Drug Discovery

Biography

Dr. Chandrabose Selvaraj  earned his Ph.D. in Bioinformatics from the Department of Bioinformatics at Alagappa University, India in 2014. His expertise falls in the area of Computational drug design for anti-viral inhibitors and anti-cancer inhibitors. Dr. Selvaraj completed his postdoctoral research at prestigious institutions such as the Food and Drug Administration (USA), Central European Institute of Technology (CEITEC-Brno), Konkuk University (South Korea), and Indian Institute of Technology-Mandi (India). Currently, he is an Associate Professor at the Center for Global Health Research, Saveetha Institute of Medical and Technical Sciences in Chennai. He has received several prestigious awards, including the Marie Skłodowska-Curie Actions in 2018 and the NIH sponsored ORISE fellowship in 2016. He has also been granted an international travel grant by ApBionet. Dr. Selvaraj has been recognized with young scientist awards from various prestigious societies and has received multiple citations for his scientific contributions. He has published over 125 research papers in peer-reviewed journals, edited 3 books, and contributed to 25 book chapters. He holds three international patents for his work and serves as an editorial board member for 26 journals and a reviewer for 88 journals. His research focuses on understanding molecular-level interactions in human disease mechanisms for chemical biology and drug discovery. He applies theoretical and biophysical methods using various computational approaches, for the filter to be effective in experimental validations.

Education

  • Ph.D. Bioinformatics, Alagappa University - 2014

  • M.Sc. Bioinformatics, Bharathiar University - 2009

  • B.Sc. Microbiology, Madurai Kamaraj University - 2007

Key research areas

  • Antiviral Inhibitor Design

  • Anticancer Inhibitor Design

  • Tools and Database Development

  • Artificial Intelligence for Drug Discovery

Recent Publications

  1. Shiammala, P. N., Duraimutharasan, N. K. B., Vaseeharan, B., Alothaim, A. S., Al-Malki, E. S., Snekaa, B., & Selvaraj, C. (2024). Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods.219, 82-94. . (IF: 5.10).

  2. Selvaraj, C., Pedone, E., Lee, J. K., & Singh, S. K. Molecular Level Atomistic and Structural Insights on Biological Macromolecules, Inhibition, and Dynamics Studies. Frontiers in Molecular Biosciences, 11, 1362215. (IF: 5.81).

  3. Ghasemian, M., Babaahmadi‐Rezaei, H., Khedri, A., & Selvaraj, C. (2023). The oncogenic role of SAMMSON lncRNA in tumorigenesis: A comprehensive review with especial focus on melanoma. Journal of Cellular and Molecular Medicine, 27(24), 3966-3973. (IF: 5.31).

  4. Kathiresan, N., Selvaraj, C., Pandian, S., Subbaraj, G. K., Alothaim, A. S., Safi, S. Z., & Kulathaivel, L. (2023). Proteomics and genomics insights on malignant osteosarcoma. Advances in Protein Chemistry and Structural Biology, 138, 275-300. (IF: 5.20).

  5. Ezhilarasan, D., Shree Harini, K., Karthick, M., & Selvaraj, C. (2024). Ethyl gallate concurrent administration protects against acetaminophen‐induced acute liver injury in mice: An in vivo and in silico approach. Chemical Biology & Drug Design, 103(1), e14369. (IF: 3.00).

  6. Selvaraj, C., Dinesh, D. C., Pedone, E. M., Alothaim, A. S., Vijayakumar, R., Rudhra, O., & Singh, S. K. (2023). SARS-CoV-2 ORF8 dimerization and binding mode analysis with class I MHC: computational approaches to identify COVID-19 inhibitors. Briefings in Functional Genomics, 22(2), 227-240. (IF: 4.86).

  7. Kari, S., Murugesan, A., Thiyagarajan, R., Kidambi, S., Razzokov, J., Selvaraj, C., & Marimuthu, P. (2023). Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification. Biomedicine & Pharmacotherapy, 160, 114320. (IF: 7.50).

  8. Nayarisseri, A., Bhrdwaj, A., Khan, A., Sharma, K., Shaheen, U., Selvaraj, C., & Singh, S. K. (2023). Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model. Briefings in Functional Genomics, 22(2), 204-216. (IF: 4.86).

  9. Selvaraj, C., Chandra, I., & Singh, S. K. (2021). Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Molecular diversity, 1-21. (IF:3.28).