DeepSPInN - multimodal Deep learning for molecular Structure Prediction from Infrared and NMR spectra

Molecular spectroscopy studies the interaction of molecules with electromagnetic radiation, and interpreting the resultant spectra is invaluable for deducing the molecular structures. However, it is a strenuous task that requires highly specific domain knowledge. DeepSPInN predicts the molecular structure when given Infrared and 13C Nuclear magnetic resonance spectra without referring to any pre-existing spectral databases or molecular fragment knowledge bases. DeepSPInN does this by formulating the molecular structure prediction problem as a Markov decision process (MDP) and employs Monte-Carlo tree search to explore and choose the actions in the formulated MDP. On the QM9 dataset, DeepSPInN is able to predict the molecular structure for 95.98% of the input spectra in an average time of 160 seconds for molecules with less than 10 heavy atoms. This study is the first of its kind that uses multimodal data for molecular structure prediction, and is a leap forward in automated molecular spectral analysis.