DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction

TL;DR

This paper critically evaluates DeepFRI, a GCN-based protein function prediction model, revealing interpretability challenges and the importance of explainability methods for trustworthy AI in drug discovery.

Contribution

It applies advanced explainability techniques to DeepFRI, highlighting the tradeoffs between accuracy and interpretability in protein function prediction models.

Findings

GradCAM provides broad relevance maps

PGExplainer accurately identifies active sites

Model predictions often focus on conserved motifs

Abstract

Machine learning technologies for protein function prediction are black box models. Despite their potential to identify key drug targets with high accuracy and accelerate therapy development, the adoption of these methods depends on verifying their findings. This study evaluates DeepFRI, a leading Graph Convolutional Network (GCN) based tool, using advanced explainability techniques (GradCAM, Excitation Backpropagation, and PGExplainer) and adversarial robustness tests. Our findings reveal that the model's predictions often prioritize conserved motifs over truly deterministic residues, complicating the identification of functional sites. Quantitative analyses show that explainability methods differ significantly in granularity, with GradCAM providing broad relevance and PGExplainer pinpointing specific active sites. These results highlight tradeoffs between accuracy and interpretability, suggesting areas for improvement in DeepFRI's architecture to enhance its trustworthiness in drug discovery and regulatory settings.