger (Maestro), PyRx, and PyMOL for molecular docking, virtual screening, and molecular dynamics simulations. I also contributed to transcriptomic analysis projects and recently led a study applying machine learning models and molecular simulations to identify potential EGFR inhibitors from natural product databases.

Despite financial challenges during my early undergraduate years, I demonstrated resilience and significant academic improvement, developing strong time-management and problem-solving skills. I find deep fulfilment in computational biology and drug discovery, where my passion for science aligns with my commitment to improving human health through data-driven biological research.