Semester
Fall
Date of Graduation
2022
Document Type
Thesis
Degree Type
MS
College
Eberly College of Arts and Sciences
Department
Chemistry
Committee Chair
Blake Mertz
Committee Co-Chair
Hacer Karatas Bristow
Committee Member
Werner J. Geldenhuys
Committee Member
Mark L. McLaughlin
Abstract
Docking simulations are a vital virtual screening technique to identify potential drug candidates by predicting the binding score and position for protein-ligand interactions. As ligand libraries expand into the billions, the number of groups able to run these simulations decreases drastically. Here we propose a simple methodology allowing for more accessibility of docking large ligand libraries. This method uses simple molecular features and unsupervised learning to cluster like molecules. These clusters are then randomly sampled, creating a training set. A model is trained using the bag of bonds method and a simple neural network to predict the binding affinity of the rest of the library. Using a ten thousand class A, G-protein coupled receptor (GPCR) ligand library, the proposed method is tested against two conformations of the protein platelet-activating factor receptor (PAFR). We could quickly and accurately predict the binding affinity using only five percent of the library through this method.
Recommended Citation
Barber, John, "The Use of Neural Networks in Accelerating Small Molecule Docking of GPCRs" (2022). Graduate Theses, Dissertations, and Problem Reports. 11499.
https://researchrepository.wvu.edu/etd/11499
Embargo Reason
Publication Pending