Table 1: Evaluation of models trained on human PPIs.

We show performance of D-SCRIPT, PIPR (currently the best-performing sequence-based deep learning PPI prediction method), and two hybrid approaches: D-HYBRID refers to D-SCRIPT predictions augmented with PIPR predictions, and P-HYBRID refers to PIPR predictions augmented with D-SCRIPT predictions (STAR Methods). H. sapiens results are average performance over 5-fold cross validation, using 38,345 positive (and 10 times as many negative) PPIs for training for each fold. All other species were evaluated using a model trained on human data, and evaluated on 5,000 positive and 50,000 negative PPIs (2,000/20,000 for E. coli due to limited data). D-SCRIPT outperforms PIPR cross-species, though PIPR performs better on in-sample species (i.e. human cross-validation). The best performing method for each species and metric is bolded. AUPR: area under precision-recall curve; AUROC: area under receiver operating characteristic curve. See also Table S1.

Species	Model	AUPR	Precision	Recall	AUROC
M. musculus	PIPR	0.526	0.734	0.331	0.839
	D-SCRIPT	0.580	0.818	0.346	0.833
	D-HYBRID	0.609	0.820	0.355	0.838
D. melanogaster	PIPR	0.278	0.521	0.121	0.728
	D-SCRIPT	0.552	0.798	0.359	0.824
	D-HYBRID	0.562	0.798	0.361	0.824
C. elegans	PIPR	0.346	0.673	0.142	0.757
	D-SCRIPT	0.548	0.840	0.306	0.813
	D-HYBRID	0.559	0.841	0.308	0.814
S. cerevisiae	PIPR	0.230	0.398	0.085	0.718
	D-SCRIPT	0.405	0.706	0.223	0.789
	D-HYBRID	0.417	0.708	0.225	0.789
E. coli	PIPR	0.308	0.629	0.131	0.675
	D-SCRIPT	0.571	0.791	0.520	0.863
	D-HYBRID	0.588	0.793	0.394	0.863
H. sapiens (5-fold cross validation)	PIPR	0.835	0.838	0.701	0.960
	D-SCRIPT	0.516	0.728	0.278	0.833
	P-HYBRID	0.844	0.949	0.400	0.962