Beyond Static Pockets: Benchmarking and Modeling Multi-State Transporter Drug Discovery

Transporter drug discovery requires consideration of multiple conformations of the same protein, yet existing computational methods and models still struggle to rapidly and accurately identify ligand-preferred conformations. We introduce SLCState, a manually curated benchmark, and SLCHier, a model for conformational recognition. SLCState contains 762 pocket–ligand records from 622 PDB entries, covering 109 proteins, 14 structural folds, and five conformational states. Seven data splits assess generalization to unseen proteins, new ligand associations, and increasingly complex state sets. We also propose SLCHier, which uses the transporter structural hierarchy as a prior and represents it in hyperbolic space through a two-scale representation that preserves state-level pocket embeddings while organizing them with transporter-level centers. Trained separately on each split, SLCHier achieves higher top-1 accuracy than the compared zero-shot baselines in all seven settings. Controlled ablations further confirm the importance of the structural hierarchy and hyperbolic representations. Together, SLCState and SLCHier provide an evaluation framework and modeling approach for multi-state transporter drug discovery.