Synthesis of 5,6-Dihydropyran-2-ones as Potential Inhibitors of HIV-1 Protease
Document Type
Oral Presentation
Campus where you would like to present
SURC 135
Start Date
17-5-2012
End Date
17-5-2012
Abstract
Drug discovery and development for HIV/AIDS has led to groundbreaking anti-retroviral therapies including HIV-1 protease inhibitors. However, the rise in resistance to current treatments as well as issues regarding drug toxicity and affinity, generate a need for more effective inhibitory structures. This synthetic chemistry research builds on a previous research effort, in which HIV-1 protease inhibiting structures were designed using molecular modeling methods. Quantitative Structure-Activity Relationship (QSAR) was implemented using a fuzzy neural network to predict the biological activity for these compounds. We are synthesizing the novel structures through known methodologies. The inhibitory values of the target compounds will be determined and compared to the values predicted by the neural networks. We hope that the target compounds will possess better inhibitory properties, increased bioavailability and decreased toxicity compared to currently available inhibitors.
Recommended Citation
Sigurjonsson, Kristín and Nye, Jesse, "Synthesis of 5,6-Dihydropyran-2-ones as Potential Inhibitors of HIV-1 Protease" (2012). Symposium Of University Research and Creative Expression (SOURCE). 93.
https://digitalcommons.cwu.edu/source/2012/oralpresentations/93
Additional Mentoring Department
Chemistry
Synthesis of 5,6-Dihydropyran-2-ones as Potential Inhibitors of HIV-1 Protease
SURC 135
Drug discovery and development for HIV/AIDS has led to groundbreaking anti-retroviral therapies including HIV-1 protease inhibitors. However, the rise in resistance to current treatments as well as issues regarding drug toxicity and affinity, generate a need for more effective inhibitory structures. This synthetic chemistry research builds on a previous research effort, in which HIV-1 protease inhibiting structures were designed using molecular modeling methods. Quantitative Structure-Activity Relationship (QSAR) was implemented using a fuzzy neural network to predict the biological activity for these compounds. We are synthesizing the novel structures through known methodologies. The inhibitory values of the target compounds will be determined and compared to the values predicted by the neural networks. We hope that the target compounds will possess better inhibitory properties, increased bioavailability and decreased toxicity compared to currently available inhibitors.
Faculty Mentor(s)
Levente Fabry-Asztalos