Evaluating and clustering retrosynthesis pathways with learned strategy
Name
d0sc05078d.pdf
Description
Published version
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
327cefe6036f4e530bce9fb39bc148a0
Author(s)
Mo, Yiming
Guan, Yanfei
Verma, Pritha
Guo, Jiang
Fortunato, Mike E
Lu, Zhaohong
Coley, Connor W
Jensen, Klavs F
Date Issued
2021
Journal
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Version
Final published version
Abstract
With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of a holistic pathway evaluation mechanism makes it challenging to systematically prioritize strategic pathways except for using some simple heuristics. Herein, we introduce a data-driven approach to evaluate the relative strategic levels of retrosynthesis pathways using a dynamic tree-structured long short-term memory (tree-LSTM) model. We first curated a retrosynthesis pathway database, containing 238k patent-extracted pathways along with ∼55 M artificial pathways generated from an open-source CASP program, ASKCOS. The tree-LSTM model was trained to differentiate patent-extracted and artificial pathways with the same target molecule in order to learn the strategic relationship among single-step reactions within the patent-extracted pathways. The model achieved a top-1 ranking accuracy of 79.1% to recognize patent-extracted pathways. In addition, the trained tree-LSTM model learned to encode pathway-level information into a representative latent vector, which can facilitate clustering similar pathways to help illustrate strategically diverse pathways generated from CASP programs.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution 3.0 unported license
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DOI of Published Version
https://doi.org/10.1039/d0sc05078d
