TY - JOUR
T1 - Machine learning to predict the specific optical rotations of chiral fluorinated molecules
AU - Chen, Mengyao
AU - Wu, Ting
AU - Xiao, Kaixia
AU - Zhao, Tanfeng
AU - Zhou, Yanmei
AU - Zhang, Qingyou
AU - Aires-de-Sousa, Joao
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FQUI%2F50006%2F2019/PT#
POCI-01-0145-FEDER – 007265
National Natural Science Foundation of China (No. 21576071 ; 21776061 ) Foundation of International Science and Technology Cooperation of Henan Province (No. 162102410012 )
Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. 20091001 )
Science & Technology Innovation Team in Universities of Henan Province ( 19IRTSTHN029 )
PY - 2019/12/5
Y1 - 2019/12/5
N2 - A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.
AB - A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.
KW - Chiral fluorinated molecules
KW - Chirality
KW - Machine learning
KW - Molecular descriptors
KW - Specific optical rotation
UR - http://www.scopus.com/inward/record.url?scp=85067948025&partnerID=8YFLogxK
U2 - 10.1016/j.saa.2019.117289
DO - 10.1016/j.saa.2019.117289
M3 - Article
C2 - 31255865
AN - SCOPUS:85067948025
SN - 1386-1425
VL - 223
JO - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
JF - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
M1 - 117289
ER -