首页|Artificial neural networks to model the enantioresolution of structurally unrelated neutral and basic compounds with cellulose tris(3,5-dimethylphenylcarbamate) chiral stationary phase and aqueous-acetonitrile mobile phases
Artificial neural networks to model the enantioresolution of structurally unrelated neutral and basic compounds with cellulose tris(3,5-dimethylphenylcarbamate) chiral stationary phase and aqueous-acetonitrile mobile phases
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NSTL
Elsevier
Artificial neural networks (ANN; feed-forward mode) are used to quantitatively estimate the enantioreso-lution (Rs) in cellulose tris(3,5-dimethylphenylcarbamate) of chiral molecules from their structural infor-mation. To the best of our knowledge, for the first time, a dataset of structurally unrelated compounds is modelled using ANN, attempting to approach a model of general applicability. After setting a strategy compatible with the data complexity and their relatively limited size (56 molecules), by prefixing ini-tial ANN inner weights and the validation and cross-validation subsets, the ANN optimisation based on a novel quality indicator calculated from 9 ANN outputs allows selecting a proper (predictive) ANN archi-tecture (a single hidden layer of 7 neurons) and performing a forward-stepwise feature selection process (8 variables are selected). Such relatively simple ANN offers reasonable good general performance in pre-dicting Rs (e.g. validation plot statistics: mean squared error = 0.047 and R = 0.98 and 0.92, for all or just the validation molecules, respectively). Finally, a study of the relative importance of the selected vari-ables, combining the estimation from two approaches, suggests that the surface tension (positive overall contribution to Rs) and the -NHR groups (negative overall contribution to Rs) are found to be the main variables explaining the enantioresolution in the current conditions. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
NSTL
Elsevier
