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AI explainability audit probes drug-target interaction models

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the explainability of black-box drug-target interaction (DTI) prediction models, specifically auditing the BridgeDPI architecture. The study employs a combination of gradient-based attribution methods and feature-wise occlusion to understand how these models utilize sequence, fingerprint, and graph features. The findings suggest that explainability can serve as a critical tool for model evaluation, revealing issues like modality dominance, artifactual patterns, and dataset-specific behaviors, thereby generating hypotheses for further validation in drug discovery. AI

IMPACT Enhances understanding of AI model behavior in drug discovery, potentially leading to more reliable and interpretable computational drug design.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for explaining AI models in the drug discovery domain.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ali Vefghi, Zahed Rahmati, Mohammad Akbari ·

    Where Black-box Drug-Target Interaction Prediction Models Look: Cross-Method Explainability

    arXiv:2606.14245v1 Announce Type: new Abstract: Drug-target interaction (DTI) and affinity (DTA) predictors increasingly achieve strong benchmark scores, yet their internal use of sequence, fingerprint, and graph features often remains opaque. We present an interpretability audit…

  2. arXiv cs.LG TIER_1 English(EN) · Mohammad Akbari ·

    Where Black-box Drug-Target Interaction Prediction Models Look: Cross-Method Explainability

    Drug-target interaction (DTI) and affinity (DTA) predictors increasingly achieve strong benchmark scores, yet their internal use of sequence, fingerprint, and graph features often remains opaque. We present an interpretability audit of BridgeDPI architecture on three different da…

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