Dynamic and Chemical Constraints to Enhance the Molecular Masked Graph Autoencoders 1. This paper introduces Dynamic and Chemical Constraints (DyCC) for Masked Graph Autoencoders (MGAEs), addressing key limitations in molecular representation learning. The authors propose a novel masking strategy called GIBMS that dynamically adjusts the mask ratio and content for each molecule, preserving essential semantic information during graph masking. 2. A significant innovation is the Soft Label Generator (SLG), which transforms reconstruction objectives from hard labels to soft labels. This approach adheres to chemical constraints and allows the model to dynamically adjust the difficulty of the reconstruction task during training, leading to improved performance. 3. The DyCC framework is model-agnostic and can be integrated into various MGAEs. Comprehensive experiments demonstrate substantial performance improvements across multiple models, highlighting the effectiveness and generalizability of the proposed methods. 4. The authors identify and address three key issues in existing MGAEs: fixed mask ratios, inconsistent importance of atoms within molecules, and overly constrained reconstruction targets. DyCC resolves these by introducing dynamic adaptability and chemical priors into the proxy tasks. 5. The GIBMS module leverages Graph Information Bottleneck (GIB) theory to identify core substructures within molecules, ensuring that important atoms are prioritized during masking. This design enhances the model’s ability to capture critical structural features and improve downstream task performance. 6. The SLG module uses learnable prototypes to map hard labels to soft labels, reducing conflicts in self-supervised signals. This approach not only improves reconstruction accuracy but also adapts to the chemical diversity of molecules. 7. Experiments show that DyCC significantly boosts performance on molecular property prediction tasks, achieving state-of-the-art results. The framework also reduces reliance on specific tokenizers, making pretraining more robust and adaptable. 📜Paper: openreview.net/pdf/f6bfb8aa8… #MolecularRepresentationLearning #GraphAutoencoders #ChemicalConstraints #DynamicMasking #SoftLabels #Pretraining #NeurIPS2025

Maybe we can assist with some measurement solutions :-) ? #piv #Dynamicmasking

Dr Ergin presenting at the #LXSYMP2018 on dynamic masking #dynamicmasking #dynamicstudio #fluiddynamics