Multi-head cross attention pooling. After the input ligand and protein structures were encoded as multi-scale embeddings, a multi-head cross attention pooling method was designed to aggregate the current four embeddings (H atom , H lig , H res , H prot ) to protein-ligand complex embedding with hierarchical structure representation. Cross attention strategy has been applied to various biomedical tasks for cross-modality representation aggregation. Compared to self-attention, a more well-recognized attention-based mechanism, cross attention sets embedding from one modality/source as query vector, and the embedding from other modality/source as key and value vector to execute attention matrix calculation and weighted aggregation. The matrix computing and aggregating flow regarding the multi-head cross attention pooling is shown in Fig. . Specifically, given the number of attention head as N attn for n-th head and feature dimension as d h , the ligand atomic embedding was defined as the query vector (Q