HBDrug3D: A Unified Benchmark Dataset of Heterobifunctional Drugs for Geometric Linker Design

Heterobifunctional drugs, including proteolysis-targeting chimeras (PROTACs), peptide-drug conjugates (PDCs), and antibody-drug conjugates (ADCs), have emerged as important therapeutic modalities. However, the scarcity of publicly available 3D data and standardized evaluation protocols limits the development and comparison of machine learning methods for these molecules. We introduce HBDrug3D, one of the largest publicly available datasets of heterobifunctional drugs, comprising 70,393 molecular conformations across PROTACs, PDCs, and ADCs. Each molecule is annotated at the component level, identifying its linker and modality-specific functional groups, including warheads, payloads, and E3 ligands. Based on HBDrug3D, we establish a standardized benchmark for geometric linker design, a central task in heterobifunctional drug development. The benchmark evaluates generated linkers in terms of validity, novelty, and physicochemical properties relevant to medicinal chemistry. By integrating diverse molecular modalities, component-level annotations, and reproducible evaluation protocols, HBDrug3D provides a common foundation for developing and systematically comparing machine learning approaches to heterobifunctional drug design.