Chemprop: A Machine Learning Package for Chemical Property Prediction
Name
heid-et-al-2023-chemprop-a-machine-learning-package-for-chemical-property-prediction.pdf
Description
Published version
Size
1.82 MB
Format
Adobe PDF
Checksum (MD5)
15203b2ac612c8407811625995dd4c5b
Author(s)
Heid, Esther
Greenman, Kevin P
Chung, Yunsie
Li, Shih-Cheng
Graff, David E
Vermeire, Florence H
Wu, Haoyang
Green, William H
McGill, Charles J
Date Issued
December 26, 2023
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society
Citation
Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, and Charles J. McGill. Journal of Chemical Information and Modeling 2024 64 (1), 9-17.
Version
Final published version
Abstract
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 incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.jcim.3c01250
