Performance of DeepReac and other models on regression prediction for three benchmark datasets^d.

	Dataset A		Dataset B		Dataset C
	RMSE	R 2	RMSE	R 2	MAE	R 2
Mean	0.273 ± 0.002	—b	0.290 ± 0.004	—b	0.558 ± 0.035	—b
Median	0.276 ± 0.003	—b	0.303 ± 0.006	—b	0.557 ± 0.036	—b
Previous work9,83,84a	0.073 ± 0.004	0.919 ± 0.010	0.180 ± 0.004	0.354 ± 0.034	0.186 ± 0.010	0.822 ± 0.020
MFF + RF33a	0.071 ± 0.004	0.924 ± 0.009	—c	—c	0.132 ± 0.010	0.912 ± 0.012
DeepReac	0.053 ± 0.004	0.960 ± 0.006	0.088 ± 0.006	0.901 ± 0.013	0.096 ± 0.018	0.956 ± 0.012
DeepReac_noG	0.134 ± 0.011	0.674 ± 0.067	0.171 ± 0.008	0.467 ± 0.072	0.178 ± 0.021	0.852 ± 0.026
DeepReac_noC	0.061 ± 0.003	0.949 ± 0.005	0.096 ± 0.001	0.884 ± 0.003	0.185 ± 0.011	0.847 ± 0.025
DeepReac_noGC	0.150 ± 0.004	0.568 ± 0.007	0.200 ± 0.004	0.114 ± 0.068	0.198 ± 0.014	0.837 ± 0.017

^aBecause the validation method is different from the original studies, we retrained these models and tested. Note that the retained models have a slightly lower prediction performance than these methods reported originally.

^bThe R² values for the mean and median models turn out to be all negative, which are not meaningful, so they were omitted.

^cSince MFF didn't indicate how to encode inorganic compounds which are included in Dataset B, we didn't train the MFF + RF model on this dataset.

^dThe values correspond to mean ± standard deviation of the CV results. The best results are given in bold. RMSE, root-mean-square error. MAE, mean absolute error, in kcal mol⁻¹. R², coefficient of determination. MFF, multiple fingerprint feature. RF, random forest. See also Fig. S12–S26.