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Generating molecules with optimized aqueous solubility using iterative graph translation
Name
d1re00315a.pdf
Description
Published version
Size
4.14 MB
Format
Adobe PDF
Checksum (MD5)
b974d1abb6979936a8c89a4c534d687a
Author(s)
Bilodeau, Camille
Jin, Wengong
Xu, Hongyun
Emerson, Jillian A
Mukhopadhyay, Sukrit
Kalantar, Thomas H
Jaakkola, Tommi
Barzilay, Regina
Jensen, Klavs F
Date Issued
November 15, 2021
Journal
Reaction Chemistry & Engineering
Publisher
Royal Society of Chemistry (RSC)
Citation
Bilodeau, Camille, Jin, Wengong, Xu, Hongyun, Emerson, Jillian A, Mukhopadhyay, Sukrit et al. 2021. "Generating molecules with optimized aqueous solubility using iterative graph translation." Reaction Chemistry & Engineering.
Version
Final published version
Abstract
While molecular discovery is critical for solving many scientific problems, the time and resource costs of experiments make it intractable to fully explore chemical space. Here, we present a generative modeling framework that proposes novel molecules that are 1) based on starting candidate structures and 2) optimized with respect to one or more objectives or constraints. We explore how this framework performs in an applied setting by focusing on the problem of optimizing molecules for aqueous solubility, using an experimental database containing data curated from the literature. The resulting model was capable of improving molecules with a range of starting solubilities. When synthetic feasibility was applied as a secondary optimization constraint (estimated using a combination of synthetic accessibility and retrosynthetic accessibility scores), the model generated synthetically feasible molecules 83.0% of the time (compared with 59.9% of the time without the constraint). To validate model performance experimentally, a set of candidate molecules was translated using the model and the solubilities of the candidate and generated molecules were verified experimentally. We additionally validated model performance via experimental measurements by holding out the top 100 most soluble molecules during training and showing that the model could rediscover 33 of those molecules. To determine the sensitivity of model performance to dataset size, we trained the model on different subsets of the initial training dataset. We found that model performance did not decrease significantly when the model was trained on a random 50% subset of the training data but did decrease when the model was trained on subsets containing only less soluble molecules (i.e., the bottom 50%). Overall, this framework serves as a tool for generating optimized, synthetically feasible molecules that can be applied to a range of problems in chemistry and chemical engineering.
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
10.1039/d1re00315a
