Surveying Enantioselectivity of Two Candida antarctica‐lipase‐B Homologs Towards Chiral sec‐Alcohols

Kim, Younghyun; Park, Seongsoon

doi:10.1002/bkcs.11289

“…5 ). These include the correct product prediction including chirality for kinetic resolutions using lipases (reactions (1) 40 and (2)), 41 two enantioselective reductions of ketones using alcohol dehydrogenases (reaction (3) 42 and (4)), 43 an enantioselective imine reduction (reaction (5)) 44 and reductive amination with a transaminase (reaction (6)). 45 …”

Section: Resultsmentioning

confidence: 99%

“…The types of enzymatic reactions predicted correctly by the Enzymatic Transformer are well illustrated by selected cases (Figure 5). These include the correct product prediction including chirality for kinetic resolutions using lipases (reactions 1 40 and 2), 41 two enantioselective reductions of ketones using alcohol dehydrogenases (reaction 3 42 and 4), 43 an enantioselective imine reduction (reaction 5) 44 and reductive amination with a transaminase (reaction 6). 45 Considering that none of the products of these reactions have been seen by the model during training, the ability of the Enzymatic Transformer to predict not only the correct reaction product but also the correct stereochemical outcome of the enantiomeric resolution reactions is remarkable.…”

Section: Examples Of Correct and Incorrect Predictions By The Enzymatic Transformermentioning

confidence: 99%

Predicting enzymatic reactions with a molecular transformer

Kreutter

¹

,

Schwaller

²

,

Reymond

³

2021

View full text Add to dashboard Cite

show abstract

“…The types of enzymatic reactions predicted correctly by the Enzymatic Transformer are well illustrated by selected cases (Figure 5). These include the correct product prediction including chirality for kinetic resolutions using lipases (reactions 1 40 and 2), 41 two enantioselective reductions of ketones using alcohol dehydrogenases (reaction 3 42 and 4), 43 an enantioselective imine reduction (reaction 5) 44 and reductive amination with a transaminase (reaction 6). 45 Considering that none of the products of these reactions have been seen by the model during training, the ability of the Enzymatic Transformer to predict not only the correct reaction product but also the correct stereochemical outcome of the enantiomeric resolution reactions is remarkable.…”

Section: Examples Of Correct and Incorrect Predictions By The Enzymatic Transformermentioning

confidence: 99%

Predicting Enzymatic Reactions with a Molecular Transformer

Kreutter¹,

Schwaller²,

Reymond

³

2021

Preprint

View full text Add to dashboard Cite

The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. <a>Here we used multi-task transfer learning to train the Molecular Transformer, a sequence-to-sequence machine learning model, with one million reactions from the US Patent Office (USPTO) database combined with 32,181 enzymatic transformations annotated with a text description of the enzyme. The resulting Enzymatic Transformer model predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy. One of the key novelties is that we combined the reaction SMILES language of only 405 atomic tokens with thousands of human language tokens describing the enzymes, such that our Enzymatic Transformer not only learned to interpret SMILES, but also the natural language as used by human experts to describe enzymes and their mutations.</a>

show abstract