Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review.

Artificial intelligence is now central to computational drug discovery, yet performance in core tasks-drug-target interaction (DTI) prediction, virtual screening (VS), and docking scoring-is still limited by the multiscale geometric nature of molecular recognition and by evaluation pitfalls such as dataset bias and leakage. Topological deep learning (TDL) offers a complementary route to encode global and multiscale structure from ligands, binding pockets, surfaces, and protein-ligand complexes via persistent homology and related constructions. This review provides a practical, task-driven synthesis of TDL methods for DTI/VS/docking scoring, with an emphasis on design choices that determine real-world utility: (i) data modality (ligand, pocket, or complex/pose) under controllable uncertainty, (ii) topological objects and filtration families (distance/alpha versus physicochemical or interaction-field filtrations), and (iii) vectorizations and integration patterns (persistent homology-as-features, hybrid geometric deep learning, and emerging end-to-end approaches). Distinct from prior surveys, we present a decision-oriented taxonomy and a benchmark-driven evaluation playbook that specifies minimum standards for splits (scaffold, temporal, and target-wise/cluster), metrics (including early-recognition metrics for VS), baselines, and ablations to isolate the topological contribution. To support reproducibility, we provide a reporting checklist and curated summary tables (methods matrix and benchmark recommendations) that map tasks to recommended protocols and common failure modes.