Equivariant Masked Position Prediction for Efficient Molecular Representation
This work addresses the problem of limited molecular data for graph neural networks, which is significant for researchers and practitioners in the field of computational chemistry.
The authors tackled the challenge of limited molecular data for graph neural networks by introducing Equivariant Masked Position Prediction, resulting in significant performance enhancement and surpassing state-of-the-art self-supervised approaches. Experimental results showed improved acquisition of physical properties.
Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability to effectively capture the fundamental principles of physics and chemistry, which constrains their generalization capabilities. To address this challenge, we introduce a novel self-supervised approach termed Equivariant Masked Position Prediction (EMPP), grounded in intramolecular potential and force theory. Unlike conventional attribute masking techniques, EMPP formulates a nuanced position prediction task that is more well-defined and enhances the learning of quantum mechanical features. EMPP also bypasses the approximation of the Gaussian mixture distribution commonly used in denoising methods, allowing for more accurate acquisition of physical properties. Experimental results indicate that EMPP significantly enhances performance of advanced molecular architectures, surpassing state-of-the-art self-supervised approaches. Our code is released in https://github.com/ajy112/EMPP