Microsoft Corporation
MOLECULE EMBEDDING USING GRAPH NEURAL NETWORKS AND MULTI-TASK TRAINING

Abstract:

An embedding model maps a graph representation of a molecule to an embedding space. The embedding model may include one or more graph neural network layers that use a message passing framework and one or more attention layers. The one or more attention layers may determine an edge weight for each message received by a receiving node from one or more sending nodes. The edge weight may be based on features of the receiving node and features of the one or more sending nodes. The one or more graph neural network layers may determine embedded features for the graph based on the messages and the edge weights. The embedding model may determine molecule features for the molecule based on the embedded features. The molecule features may map to an embedding space. The embedding model may be trained using multi-task training to generate a more generic embedding space.