Adding common-plus-uncommon substructure masks and group lasso or sparse group lasso losses to activity-cliff GNNs improves per-target pIC50 prediction and attribution consistency on six tyrosine kinases.
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
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abstract
A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity. To move beyond a simple diffusion, filter weights are conditioned on the specific edge labels in the neighborhood of a vertex. Together with the proper choice of graph coarsening, we explore constructing deep neural networks for graph classification. In particular, we demonstrate the generality of our formulation in point cloud classification, where we set the new state of the art, and on a graph classification dataset, where we outperform other deep learning approaches. The source code is available at https://github.com/mys007/ecc
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2025 1verdicts
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Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization
Adding common-plus-uncommon substructure masks and group lasso or sparse group lasso losses to activity-cliff GNNs improves per-target pIC50 prediction and attribution consistency on six tyrosine kinases.