Image segmentation and object detection of fluorescent neuronal cells using a pretrained deep learning model (ongoing project)
In this project I am working with machine learning, deep learning, and Python programming to identify the most accurate model for the study.
The goal is to automate the segmentation and counting of fluorescent neuronal cells to improve efficiency and reduce manual annotation errors in biomedical research.
Illuminating the antimicrobial activities of small molecules present in Abutilon theophrasti against drug-resistant Neisseria gonorrhoeae: an in-silico approach
Targeted the unexplored virulent FtsY protein of Neisseria gonorrhoeae to address multidrug resistance, modeled its 3D structure through homology modeling, and validated it using structural analysis tools because no experimentally resolved crystallographic structure was available.
Created and evaluated a virtual screening phytochemical database from Abutilon theophrasti.
Identified Chrysoeriol as a top candidate with favorable pharmacokinetics and a strong binding affinity (-6.9 kcal/mol), outperforming the control antibiotic ceftriaxone against the target.
