Physical Chemistry Commons^™

Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei

Mathematics Faculty Publications

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex …

Understanding The Relationship Between Local Environmental Changes And The Function Of The Ph Low Insertion Peptide, Violetta Burns Casamayor

Graduate Theses, Dissertations, and Problem Reports

Cancer is the second leading cause of death in the US with over 1.7 million new cases each year. Current cancer treatments tend to also target healthy tissues due to similarities with cancerous ones, resulting in acute side effects. Early detection is the best approach towards defeating cancer, however, modern imaging techniques require sizeable samples, often implying a late stage in the disease. One common attribute of tumors is their acidic microenvironment, which can be taken advantage of.

The pH Low Insertion Peptide (pHLIP) is a membrane-active peptide that can take advantage of the acidic microenvironment surrounding cancer cells. pHLIP …