Reversible Deep Learning for 13C NMR in Chemoinformatics: On Structures and Spectra

1. 1Build a conditional invertible neural network using i-RevNet style bijective blocks to model the relationship between molecular structures and 13C NMR spectra
2. 2Train the network to predict a 128-bit binned spectrum code from a graph-based structure encoding
3. 3Use the trained model to generate molecular structures from given 13C NMR spectra
4. 4Evaluate the performance of the model using metrics such as accuracy and mean squared error
5. 5Apply the reversible deep learning model to real-world chemoinformatics problems, such as drug discovery and materials science

Chemoinformatics researchers and practitioners can benefit from this approach to improve the accuracy of molecular structure and spectrum predictions, while computational chemists and machine learning engineers can collaborate to develop and implement such models

💡 Reversible deep learning can be used to predict 13C NMR spectra from molecular structures and vice versa, enabling the development of more accurate and efficient chemoinformatics models

Reversible deep learning for 13C NMR in chemoinformatics! Predict molecular structures and spectra with a single conditional invertible neural network #chemoinformatics #deeplearning