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 of BridgeDPI architecture on three different datasets including Gao, Human, and C.elegans. This study combines gradient-based attributions -- integrated gradients, saliency, layer-wise relevance propagation, SmoothGrad, and SmoothGrad-IG -- with feature-wise occlusion ablation and strict intersection consensus across
The increasing complexity and adoption of AI in critical fields like drug discovery necessitates greater explainability, which is a growing focus in AI research.
Improved explainability in AI-driven drug discovery will accelerate development, increase trust, and potentially lead to more effective and safer therapeutic interventions.
The ability to understand 'where' DTI prediction models look enhances confidence in their predictions and allows for iterative improvements in model design and drug candidate selection.
- · Pharmaceutical companies
- · AI researchers in drug discovery
- · Patients
- · Biotech startups
- · Drug discovery methods reliant solely on opaque black-box AI
- · Companies unable to integrate explainable AI into R&D
Increased adoption of explainable AI techniques in drug-target interaction prediction will lead to more robust and reliable drug discovery pipelines.
Faster and more efficient identification of novel drug candidates and targets, potentially reducing development costs and time-to-market for new therapies.
A shift towards 'interpretable AI' as a standard in sensitive applications like medicine, setting a precedent for other high-stakes domains.
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Read at arXiv cs.LG