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Quantitative structure‐enantioselectivity relationships using neural networks. Bioconversion of carbonyl compounds using baker's yeast
Author(s) -
Zakarya Driss,
Farhaoui Lahbib,
FkihTétouani Souâd
Publication year - 1996
Publication title -
journal of physical organic chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 66
eISSN - 1099-1395
pISSN - 0894-3230
DOI - 10.1002/(sici)1099-1395(199610)9:10<672::aid-poc827>3.0.co;2-5
Subject(s) - chemistry , yeast , bioconversion , enantiomer , reduction (mathematics) , correlation coefficient , quantitative structure–activity relationship , partition coefficient , stereochemistry , organic chemistry , fermentation , biochemistry , machine learning , geometry , mathematics , computer science
Quantitative structure‐enantioselectivity relationships were studied for the reduction of a set of 73 carbonyl compounds with baker's yeast. The established model, using a neural network, allowed the prediction of the reduction selectivity (% S enantiomer) with success. The correlation coefficient between the observed and calculated % S was 0·99. The model was also used to predict the enantioselectivity of the reduction of α‐diketones using baker's yeast and different microorganisms.