Chemprop: A Machine Learning Package for Chemical Property Prediction

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we…

• NS
  National Science Foundation

  Award: 1745302
• MI
  Massachusetts Institute of Technology
• VC
  Virginia Commonwealth University
• AS
  Austrian Science Fund

  Award: J-4415
• KL
  KU Leuven

  Award: STG/22/032
• DO
  Division of Graduate Education

  Award: 1745302
• DS
  Defense Sciences Office, DARPA

  Award: HR00111920025