Priyanka Kumari

Detail-oriented team player with strong organizational skills. Ability to handle multiple projects simultaneously with a high degree of accuracy.

• Kumari, P., Van Laethem, T., Hubert, P., Fillet, M., Sacré, P. Y., & Hubert, C. (2023). Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy. Molecules, 28(4), 1696.
• Kumari, P., Van Laethem, T., Hubert, P., Fillet, M., Sacré, P. Y., & Hubert, C. (2023). mTQSRR: A multitarget approach of Quantitative Structure Retention-Relationship Modeling of small molecules (Under Review)
• Kumari, P., Pradhan, B., Koromina, M., Patrinos, G. P., & Steen, K. V. (2022). Discovery of new drug indications for COVID-19: A drug repurposing approach. PloS one, 17(5), e0267095.
• Van Laethem, T., Kumari, P., Hubert, P., Fillet, M., Sacré, P. Y., & Hubert, C. (2022). A pharmaceutical-related molecules dataset for reversed-phase chromatography retention time prediction built on combining pH and gradient time conditions. Data in Brief, 42, 108017.
• Van Laethem, T., Kumari, P., Boulanger, B., Hubert, P., Fillet, M., Sacré, P. Y., & Hubert, C. (2022). User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules. Molecules, 27(23), 8306.
• Kumari, P., Nath, A., & Chaube, R. (2015). Identification of human drug targets using machine-learning algorithms. Computers in biology and medicine, 56, 175-1

• Conference Proceedings (Peer review):
• Srivastava, A., Shiva, M., Kumari, P., & Hasija, Y. (2017). Extensive computational analysis of chromosome 11. International Journal of Bioinformatics Research and Applications, 13(3), 242-263.
• Kumari, P., Singh, A., & Hasija, Y. (2017, June). Functional Annotation of Hypothetical proteins of Lactobacillus rhamnosus: An In Silico Approach. In Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science (pp. 65-69). ACM
• Book Chapter:
• · Nath, A., Kumari, P., & Chaube, R. (2018). Prediction of human drug targets and their interactions using machine learning methods: current and future perspectives. Computational Drug Discovery and Design, 21-30.
  

• Current: Different approaches of QSRR modelling of small pharmaceutical compounds in Reverse Phase Liquid Chromatography, University of Liege, BELGIUM
• Past:-
• Drug repurposing for Covid-19 using unsupervised machine learning techniques(BIO3,GIGA,CHU, ULiege,BELGIUM)
• Image based retention property prediction of small molecules in RPLC(LCAP, CIRM, BELGIUM)
• Identifying major players corresponding to oxidative stress in T2D: “A protein-protein Interaction analysis”, IISC, Bangalore, INDIA.
• Properties and identification of Human protein Drug Targets and their classification using machine learning algorithms”, BHU, INDIA
• Tools and Methodology: R, Python, sci-kit learn, Supervised and unsupervised machine learning algorithms (regression), deep learning, and transfer learning.

• Award:
• Vice chancellor Gold Medal for academic excellence in MTech at Delhi Technological University(DCE) · Dec 2017
• Scholarships/Fundings:
• GATE Fellowship by AICTE, INDIA
• EOS-FNRS funding for PhD , University of Liege, Belgium

• Oral Presentations:
• QSRR for small pharmaceutical compounds in RPLC: A machine learning approach. CAC-2022, Rome, ITALY
• Quantitative structure retention relationship of small compounds in Liquid chromatography E-chimiometrics-2021, BELGIUM
• Discovering anti-aging drugs utilizing unsupervised learning and pharmacokinetic study 6Th International Conference on Bioinformatics and Biomedical Engineering 2017,, SINGAPORE