Conformer‐Resolved Optical Rotation Prediction From Three‐Dimensional Quantum and Semiempirical Molecular Fields Using 3D Convolutional Neural Networks

Optical rotation is highly sensitive to molecular conformation, making prediction expensive when conformational ensembles and electronic‐response calculations are required. We evaluate three‐dimensional convolutional neural networks trained on volumetric molecular descriptors for conformer‐level optical rotation. A chemically structured set of substituted phenylethanol and phenylethylamine derivatives yielded 82,790 valid conformer‐level sTD‐DFT reference records. Three descriptors were compared on a common grid: HF/STO‐3G electron density, B3LYP/cc‐pVDZ electron density, and GFN2‐xTB charge fields, each combined with atom‐type channels. Performance was assessed using random conformer splits, structured chemical holdouts, and an independently generated CREST/GFN2‐xTB conformer‐ensemble benchmark for the same 65 compounds (705 conformers). Conformer‐level predictions were Boltzmann‐weighted and compared with sTD‐DFT molecular rotations. The GFN2‐xTB model gave the best deployment performance (MAE 15.3 deg dm −1 (g mL −1 ) −1 , RMSE 22.2, R 2 = 0.768, Pearson r = 0.901, 93.8% sign accuracy). Compound‐specific GFN2‐xTB models did not outperform the full‐dataset model, indicating that shared chemical and conformational information improves transfer. These results show that inexpensive semiempirical charge fields can retain sufficient conformer‐resolved chiroptical information for rapid ensemble optical rotation prediction within this chemical series.