Understanding Conformation Importance in Data-Driven Property Prediction Models.

The prediction of molecular properties is essential in chemoinformatics and has many applications in drug discovery and materials design. Molecular representations play a key role in the prediction models to achieve high prediction accuracy. Nevertheless, appropriate molecular descriptors, including the utilization of conformational information, have been unclear due to a lack of systematic analysis of property prediction models and control. This study investigates the influence of using multiple conformers in machine learning-based property prediction, comparing two- and three-dimensional descriptors using three independent data sets: a large-scale quantum mechanical property, a medium-scale melting point, and small-scale enantioselective chemical reaction data sets. One unique aspect of this study is creating these carefully controlled data sets for models' performance evaluation in conformational diversity and the target property's dependence on conformation. Our findings show that using all available conformers as simple data augmentation consistently achieves high prediction accuracy among aggregation approaches, followed by mean aggregation. Furthermore, Uni-Mol, an end-to-end prediction model utilizing atomic coordinates and elements, combined with the ground-truth conformation, significantly outperformed traditional 2D and 3D descriptors and predicted conformational-sensitive properties with high accuracy. Although the prediction accuracy of the Uni-Mol model significantly decreased using the wrong conformers, it still outperformed two-dimensional extended connectivity fingerprints, which showed higher prediction accuracy than most of the tested 3D descriptors.