Phuong-Thao Tran | Medicinal Chemistry | Best Researcher Award

Best Researcher Award

Phuong-Thao Tran
Hanoi University of Pharmacy, Vietnam

Phuong-Thao Tran
Affiliation Hanoi University of Pharmacy
Country Vietnam
Scopus ID 55908125500
Documents 51
Citations 660
h-index 17
Subject Area Medicinal Chemistry, neurodegenerative disorders, Alzheimer’s disease
Event International Academic Achievements & Awards
ORCID 0000-0003-4855-2544

Phuong-Thao Tran is a researcher at Hanoi University of Pharmacy, Vietnam, whose work centers on medicinal chemistry and the discovery of compounds relevant to neurodegenerative disorders, particularly Alzheimer’s disease.[1] This article presents her profile in the context of the Best Researcher Award at the International Academic Achievements & Awards event.

Abstract

Phuong-Thao Tran is a medicinal chemistry researcher affiliated with Hanoi University of Pharmacy. Her publications address the design and evaluation of enzyme inhibitors for Alzheimer’s disease, including inhibitors of human glutaminyl cyclase and acetylcholinesterase, together with related studies in cancer biology.[2] With 51 indexed documents and 660 citations, her record reflects sustained and recognized scholarly activity in drug discovery.

Keywords

Medicinal chemistry, Alzheimer’s disease, neurodegenerative disorders, glutaminyl cyclase inhibitors, acetylcholinesterase, structure–activity relationship, computational drug design.

Introduction

Neurodegenerative disorders, especially Alzheimer’s disease, remain among the most difficult challenges in modern therapeutics. Medicinal chemists contribute by identifying molecular targets and designing compounds that can modulate them. Phuong-Thao Tran works in this field at Hanoi University of Pharmacy, combining synthetic chemistry, structure–activity analysis and computational approaches.[1] Her work has appeared in journals such as the Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry, ACS Omega and Cancers.

Research Profile

According to Scopus, the researcher’s author record (ID 55908125500) includes 51 documents, 660 citations and an h-index of 17.[1] Her principal subject areas are medicinal chemistry and neurodegenerative disorders, with a particular emphasis on Alzheimer’s disease. Her ORCID record provides a further public listing of her scholarly outputs.[5]

Research Contributions

Her contributions can be grouped into several themes:

  • Glutaminyl cyclase inhibitors. Rational design of potent human glutaminyl cyclase inhibitors as candidate anti-Alzheimer’s agents, together with structure–activity studies of inhibitors built on an imidazolyl propyl thiourea template.[2][3]
  • Acetylcholinesterase studies. Computational and experimental work on predicting ligand affinity and identifying possible inhibitors through umbrella sampling and machine learning.[4]
  • Cancer biology. A review of the emerging role of circulating tumor cells in gastric cancer, indicating a broader biomedical scope.

Publications

Selected highly cited publications, with citation counts as listed in the supplied profile data, include:

  • “Discovery of potent human glutaminyl cyclase inhibitors as anti-Alzheimer’s agents based on rational design,” Journal of Medicinal Chemistry 60(6), 2573–2590 (2017); 64 citations.[2]
  • “Emerging role of circulating tumor cells in gastric cancer,” Cancers 12(3), 695 (2020); 60 citations.
  • “Structure Activity Relationship of Human Glutaminyl Cyclase Inhibitors Having an N-(5-Methyl-1H-imidazol-1-yl)propyl Thiourea Template,” Bioorganic & Medicinal Chemistry (2013); 45 citations.[3]
  • “Prediction of AChE-ligand affinity using the umbrella sampling simulation,” Journal of Molecular Graphics and Modelling 93, 107441 (2019); 43 citations.
  • “Identifying possible AChE inhibitors from drug-like molecules via machine learning and experimental studies,” ACS Omega 7(24), 20673 (2022); 35 citations.[4]

Research Impact

The researcher’s impact is reflected in citation metrics and in the longevity of her work. Publications from 2013 to 2022 continue to attract citations, and the most cited paper, on glutaminyl cyclase inhibitors, has 64 citations.[1] An h-index of 17 indicates that at least 17 of her documents have each been cited at least 17 times. Her combination of experimental and computational methods also supports the reuse of her findings in later drug-design studies.

Award Suitability

The Best Researcher Award at the International Academic Achievements & Awards event recognizes researchers with significant scholarly contributions.[5] Phuong-Thao Tran’s profile aligns with such criteria through:

  • A focused research agenda in medicinal chemistry and Alzheimer’s disease therapeutics.
  • A publication record in peer-reviewed international journals.
  • Citation indicators of 660 citations and an h-index of 17.
  • Methodological breadth spanning synthesis, structure–activity analysis and computational modelling.

