Dr. Ranabir Majumder

Background

Dr. Majumder earned his PhD in Cancer Biology and Drug Discovery from the Indian Institute of Technology Kharagpur, India. Specializing in Cancer Biology & Drug Discovery, he focuses on the intersection of Pharmacy and Biomedical Engineering, with a particular emphasis on uncovering the anticancer and chemoresistance properties in cancer treatment. His research contributions span cancer biology, drug discovery, and structural bioinformatics, with a strong emphasis on integrating structure-based computational approaches with experimental validation to achieve mechanistic insight and translational relevance. During his doctoral research at IIT Kharagpur, titled “Anticancer Activity of Aloe vera Leaves Extract against Human Breast Cancer: A Computational and Experimental Approach”, he systematically investigated natural product–derived anticancer agents using a combined in-silico and in-vitro framework. This work led to the identification and mechanistic characterization of multiple bioactive compounds, including Aloe vera constituents such as campesterol, riboflavin, daidzin, and related molecules, establishing their anticancer potential in human breast cancer models. He employed molecular docking, molecular dynamics simulations, and binding energy calculations to elucidate ER alpha-targeting and DNA-intercalating mechanisms, which were subsequently validated using cell-based assays including cytotoxicity, cell-cycle analysis, DNA damage response assays, western blotting, and microscopy. These studies contributed to a deeper understanding of how small molecules interact with nucleic acids/proteins and modulate cancer cell proliferation and survival. 

Building on this foundation, his postdoctoral research at Oklahoma State University expanded into protein-centric drug discovery, focusing on oncogenic signaling proteins, including KRAS. Using long-timescale molecular dynamics simulations and machine learning (PCA/ICA/PaCMAP/UMAP) techniques, he is investigating how specific oncogenic mutations reshape conformational ensembles, alter allosteric communication, and influence druggability, providing mechanistic explanations for mutation-specific functional behavior that cannot be inferred from static structures alone. This work emphasized causality over correlation, linking molecular dynamics to biological outcomes and generating testable hypotheses relevant to precision oncology.