PMID- 17956083
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20080215
LR  - 20071126
IS  - 1549-9596 (Print)
IS  - 1549-9596 (Linking)
VI  - 47
IP  - 6
DP  - 2007 Nov-Dec
TI  - Prediction of 1H NMR coupling constants with associative neural networks trained 
      for chemical shifts.
PG  - 2089-97
AB  - Fast accurate predictions of 1H NMR spectra of organic compounds play an 
      important role in structure validation, automatic structure elucidation, or 
      calibration of chemometric methods. The SPINUS program is a feed-forward neural 
      network (FFNN) system developed over the last 8 years for the prediction of 1H 
      NMR properties from the molecular structure. It was trained using a series of 
      empirical proton descriptors. Ensembles of FFNNs were incorporated into 
      Associative Neural Networks (ASNN), which correct a prediction on the basis of 
      the observed errors for the k nearest neighbors in an additional memory. Here we 
      show a procedure to estimate coupling constants with the ASNNs trained for 
      chemical shifts-a second memory is linked consisting of coupled protons and their 
      experimental coupling constants. An ASNN finds the pairs of coupled protons most 
      similar to a query, and these are used to estimate coupling constants. Using a 
      diverse general data set of 618 coupling constants, mean absolute errors of 
      0.6-0.8 Hz could be achieved in different experiments. A Web interface for 1H NMR 
      full-spectrum prediction is available at http://www.dq.fct.unl.pt/spinus.
FAU - Binev, Yuri
AU  - Binev Y
AD  - REQUIMTE and CQFB, Departamento de Quimica, Faculdade de Ciencias e Tecnologia, 
      Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
FAU - Marques, Maria M B
AU  - Marques MM
FAU - Aires-de-Sousa, Joao
AU  - Aires-de-Sousa J
LA  - eng
PT  - Journal Article
DEP - 20071023
PL  - United States
TA  - J Chem Inf Model
JT  - Journal of chemical information and modeling
JID - 101230060
EDAT- 2007/10/25 09:00
MHDA- 2007/10/25 09:01
CRDT- 2007/10/25 09:00
PHST- 2007/10/25 09:00 [pubmed]
PHST- 2007/10/25 09:01 [medline]
PHST- 2007/10/25 09:00 [entrez]
AID - 10.1021/ci700172n [doi]
PST - ppublish
SO  - J Chem Inf Model. 2007 Nov-Dec;47(6):2089-97. doi: 10.1021/ci700172n. Epub 2007 
      Oct 23.