1 citations · 3 across the 6 of their papers we have counts for
6 papers
MolEdit: Knowledge Editing for Multimodal Molecule Language Models
Zhenyu Lei, Patrick Soga, Yaochen Zhu +3
Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have…
Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He +2
Predicting changes in binding free energy () is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have o…
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Yushun Dong, Patrick Soga, Yinhan He +2
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggreg…
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga +3
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs,…
Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata
Patrick Soga, David Chiang
A current goal in the graph neural network literature is to enable transformers to operate on graph-structured data, given their success on language and vision tasks. Since the tra…
Deep Ensembles for Graphs with Higher-order Dependencies
Steven J. Krieg, William C. Burgis, Patrick M. Soga +1
Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximat…