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- W4313483804 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." @default.
- W4313483804 created "2023-01-06" @default.
- W4313483804 creator A5050073437 @default.
- W4313483804 date "2023-01-04" @default.
- W4313483804 modified "2023-09-29" @default.
- W4313483804 title "The Use of Neural Networks in Accelerating Small Molecule Docking of GPCRs" @default.
- W4313483804 doi "https://doi.org/10.33915/etd.11499" @default.
- W4313483804 hasPublicationYear "2023" @default.
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