A graph-convolutional neural network model for the prediction of chemical reactivity

Abstract

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2019
Source ID
10.1039/c8sc04228d

Entities

People

  • Connor W. Coley
  • Klavs F. Jensen
  • Luke Rogers
  • Regina Barzilay
  • Timothy F. Jamison
  • Tommi S. Jaakkola
  • Wengong Jin
  • William H. Green

Organizations

  • Defense Advanced Research Projects Agency
  • MIT Computer Science and Artificial Intelligence Laboratory
  • Massachusetts Institute of Technology
  • National Science Foundation
  • United States Army
  • University of Cambridge
  • Yusuf Hamied Department of Chemistry

Tags

Fields of Study

  • Chemistry

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks