Machine learning for asymmetric catalysis

Jake Yeston

doi:10.1126/science.367.6478.638-e

A Molecular Stereostructure Descriptor Based On Spherical Projection

Synlett ◽

10.1055/s-0040-1705977 ◽

2020 ◽

Author(s):

Shuo-Qing Zhang ◽

Xin Hong ◽

Li-Cheng Xu ◽

Xin Li ◽

Miao-Jiong Tang ◽

...

Keyword(s):

Machine Learning ◽

Phosphoric Acid ◽

Asymmetric Catalysis ◽

Van Der Waals ◽

Chiral Phosphoric Acid ◽

Key Features ◽

Precise Representation ◽

Spherical Projection

AbstractDescription of molecular stereostructure is critical for the machine learning prediction of asymmetric catalysis. Herein we report a spherical projection descriptor of molecular stereostructure (SPMS), which allows precise representation of the molecular van der Waals (vdW) surface. The key features of SPMS descriptor are presented using the examples of chiral phosphoric acid, and the machine learning application is demonstrated in Denmark’s dataset of asymmetric thiol addition to N-acylimines. In addition, SPMS descriptor also offers a color-coded diagram that provides straightforward chemical interpretation of the steric environment.

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A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1916392117 ◽

2020 ◽

Vol 117 (3) ◽

pp. 1339-1345 ◽

Cited By ~ 3

Author(s):

Sukriti Singh ◽

Monika Pareek ◽

Avtar Changotra ◽

Sayan Banerjee ◽

Bangaru Bhaskararao ◽

...

Keyword(s):

Machine Learning ◽

Asymmetric Catalysis ◽

Predictive Power ◽

Enantiomeric Excess ◽

Asymmetric Hydrogenation ◽

Gradient Boosting ◽

Steady Increase ◽

Asymmetric Transformations ◽

Asymmetric Catalysts ◽

Extreme Gradient Boosting

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate–catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

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Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol

10.21203/rs.3.rs-820030/v1 ◽

2021 ◽

Author(s):

Xiaofei Sun ◽

Jingyuan Zhu ◽

Hengzhi You ◽

Bin Chen ◽

Fener Chen

Keyword(s):

Machine Learning ◽

Asymmetric Catalysis ◽

Chemical Space ◽

Asymmetric Hydrogenation ◽

Machine Learning Algorithms ◽

Accurate Prediction ◽

Coupling Reactions ◽

Data Set ◽

Uniform Machine ◽

Reaction Performance

Abstract Synthetic reactions, especially asymmetric reactions are key components of modern chemistry. Chemists have put enormous experimental effort into recognizing various molecule patterns to enable efficient synthesis and asymmetric catalysis. Recent application of machine learning algorithms and chemoinformatics in this field demonstrated their huge potential in facilitating this process by accurate prediction. However, existing methods are relatively limited to specific designed data set, and only implement single prediction of reaction performance or reaction enantioselectivity, rendering their general use in broader scenarios challenging. Here we provide a uniform machine learning protocol that can predict both reaction performance and enantioselectivity with high accuracy. Reconstruction of molecular chemical space derived from more comprehensive three-dimensional atomic and molecular descriptors allow for training of our neural network-based model over four representative datasets. This uniform machine learning protocol was validated with outperformance of accuracy than other methods over all four cases (C-C, C-N, C-S cross coupling reactions and asymmetric hydrogenation) in the prediction of both reaction performance and enantioselectivity. It was also successfully applied to the out-of-set and sparse set prediction, leveraging its possible wide application in accelerating synthesis improve and molecular architects.

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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Chemical Science ◽

10.1039/c9sc01844a ◽

2019 ◽

Vol 10 (27) ◽

pp. 6697-6706 ◽

Cited By ~ 16

Author(s):

Yehia Amar ◽

Artur M. Schweidtmann ◽

Paul Deutsch ◽

Liwei Cao ◽

Alexei Lapkin

Keyword(s):

Machine Learning ◽

Asymmetric Catalysis ◽

Molecular Descriptors ◽

Process Development ◽

Solvent Selection ◽

Significant Challenge

Rational solvent selection remains a significant challenge in process development.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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