Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
Yale University | Microsoft
The 30-Second View
IN SHORT: This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prioritizes structural motifs over functional residues, complicating reliable identification of drug targets.
Innovation (TL;DR)
- Methodology Comprehensive benchmarking of three post-hoc explainability methods (GradCAM, Excitation Backpropagation, PGExplainer) on DeepFRI with quantitative sparsity analysis.
- Methodology Development of a modified DeepFool adversarial testing framework for protein sequences, measuring mutation thresholds required for misclassification.
- Biology Revealed that DeepFRI prioritizes amino acids controlling protein structure over function in >50% of tested proteins, highlighting a fundamental accuracy-interpretability trade-off.
Key conclusions
- DeepFRI required 206 mutations (62.4% of 330 residues) in the lac repressor for misclassification, demonstrating extreme robustness but potentially missing subtle functional alterations.
- Explainability methods showed significant granularity differences: PGExplainer was 3× sparser than GradCAM and 17× sparser than Excitation Backpropagation across 124 binding proteins.
- All three methods converged on biochemically critical P-loop residues (0-20) in ARF6 GTPase, validating DeepFRI's focus on conserved functional motifs in straightforward domains.
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 trade-offs between accuracy and interpretability, suggesting areas for improvement in DeepFRI’s architecture to enhance its trustworthiness in drug discovery and regulatory settings.