Engineering topics
Pope, Jenna
Publications and source records attributed to Pope, Jenna.
FLAMINGo: Few shot Learning Analysis of Multi-modal Inference Network Graphs
A multi-modal few-shot learning approach is used to identify similarities between particles obtained from environmental samples. The inferred similarities are then used in a similarity graph to identify groupings and outliers in the sample
3D_MolGNN_RL
3D-MolGNNRL, couples reinforcement learning (RL) to a deep generative model based on 3D-Scaffold to generate target candidates specific to a protein pocket building up atom by atom from the core scaffold. 3D-MolGNNRL provides an efficient way to optimize key features within a protein pocket using a parallel graph neural network model. The agent learns to build molecules in 3D space while optimizing the binding affinity, potency, and synthetic accessibility of the candidates generated for the SARS-CoV-2 Main protease
PNNL-CompBio/pf-gnn_pli
We have developed a deep learning based method to decode the protein-ligand interactions and predict their probability of binding. We use 3-Dimensional structure of proteins and ligand molecules with the ligand docked to the receptor and use the information at the interaction region to assess the binding of the protein-ligand complex.