Generating molecules with optimized aqueous solubility using iterative graph translation

Abstract

We present a generative modeling framework that can be used to discover new, optimal molecules. Our method involves iteratively 1) training a translation model, and 2) translating all molecules in the training dataset.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2022
Source ID
10.1039/d1re00315a

Entities

People

  • Camille L Bilodeau
  • Hongyun Xu
  • Jillian A Emerson
  • Klavs F. Jensen
  • Regina Barzilay
  • Sukrit Mukhopadhyay
  • Thomas H. Kalantar
  • Tommi S. Jaakkola
  • Wengong Jin

Organizations

  • Defense Advanced Research Projects Agency
  • Defense Threat Reduction Agency
  • Dow Chemical Company
  • Massachusetts Institute of Technology

Tags

Readers

  • Molecular and Cellular Biology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation
  • AI & ML - Neural Networks