Additionally, permutation tests were conducted to assess the individual influence of protein and ligand embeddings, as well as labels on model performance. This involved shuffling protein embeddings in the training set and evaluating model performance against unshuffled embeddings in the holdout set. This method was mirrored by shuffling embeddings in the test sets to understand the direct effects of permutations on model predictive ability.