Interpretable PROTAC Degradation Prediction With Structure-Informed Deep Ternary Attention Framework.

Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional ligands bridging Proteins-Of-Interest (POIs) and E3 ligases for ubiquitin-proteasome degradation, promising to target the 'undruggable'. While PROTAC research primarily relies on costly and time-consuming wet-lab experiments, deep learning offers potential to accelerate development and reduce expenses. However, many deep learning methods for PROTAC degradation prediction overlook hierarchical molecular representation and protein structural data, hindering data modeling. Moreover, their black-box nature hampers interpretability, failing to provide intuitive insights into substructure interactions within the PROTAC system. This study introduces PROTAC-STAN, a structure-informed deep ternary attention network (STAN) framework for interpretable PROTAC degradation prediction. PROTAC-STAN represents PROTAC molecules across atom, molecule, and property hierarchies and incorporates structure information for POIs and E3 ligases via protein language model. Furthermore, it simulates interactions among three entities at substructure levels via a novel ternary attention network tailored for the PROTAC system, providing unprecedented insights into the degradation mechanism. PROTAC-STAN yields over 10% improvement across multiple metrics compared to the best baselines, while providing significant interpretability through atomic and residue level visualization of molecule and complex. Exploratory evaluations and case studies demonstrate strong real-world applicability. The excellence of PROTAC-STAN is anticipated to establish a foundational tool for future PROTAC research.