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Enc07 Neutral Network Algorithms 070420


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In silico prediction of small molecules properties is widely used in todays industry and academia. Particularly, NMR spectra are predicted by a variety of software packages. Two main approaches are used:
Database-based. Compounds are compared against a database, the result is calculated using data for close structural relatives found in the dataset.
Regression-based. Experimental database is used to calculate parameters of non-linear regression. Chemical shift is represented as a non-linear function of some variables which describe characteristic features of a molecule of interest.

Two outlined approaches require different strategies for further improvement. Database-based results are improved by acquiring larger database and/or including data for user-specific data into calculation.

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Enc07 Neutral Network Algorithms 070420

  1. 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
  2. 2. New Outline <ul><li>Impetus behind us using NN Algorithms </li></ul><ul><li>Implementation of NN approach </li></ul><ul><li>Reasons why this is better than past approaches </li></ul><ul><li>How are they accessed in the software </li></ul><ul><li>Statistical comparisons </li></ul><ul><li>The future of NN and HOSE code predictors </li></ul>
  3. 3. Why Neural Networks? <ul><li>The Neural Network algorithm offers a very specific advantage </li></ul><ul><ul><li>Speed of calculation is hundreds of times faster </li></ul></ul><ul><ul><li>This enables prediction on-the-fly </li></ul></ul><ul><ul><ul><li>For Structure Elucidator, a key feature </li></ul></ul></ul>
  4. 4. Why Neural Networks? <ul><li>Also a fresh approach for ACD/Labs to shift prediction </li></ul><ul><li>We are always researching new ways to improve our software </li></ul><ul><ul><li>Also see our poster (#150) on our new increments scheme </li></ul></ul>
  5. 5. Realization <ul><li>The Neural Network algorithm was outperforming our version 9 HOSE code! </li></ul><ul><li>Steps were then taken to migrate this algorithm out of Structure Elucidator and into the ACD/CNMR Predictor </li></ul>
  6. 6. Implementation
  7. 7. Neural Network Algorithm
  8. 8. Implementation <ul><li>Training the Neural Net </li></ul><ul><ul><li>Entire database from version 9 used </li></ul></ul><ul><ul><li>Additional database of 187,000 shifts used for accuracy testing </li></ul></ul>
  9. 9. Neural Network Approach <ul><li>How does this neural net implementation compare to others in the industry? </li></ul><ul><li>What is unique about it? </li></ul><ul><li>Does this make it better or worse? </li></ul>
  10. 10. Neural Network Approach <ul><li>Our research brought us to some new conclusions </li></ul><ul><li>Some implementation details differed from previous industry attempts </li></ul>
  11. 11. Neural Network Approach <ul><li>We found that: </li></ul><ul><ul><ul><li>Characteristics of the Neural Net were NOT the most important factor </li></ul></ul></ul><ul><ul><ul><li>Structure encoding scheme was most important </li></ul></ul></ul><ul><ul><ul><li>Size and accuracy of training set is key </li></ul></ul></ul><ul><ul><ul><ul><li>Our huge quality checked database gave us a tremendous advantage </li></ul></ul></ul></ul>
  12. 12. Using the Neural Network Predictions <ul><li>How are they accessed in the software? </li></ul>
  13. 13. Using the Neural Network Predictions
  14. 14. Using the Neural Network Predictions
  15. 15. Limitations of the Neural Network Predictions <ul><li>Predictions are a black box </li></ul><ul><ul><li>No calculation protocol as for HOSE code </li></ul></ul><ul><li>Training of predictions could be possible </li></ul><ul><ul><li>Does not outperform HOSE code training </li></ul></ul>
  16. 16. Statistics <ul><li>How do NN compare to old and new HOSE code? </li></ul><ul><li>When should I use NN? </li></ul><ul><li>What is the new performance? </li></ul>
  17. 17. Prediction Accuracy <ul><li>We calculate our prediction accuracy for HOSE code the same way every year </li></ul><ul><ul><li>A “Leave-one-out” analysis of our entire database (2 million chemical shifts) </li></ul></ul><ul><li>This allows us to compare year on year improvement </li></ul><ul><li>A TRUE analysis of how accurate the predictors are </li></ul>
  18. 18. L-O-O Analysis Version 8.00 Version 10.05
  19. 19. Prediction Accuracy <ul><li>Standard Error of Prediction Formula: </li></ul>
  20. 20. Prediction Accuracy <ul><li>CNMR Predictor Standard Error </li></ul><ul><ul><li>Version 8 - 3.11 ppm </li></ul></ul><ul><ul><li>Version 9 - 2.32 ppm </li></ul></ul><ul><ul><li>Version 10.00 - 2.26 ppm </li></ul></ul><ul><ul><li>Version 10.05 – 1.84 ppm </li></ul></ul><ul><ul><ul><li>A 21% increase in accuracy over version 9! </li></ul></ul></ul><ul><ul><ul><li>A 41% increase in accuracy over version 8! </li></ul></ul></ul>
  21. 21. Prediction Accuracy <ul><li>Comparison of HOSE and Neural Network </li></ul><ul><ul><li>>187,000 chemical shifts used in test </li></ul></ul><ul><ul><li>NN algorithms- 12% accuracy increase over version 9 HOSE Code </li></ul></ul><ul><ul><li>Version 10 HOSE code- 16% accuracy increase over version 9 HOSE code </li></ul></ul><ul><li>HOSE Code is better for now </li></ul>
  22. 22. The Future of Neural Nets <ul><li>What is planned for NMR Predictors? </li></ul><ul><li>How do Neural Networks fit into these plans? </li></ul>
  23. 23. The Future of Neural Nets <ul><li>Version 11 will further integrate the Neural Network Algorithm </li></ul><ul><ul><li>An intelligent hybrid approach </li></ul></ul><ul><ul><li>Much like the use of incremental scheme today </li></ul></ul><ul><li>Stay tuned for more validation results </li></ul><ul><ul><li>1 H NMR validation study </li></ul></ul>
  24. 24. Acknowledgements <ul><li>Kirill Blinov </li></ul><ul><li>Mikhail Kvasha </li></ul><ul><li>Marina Solnetseva and the database team </li></ul><ul><li>Ryan Sasaki </li></ul>