Analyzing Learned Molecular Representations for Property Prediction
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Abstract
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.
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The KM prediction model uses a graph neural network to create a 50-dimensional task-specific fingerprint of the substrate.
“The K M prediction model uses a graph neural network (GNN) 71 , 72 to create a 50-dimensional task-specific fingerprint of the substrate.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [72]
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- Authors
- Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., & Barzilay, R.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Massachusetts Institute of Technology (US)
- BASF (Germany) (DE)
- Amgen (United States) (US)
- Amgen Inc., Cambridge, Massachusetts 02141, United States
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States
- Corresponding author
- Yang, Kevin
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-07-30
- Volume
- 59
- Issue
- 8
- Pages
- 3370-3388
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC6727618
- OpenAlex
- W2966357564
- Cited by
- 1,923
- References
- 47
- Citations by year
- 2026: 415 · 2025: 391 · 2024: 348 · 2023: 302 · 2022: 195 · 2021: 171 · 2020: 92 · 2019: 9
- FWCI
- 80.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- Novartis
- BASF
- Defense Advanced Research Projects Agency (ARO W911NF-16-2-0023)
- Massachusetts Institute of Technology
- Amgen
- Machine Learning for Pharmaceutical Discovery and Synthesis Consortium
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- molecular property prediction, graph convolutional networks, molecular fingerprints, molecular descriptors, benchmarking, reproducibility
- MeSH terms
- Computer Graphics, Neural Networks, Computer
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