Mr. Harrison Onyango | Drug Design and Discovery | Best Researcher Award

Laboratory Technologist, at Masinde Muliro University of Science and Technology, Kenya.

✨ Harrison Onyango is a dedicated bioinformatics researcher committed to ethical scientific advancements and innovative problem-solving. With a strong academic foundation in biochemistry and bioinformatics, he applies computational techniques to tackle pressing challenges in public health and drug discovery. Harrison’s expertise lies in molecular dynamics simulations, pharmacophore modeling, and artificial intelligence applications in healthcare. His work at HovaPharm Solutions and Masinde Muliro University of Science and Technology underscores his passion for bridging computational tools with biomedical research. His goal is to drive impactful discoveries that enhance healthcare solutions worldwide.

Professional Profile

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Education 🎓

Harrison Onyango holds a B.Sc. in Biochemistry from the Technical University of Kenya (2015) and is pursuing an M.Sc. in Bioinformatics at Masinde Muliro University of Science and Technology, awaiting graduation in 2025. He has undertaken professional training in genome sequencing (WCS & H3ABioNet), bioinformatics (H3ABioNet), and statistical data analysis using R (RuFORUM). These programs have enhanced his computational biology and data analysis skills, allowing him to contribute to cutting-edge biomedical research.

Experience 💼

Harrison has diverse research and professional experience. Currently, he serves as a researcher at HovaPharm Solutions, focusing on drug discovery and computational bioinformatics. Concurrently, he works as a part-time laboratory technologist at Masinde Muliro University of Science and Technology, supporting research and teaching. His previous roles include public service internships at Meru and Masinde Muliro Universities, where he conducted biochemical analyses. Additionally, he contributed to the 2019 Kenya census as a content supervisor and worked as a content writer for Creative Writing Kenya.

Research Interests 📝

Harrison’s research focuses on computational drug discovery, molecular dynamics simulations, and artificial intelligence in bioinformatics. His work explores in silico approaches to identify novel inhibitors for malaria and COVID-19 treatments. He has also investigated vaccine adjuvant leads for Mosquirix and breast cancer diagnosis using AI-driven mutational signatures. His interdisciplinary approach combines machine learning, structural biology, and pharmaceutical chemistry to enhance drug development and disease modeling.

Awards & Grants 🏆

Harrison received a University Innovation Fund (UIF) grant from Masinde Muliro University of Science and Technology (FY22/2023-URF-004), securing Kshs 500,000 for his research on in silico malaria drug discovery. His academic excellence and research contributions have positioned him as a leading scientist in computational biology and pharmaceutical sciences.

Top Noted Publications & Citations 📚

• Onyango, H., et al. (2024)

  • Title: Ligand-based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulations of Pfhsp90 Fingerprint Signatures in Plasmodium Malaria Treatment
  • Journal: Computational and Structural Biotechnology Reports
  • Focus: Identification of potential inhibitors for Plasmodium falciparum heat shock protein 90 (PfHsp90), crucial for malaria treatment, using computational drug discovery techniques.

• Onyango, O. H., et al. (2023)

  • Title: In-silico analysis of potent Mosquirix vaccine adjuvant leads
  • Journal: Journal of Genetic Engineering and Biotechnology
  • Focus: Computational screening of potential adjuvants to enhance the efficacy of the Mosquirix malaria vaccine.

• Onyango, O. H. (2023)

  • Title: In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review
  • Journal: Advances in Pharmacological and Pharmaceutical Sciences
  • Focus: A systematic review of computational models used for identifying potential anti-COVID-19 drugs.

• Odhiambo, P., et al. (2023)

  • Title: Mutational signatures for breast cancer diagnosis using artificial intelligence
  • Journal: Journal of the Egyptian National Cancer Institute
  • Focus: Application of AI to identify mutational signatures in breast cancer for improved diagnostics.

• Onyango, H., et al. (2022)

  • Title: In silico identification of new anti-SARS-CoV-2 main protease (Mpro) molecules with pharmacokinetic properties from natural sources using molecular dynamics (MD) simulations and hierarchical virtual screening
  • Journal: Journal of Tropical Medicine
  • Focus: Computational discovery of natural compounds targeting the SARS-CoV-2 main protease for COVID-19 treatment.