Conclusion

Phuong-Thao Tran of Hanoi University of Pharmacy has built a research record centered on the discovery of enzyme inhibitors relevant to Alzheimer’s disease. Her publications, citation metrics and methodological range support her nomination for the Best Researcher Award at the International Academic Achievements & Awards event.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Phuong-Thao Tran, Author ID 55908125500. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55908125500
  2. Hoang, V. H., Tran, P. T., Cui, M., Ngo, V. T. H., Ann, J., Park, J., Lee, J., Choi, K., Cho, H., et al. (2017). Discovery of potent human glutaminyl cyclase inhibitors as anti-Alzheimer’s agents based on rational design. Journal of Medicinal Chemistry, 60(6), 2573–2590.
    https://pubmed.ncbi.nlm.nih.gov/28234463/
  3. Tran, P. T., Hoang, V. H., Thorat, S. A., Kim, S. E., Ann, J., Chang, Y. J., Nam, D. W., et al. (2013). Structure activity relationship of human glutaminyl cyclase inhibitors having an N-(5-methyl-1H-imidazol-1-yl)propyl thiourea template. Bioorganic & Medicinal Chemistry.
    https://doi.org/10.1016/j.bmc.2013.08.027
  4. Nguyen, T. H., Tran, P. T., Pham, N. Q. A., Hoang, V. H., Hiep, D. M., & Ngo, S. T. (2022). Identifying possible AChE inhibitors from drug-like molecules via machine learning and experimental studies. ACS Omega, 7(24), 20673.
    https://doi.org/10.1021/acsomega.2c01341
  5. ORCID. (n.d.). Phuong-Thao Tran (ORCID iD 0000-0003-4855-2544). ORCID.
    https://orcid.org/0000-0003-4855-2544

Ravi Maharjan | Pharmaceutics | Best Researcher Award

Dr. Ravi Maharjan | Pharmaceutics | Best Researcher Award

Dr. Ravi Maharjan | Pharmaceutics | Assistant Research Professor at Yonsei University | South Korea

Pharmaceutics is the foundation of Dr. Ravi Maharjan’s distinguished career as a research professor at Yonsei University, Korea, where he has been at the forefront of integrating AI/ML and Digital Twin technologies to revolutionize biopharmaceutical manufacturing, particularly in mRNA and siRNA-based lipid nanoparticle (LNP) vaccine development. Dr. Ravi Maharjan earned his PhD in Pharmaceutics and has since developed a deep expertise in biopharmaceutical process optimization, continuous manufacturing, lyophilization, stabilization strategies, and innovative delivery approaches targeting ocular and brain tissues. His professional experience spans extensive teaching, mentoring, and research leadership roles, where he has guided national and international collaborations with universities, pharmaceutical companies, and research institutions, including Pusan National University, CHA University, RCPE, RNAAnalytics Austria, and IMDEA Spain, demonstrating his ability to bridge academic research with industrial applications. Dr. Ravi Maharjan’s research interests focus on applying AI/ML algorithms for predictive modeling of complex pharmaceutical processes, optimizing formulations, ensuring protein stability, and enhancing the physical and biological stability of therapeutics. His work integrates pharmaceutics, process analytical technologies (PAT), quality by design (QbD), experimental design (DOE), and computational modeling to advance next-generation biopharmaceutical platforms. Among his key research skills are data-driven formulation design, predictive modeling, digital twin deployment, computational optimization of LNP delivery, lyophilized formulation screening, molecular simulation, process monitoring, and the translation of lab-scale research into continuous manufacturing systems. Dr. Ravi Maharjan has an impressive record of scholarly contributions, including over 23 SCIE Q1 publications, two books, two book chapters, more than 115 peer-review assignments, over 30 conference presentations, and eight invited talks. He has served as associate editor for Pharmaceutical Science and Technology (USA), editorial board member for Scientific Reports (Nature Portfolio, UK), Pharmaceutics (Switzerland), and Current Pharmaceutical Biotechnology (UAE), as well as conference organizer for multiple international events. His awards and honors recognize his contributions to both fundamental pharmaceutics and applied biopharmaceutical manufacturing, highlighting his excellence in research innovation and mentorship. Dr. Ravi Maharjan has demonstrated outstanding ability to secure funding, lead interdisciplinary projects, and foster scientific collaboration across countries and institutions, contributing to the advancement of mRNA and siRNA therapeutics, the optimization of excipients, and the improvement of drug delivery systems. His work on digital twins and AI/ML-driven process optimization has paved the way for predictive control in continuous manufacturing, ensuring reproducibility, efficiency, and quality in pharmaceutical production. In conclusion, Dr. Ravi Maharjan embodies the integration of scientific innovation, computational methods, and practical pharmaceutics expertise, making significant impacts in biopharmaceutical sciences, while continuing to mentor the next generation of researchers, drive technological innovation, and advance knowledge in AI-assisted pharmaceutical development.

Profile: Google Scholar

Featured Publications 

  1. Tripathi, J., Thapa, P., Maharjan, R., & Jeong, S. H. (2019). Current state and future perspectives on gastroretentive drug delivery systems. Citations: 246
  2. Maharjan, R., Hada, S., Lee, J. E., Han, H. K., Kim, K. H., Seo, H. J., Foged, C., … (2023). Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach. Citations: 48
  3. Maharjan, R., Kim, K. H., Lee, K., Han, H. K., & Jeong, S. H. (2024). Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches. Citations: 44
  4. Maharjan, R., & Jeong, S. H. (2020). High shear seeded granulation: Its preparation mechanism, formulation, process, evaluation, and mathematical simulation. Citations: 37
  5. Bhujel, R., Maharjan, R., Kim, N. A., & Jeong, S. H. (2021). Practical quality attributes of polymeric microparticles with current understanding and future perspectives. Citations: 29
  6. Maharjan, R., Lee, J. C., Lee, K., Han, H. K., Kim, K. H., & Jeong, S. H. (2023). Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry. Citations: 28
  7. Kim, K. H., Lee, J. E., Lee, J. C., Maharjan, R., Oh, H., Lee, K., Kim, N. A., & Jeong, S. H. (2023). Optimization of HPLCCAD method for simultaneous analysis of different lipids in lipid nanoparticles with analytical QbD. Citations: 17