Prot2Text: Multimodal Protein’s Function Generation with GNNs and Transformers

Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, Michalis Vazirgiannis

Published: AAAI 2024, Spotlight at DGM4H Neurips 2023 and AI4Science Neurips 2023

Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks. In the GAT model, each node assigns an importance score to its neighbors using an attention mechanism. However, similar to other graph neural networks, GATs aggregate messages from nodes that belong to different classes, and therefore produce node representations that are not well separated with respect to the different classes, which might hurt their performance. In this work, to alleviate this problem, we propose a new technique that can be incorporated into any graph attention model to encourage higher attention scores between nodes that share the same class label. We evaluate the proposed method on several node classification datasets demonstrating increased performance over standard baseline models.