Using Machine Learning To Predict Suitable Conditions
for Organic Reactions
Version 3 2018-11-28, 05:10
Version 2 2018-11-16, 20:17
Version 1 2018-11-16, 20:15
Posted on 2018-11-28 - 05:10
Reaction
condition recommendation is an essential element for the
realization of computer-assisted synthetic planning. Accurate suggestions
of reaction conditions are required for experimental validation and
can have a significant effect on the success or failure of an attempted
transformation. However, de novo condition recommendation remains
a challenging and under-explored problem and relies heavily on chemists’
knowledge and experience. In this work, we develop a neural-network
model to predict the chemical context (catalyst(s), solvent(s), reagent(s)),
as well as the temperature most suitable for any particular organic
reaction. Trained on ∼10 million examples from Reaxys, the
model is able to propose conditions where a close match to the recorded
catalyst, solvent, and reagent is found within the top-10 predictions
69.6% of the time, with top-10 accuracies for individual species reaching
80–90%. Temperature is accurately predicted within ±20
°C from the recorded temperature in 60–70% of test cases,
with higher accuracy for cases with correct chemical context predictions.
The utility of the model is illustrated through several examples spanning
a range of common reaction classes. We also demonstrate that the model
implicitly learns a continuous numerical embedding of solvent and
reagent species that captures their functional similarity.
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Gao, Hanyu; Struble, Thomas J.; Coley, Connor W.; Wang, Yuran; Green, William H.; Jensen, Klavs F. (2018). Using Machine Learning To Predict Suitable Conditions
for Organic Reactions. ACS Publications. Collection. https://doi.org/10.1021/acscentsci.8b00357
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AUTHORS (6)
HG
Hanyu Gao
TS
Thomas J. Struble
CC
Connor W. Coley
YW
Yuran Wang
WG
William H. Green
KJ
Klavs F. Jensen