Analyzing Learned Molecular Representations for Property Prediction
Name
acs.jcim.9b00237.pdf
Description
Accepted version
Size
2.98 MB
Format
Adobe PDF
Checksum (MD5)
db52a9a6ef6861f6081adfa2aea59261
Author(s)
Yang, Kevin
Swanson, Kyle
Jin, Wengong
Coley, Connor
Eiden, Philipp
Gao, Hua
Guzman-Perez, Angel
Hopper, Timothy
Kelley, Brian
Mathea, Miriam
Date Issued
2019
Journal
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Version
Author's final manuscript
Abstract
© 2019 American Chemical Society. Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial data sets spanning a wide variety of chemical end points. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary data sets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.jcim.9b00237
