Toward more reliable 13C and 1H chemical shift prediction: a systematic comparison of neural-network and least-squares regression based approaches.
The efficacy of neural network (NN) and partial least-squares (PLS) methods is compared for the prediction of NMR chemical shifts for both 1H and 13C nuclei using very large databases containing millions of chemical shifts. The chemical structure description scheme used in this work is based on individual atoms rather than functional groups. The performances of each of the methods were optimized in a systematic manner described in this work. Both of the methods, least-squares and neural network analyses, produce results of a very similar quality, but the least-squares algorithm is approximately 2--3 times faster.
Advanced Chemistry Development, Moscow Department, 6 Akademik Bakulev Street, Moscow 117513, Russian Federation, and Advanced Chemistry Development, Inc., 110 Yonge Street, 14th Floor, Toronto, Ontario, Canada M5C 1T4.
Yegor D
YD
Smurnyy
Kirill A
KA
Blinov
Tatiana S
TS
Churanova
Mikhail E
ME
Elyashberg
Antony J
AJ
Williams
D000465:Algorithms; D001688:Biological Products / Q000737:chemistry; D002247:Carbon Isotopes / Q000737:chemistry*; D016208:Databases, Factual; D006859:Hydrogen / Q000737:chemistry*; D016018:Least-Squares Analysis; D016014:Linear Models; D009682:Magnetic Resonance Spectroscopy; D008956:Models, Chemical; D016571:Neural Networks, Computer; D011381:Programming Languages; D013997:Time Factors
D001688:Biological Products; D002247:Carbon Isotopes; D006859:Hydrogen