PIPER: Multi-Conformer Reasoning with Physics-Informed Regularization for RNA–Ligand Interaction Prediction

RNA–ligand interaction prediction is an important task in RNA-targeted drug discovery, yet it remains challenging because RNAs can adopt multiple conformational states while high-resolution tertiary-structure supervision is scarce. Most existing deep learning approaches adopt a single-state formulation, compressing each RNA–ligand pair into a static representation before prediction. Collapsing multiple latent conformational states into a single pair-level representation can blur state-specific interaction signals, making it difficult to compare alternative conformational hypotheses or impose state-specific geometric and physical constraints. To address these limitations, we introduce PIPER, a physics-informed framework for explicit reasoning over a compact set of surrogate conformers without requiring atomistic structures. Given RNA sequence, secondary structure, and ligand graphs, PIPER represents each RNA–ligand pair as a compact set of surrogate conformers rather than a single static state. It performs conformer-wise competition by integrating prior-guided conformer ranking with sparse RNA-residue–ligand-atom interactions, retains the two highest-utility surrogate conformers through a sparse Top-2 posterior, and aggregates their conformer-specific scores. A source-aware screened residual regularizes the local RNA–ligand interaction regions for all generated surrogate conformers. Extensive evaluations show that PIPER outperforms strong baselines across standard benchmarks and achieves particularly strong generalization under RNA cold-start, scaffold-hopping, and dual-cold settings.