Advancements in NMR Predictions- Neural Network vs HOSE Code
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The document summarizes advancements in using neural network algorithms for NMR predictions compared to traditional HOSE code algorithms. It finds that while HOSE code predictions currently outperform neural networks, neural networks offer significant speed advantages and were shown to improve prediction accuracy over previous versions. Future versions aim to integrate neural networks and HOSE code in a hybrid approach to gain the benefits of both methods.
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Advancements in NMR Predictions- Neural Network vs HOSE Code
- 1. Advancements in NMR Predictions- Neural Network vs. HOSE Code Algorithms Brent Lefebvre NMR Product Manager ACD/Labs’ ENC User’s Meeting April 21, 2007
- 13. Using the Neural Network Predictions
- 14. Using the Neural Network Predictions
- 18. L-O-O Analysis Version 8.00 Version 10.05
Notes de l'éditeur
- Speed is not much of an advantage for someone doing one calculation at a time, but is of tremendous benefit to those who need to do batch calculations for many structures
- Speed is not much of an advantage for someone doing one calculation at a time, but is of tremendous benefit to those who need to do batch calculations for many structures
- The structure of a simple neural net. The input layer is fed with N inputs, then the values are transformed by the hidden layer and the output neuron produces the final output value.
- The hierarchical structure of the input vectors used in the current study. Spheres are numbered with Roman numerals, each consisting of 32 cells filled with counts of the substituents. The third sphere is expanded into three to take into account the double bond geometry. CI stands for “Cross-Increments”. These are additional inputs used for the rules-based calculations.
- Screen shot series of accessing Neural Network Predictions in v10
- Screen shot series of accessing Neural Network Predictions in v10