Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction

The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify…

• NS
  National Science Foundation

  Award: 1122374
• DA
  Defense Advanced Research Projects Agency

  Awards: W911NF-16-2-0023, ARO W911NF-16-2-0023
• DO
  Division of Graduate Education

  Award: 1122374
• AR
  Army Research Office

  Awards: W911NF-16-2-0023, W911NF