PL-JEPA: Joint Predictive and Generative Learning for Structure-Based Drug Discovery

Structure-based drug discovery is typically formulated as two separate tasks. Virtual screening ranks existing molecules for a protein pocket, whereas structure-based molecular generation constructs new molecules conditioned on that pocket. Although both tasks are governed by the molecular constraints imposed by the same binding pocket, they are commonly modeled separately. We introduce PL-JEPA, a protein-ligand joint embedding predictive architecture that represents these shared constraints as a binding embedding in ligand representation space. The binding embedding provides a common interface for molecular recognition and realization. It can be directly matched with ligand representations for virtual screening or used to condition an equivariant diffusion decoder for 3D molecular generation. PL-JEPA progressively learns this shared interface through predictive alignment, representation-conditioned generation, and joint predictive and generative training, making the binding embedding both searchable and realizable. A single checkpoint supports virtual screening, 3D molecular generation, and target fishing. On DUD-E and LIT-PCBA, PL-JEPA improves BEDROC${}_{80.5}$ over DrugCLIP by 36.2% and 84.3%, reaching 68.80 and 11.48, respectively. On CrossDocked2020, it achieves a median Vina Dock score of -8.23 kcal/mol and a diversity of 0.78. On CASF-2016, it improves target fishing Acc@5 from 62.59 to 65.96. Ablation studies show that the three-stage training progressively establishes and integrates the searchable and realizable properties of the binding embedding.