Supervised Pretraining for Molecular Force Fields and Properties Prediction
This addresses data scarcity for researchers in molecular modeling, though it is incremental as it applies supervised pretraining to a specific domain.
The paper tackled the problem of labeled data scarcity in molecular modeling by pretraining neural networks on 86 million molecules with atom charges and 3D geometries to predict molecular energies, resulting in significant performance improvements for seven property prediction and two force field tasks compared to training from scratch.
Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods suffer from scarcity of labeled data for particular tasks, motivating the use of large-scale dataset for other relevant tasks. We propose to pretrain neural networks on a dataset of 86 millions of molecules with atom charges and 3D geometries as inputs and molecular energies as labels. Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks. We also demonstrate that the learned representations from the pretrained model contain adequate information about molecular structures, by showing that linear probing of the representations can predict many molecular information including atom types, interatomic distances, class of molecular scaffolds, and existence of molecular fragments. Our results show that supervised pretraining is a promising research direction in molecular modeling