Do we need equivariant models for molecule generation?
This challenges the necessity of complex equivariant models in molecular discovery, potentially enabling more efficient and scalable approaches.
The study investigated whether non-equivariant convolutional neural networks with rotation augmentations can match equivariant models for molecule generation, finding they achieve competitive performance with simpler training and better scaling.
Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that explicit equivariance is essential for generating high-quality 3D molecules. However, these models are complex, difficult to train, and scale poorly. We investigate whether non-equivariant convolutional neural networks (CNNs) trained with rotation augmentations can learn equivariance and match the performance of equivariant models. We derive a loss decomposition that separates prediction error from equivariance error, and evaluate how model size, dataset size, and training duration affect performance across denoising, molecule generation, and property prediction. To our knowledge, this is the first study to analyze learned equivariance in generative tasks.