Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations.

ABSTRACT: Raman spectroscopy is a powerful technique for probing molecular vibrations, yet the computational prediction of Raman spectra remains challenging due to the high cost of quantum chemical methods and the complexity of structure-spectrum relationships. Here, we introduce Mol2Raman, a deep-learning framework that predicts spontaneous Raman spectra directly from SMILES representations of molecules. The model leverages Graph Isomorphism Networks with edge features (GINE) to encode molecular topology and bond characteristics, enabling accurate prediction of both peak positions and intensities across diverse chemical structures. Trained on a novel dataset of over 31 000 molecules with state-of-the-art Density Functional Theory (DFT)-calculated Raman spectra, Mol2Raman outperforms both fingerprin

SUBMITTER: Sorrentino S 

PROVIDER: S-EPMC12691243 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature