Table 1.

MAE and MEDAE results for the top 4 performing regressors (mean ± standard error)

		Blender	DNN			DKL			XGB
Features	Metric	desc+fgp	desc	fgp	desc+fgp	desc	fgp	desc+fgp	desc	fgp	desc+fgp
All	MAE (s)	$39.98\pm 1.49$	$42.35\pm 1.13$	$39.23\pm 1.2$	$39.54\pm 0.97$	$49.27\pm 2.09$	$45.60\pm 2.37$	$44.49\pm 1.11$	$50.24\pm 0.99$	$48.13\pm 0.63$	$48.72\pm 0.90$
	MEDAE (s)	$18.63\pm 1.24$	$20.02\pm 0.73$	$17.22\pm 0.89$	$18.00\pm 0.44$	$26.73\pm 2.00$	$23.25\pm 1.87$	$22.51\pm 0.73$	$27.18\pm 0.71$	$25.64\pm 0.56$	$25.32\pm 0.98$
Non-retained	MAE (s)	$239.02\pm 8.68$	$240.30\pm 8.61$	$235.46\pm 15.85$	$228.11\pm 5.41$	$220.07\pm 38.77$	$228.94\pm 6.10$	$216.61\pm 21.06$	$243.97\pm 7.14$	$242.19\pm 6.57$	$244.17\pm 7.89$
	MEDAE (s)	$87.18\pm 137.45$	$106.45\pm 180.01$	$25.10\pm 6.89$	$17.58\pm 3.80$	$128.53\pm 167.60$	$15.18\pm 5.86$	$33.40\pm 44.41$	$129.74\pm 117.80$	$134.06\pm 108.68$	$126.17\pm 97.29$
Retained	MAE (s)	$34.73\pm 1.14$	$37.11\pm 0.67$	$34.06\pm 0.86$	$34.56\pm 0.67$	$44.80\pm 2.57$	$40.77\pm 2.39$	$39.85\pm 1.50$	$45.14\pm 0.93$	$43.01\pm 0.65$	$43.57\pm 0.94$
	MEDAE (s)	$18.42\pm 1.37$	$19.99\pm 0.72$	$17.21\pm 0.87$	$18.01\pm 0.43$	$26.79\pm 1.99$	$23.29\pm 1.86$	$22.58\pm 0.76$	$26.90\pm 0.58$	$25.35\pm 0.56$	$25.04\pm 0.79$

In this table, desc, fgp and desc+fgp refer to the input features used by each regressor. desc means descriptors, fgp means fingerprints and desc+fgp means that both descriptors and fingerprints have been used. Note that some standard errors are larger than the mean MAE/MEDAE due to the presence of outliers. See Figs. 5 and S3 (the latter in Additional file 1) for better error estimates