The Neural Network as a Tool for Multispectral Interpretation

Abstract

A neural network which utilized data from the infrared spectra, carbon-13 NMR spectra, and molecular formulas of organic compounds was developed. The network, which had one layer of hidden units, was trained by backpropagation; network parameters were determined by a simplex optimization procedure. A database of 1560 compounds was used for training and testing. The trained network was able to identify with high accuracy the presence of a broad range of substructural features present in the compounds. The number of features identified and the accuracy were significantly greater as compared with networks using data from a single form of spectroscopy. The results have significance for the SESAMI computer-enhanced structure elucidation system.

Maximum Common Substructures of Organic Compounds Exhibiting Similar Infrared Spectra

“…Besides NMR spectroscopic techniques and mass spectrometry the use of infrared (IR) spectral data plays an important role in structure elucidation. 1,2 Computer-based approaches for the interpretation of IR spectra can be classified into three categories: (1) knowledge-based systems in which chemical expertise is encoded to assist in spectra interpretation [3][4][5] ; (2) pattern recognition methods based on multivariate data analysis, statistics, and neural networks [6][7][8][9] ; and (3) the most widely used technique, namely search in spectral libraries. Each of these approaches has its own advantages and limitations, but especially library search methods have demonstrated their usefulness in scientific and laboratory practice.…”

Elucidação estrutural de substâncias orgânicas com auxílio de computador: evoluções recentes

“…As RN consistem de um conjunto de neurônios e um conjunto de sinapses artificiais, onde um neurônio artificial recebe estímulos e envia sinais para o neurônio seguinte, assim como os neurônios biológi-cos (Figura 3). Detalhes sobre o funcionamento das RN e suas aplicações em química, bem como em elucidação estrutural de substâncias (RMN, IV e EM) foram anteriormente publicados 44,45 .…”

A General13C NMR Spectrum Predictor Using Data Mining Techniques

“…In 1995, Munk [9] uses IR and 13C NMR spectra from a database of 1,560 compounds. In his approach, the spectra and molecular formula are predictive variables, and the predicted values (outputs) are 85 "flags" indicating the presence or absence of a structural features in the molecule, from very simple ones (one sp2 C atom) to complex ones (terpene, steroid).…”