Aditi Gangopadhyay

Dr Gangopadhyay is an experienced computational biologist with over 10 years of experience in the field of computer-aided drug design. She has published several articles in peer-reviewed articles in the field of Bioinformatics. Her major research interests include drug repurposing, peptide docking, and application of molecular docking and molecular dynamics simulations for drug development against infectious diseases, with emphasis on dengue. Her post-doctoral studies focused on determining serotype-specific structural differences among dengue viral proteins and identifying serotype-selective hits against dengue using structure-based techniques and drug repurposing. Dr Gangopadhyay has authored more than 10 peer-reviewed papers, several as first or corresponding author, in indexed journals including the Journal of Biomolecular Structure and Dynamics, Computational Biology and Chemistry, the Journal of Molecular Graphics and Modelling, Toxicon, Nature Scientific Reports, and the International Journal of Peptide Research and Therapeutics. She has authored several book chapters as first author, including a book chapter in Annual Reports in Medicinal Chemistry, Elsevier. Dr Gangopadhyay has over 5 years of experience in working with manuscripts in the fields of molecular and cell biology, immunology, microbiology, computational biology and bioinformatics, pharmaceutical sciences, genetics, animal and plant sciences, ecology, environmental sciences, and other similar topics. She has reviewed articles for the Journal of Molecular Modeling, Nature Scientific Reports, Computers in Biology and Medicine, and In Silico Pharmacology. Dr Gangopadhyay joined Edanz as a Research Consultant in 2023. Dr Gangopadhyay has authored book chapters, including a chapter in Annual Reports in Medicinal Chemistry, Elsevier, on the application of computational techniques for drug discovery against tuberculosis. She has also authored a book chapter on anti-cancer therapy, and chapters on the applications of molecular docking and protein structure prediction.