# SpliceBind: Isoform-Aware Prediction of Binding Pocket Druggability

arXiv: https://arxiv.org/abs/2606.04020 · HTML: https://mlsanity.com/paper/2606.04020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Equivariant Graph Neural Networks for 3D Macromolecular Structure](https://mlsanity.com/paper/2106.03843/) (2021)
- [DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins](https://mlsanity.com/paper/2002.05643/) (2020)
- [Improving detection of protein-ligand binding sites with 3D segmentation](https://mlsanity.com/paper/1904.06517/) (2019)

## Abstract (arXiv)

Splice-mediated drug resistance occurs in up to 40% of patients on targeted kinase inhibitors, yet state-of-the-art druggability tools operate on single structures and cannot compare across isoforms. We introduce SpliceBind, a graph neural network framework for isoform-aware druggability prediction. Beyond improving prediction accuracy (AUROC 0.703 vs. P2Rank 0.634, p = 0.026), we address a more fundamental question: when do structural methods succeed, and when must they fail? Systematic analysis of six clinically validated variants spanning five mechanism classes reveals a two-tier resistance taxonomy. Domain deletions (AR-V7, Delta = -18.39) and pocket disruptions produce structurally detectable changes, while allosteric mechanisms (BRAF-p61) remain fundamentally invisible to any pocket-centric approach -- a boundary no algorithmic improvement can cross. Notably, learned embeddings capture affinity-based resistance missed by geometry alone (ALK-L1196M: Delta_SB = -0.228 vs. Delta_P2Rank = -0.95), partially bridging the structural-biochemical gap. On 229 kinase pockets spanning 25 families, SpliceBind achieves AUROC 0.703 (p = 0.026 vs. P2Rank) with robust generalization to held-out families (AUROC 0.761). This taxonomy transforms clinical workflows: upon discovering a splice variant, clinicians can immediately determine whether computational triage suffices or biochemical validation is required -- reducing time from variant discovery to therapeutic decision.