Uploaded June 2015 | Updated September 2026, 3 days ago
This talk was given by undergraduate T.J. Tkacik during the 8th Annual Computer Science Undergraduate Research Symposium in 2014. T.J.‘s research was supervised by Dr. Diane Pozefsky.
"Analysis and QSAR Modeling of Human Intestinal Transporter Database"
Membrane transport proteins are the molecular gatekeepers that regulate the movement of chemicals into and out of every cell of every living organism. In this study, a cheminformatics approach was taken to predict the substrate and inhibitory activities of 14 major human intestinal transporters using quantitative structure-activity relationship (QSAR) models built from 56 datasets. Dataset compounds were represented using two types of chemical descriptors and modeled using three supervised learning techniques. The predictive power of these predictors was analyzed for correlations with characterizing data of the original datasets.
T.J. Tkacik is a senior majoring in computer science and chemistry from Fort Mill, South Carolina. He is especially interested in algorithms and the application of computational methods to research in the natural sciences. After graduation, T.J. will work as a software developer for Epic Systems before likely completing graduate education in computer science or cheminformatics.
https://cs.unc.edu/academics/undergraduate/symposium/symposium-2014/
This talk was given by undergraduate T.J. Tkacik during the 8th Annual Computer Science Undergraduate Research Symposium in 2014. T.J.‘s research was supervised by Dr. Diane Pozefsky.
"Analysis and QSAR Modeling of Human Intestinal Transporter Database"
Membrane transport proteins are the molecular gatekeepers that regulate the movement of chemicals into and out of every cell of every living organism. In this study, a cheminformatics approach was taken to predict the substrate and inhibitory activities of 14 major human intestinal transporters using quantitative structure-activity relationship (QSAR) models built from 56 datasets. Dataset compounds were represented using two types of chemical descriptors and modeled using three supervised learning techniques. The predictive power of these predictors was analyzed for correlations with characterizing data of the original datasets.
T.J. Tkacik is a senior majoring in computer science and chemistry from Fort Mill, South Carolina. He is especially interested in algorithms and the application of computational methods to research in the natural sciences. After graduation, T.J. will work as a software developer for Epic Systems before likely completing graduate education in computer science or cheminformatics.
https://cs.unc.edu/academics/undergraduate/symposium/symposium-2014/
